Title: A Human's Guide to Words
Tags: lesswrong wordswrong selfhelp

Front: But one of the primary lessons of this gigantic list is that [...]
Back: But one of the primary lessons of this gigantic list is that saying "There's no way my choice of X can be 'wrong'" is nearly always an error in practice, whatever the theory.  You can always be wrong.  Even when it's theoretically impossible to be wrong, you can still be wrong.  There is never a Get-Out-Of-Jail-Free card for anything you do.  That's life.
37 Ways That Words Can Be Wrong

Front: 1. what is wrong about: Is Socrates a framster?
Back: 1. A word fails to connect to reality in the first place.
37 Ways That Words Can Be Wrong

Front: 1. what is the moral of the Parable of the Dagger?
Back: 1. sometimes words fail to connect to reality in the first place
37 Ways That Words Can Be Wrong

Front: 2. what is wrong about: Socrates is a human, and humans, by definition, are mortal.
Back: 2. Your argument, if it worked, could coerce reality to go a different way by choosing a different word definition.
37 Ways That Words Can Be Wrong

Front: 3. what is wrong about:  Socrates is a human, and humans, by definition, are mortal.
Back: 3. You try to establish any sort of empirical proposition as being true "by definition".
37 Ways That Words Can Be Wrong

Front: 2. followup question: Socrates is a human, and humans, by definition, are mortal.
Back: 2. So if you defined humans to not be mortal, would Socrates live forever?
37 Ways That Words Can Be Wrong

Front: 3. followup question: Socrates is a human, and humans, by definition, are mortal.
Back: 3. So is it a logical truth if we empirically predict that Socrates should keel over if he drinks hemlock?  It seems like there are logically possible, non-self-contradictory worlds where Socrates doesn't keel over - where he's immune to hemlock by a quirk of biochemistry, say.  Logical truths are true in all possible worlds, and so never tell you which possible world you live in - and anything you can establish "by definition" is a logical truth.
37 Ways That Words Can Be Wrong

Front: 4. what's wrong with: "Bob is a human."
Back: 4. You unconsciously slap the conventional label on something, without actually using the verbal definition you just gave.
37 Ways That Words Can Be Wrong

Front: 2-4. what is the moral of the Parable of the Hemlock?
Back: 2-4. Logic never dictates any empirical question; it never settles any real-world query which could, by any stretch of the imagination, go either way.
Parable of the Hemlock

Front: what is a common misconception about words?
Back: that you can define them any way you like
Words As Hidden Inferences

Front: what would make it okay to define a word any way you liked?
Back: if the brain treated words as purely logical constructs, Aristotelian classes, and you never took out any more information than you put in
Words As Hidden Inferences

Front: why isn't it okay to define words any way you like?
Back: your brain will categorize things according to the words you use without your conscious approval
Words As Hidden Inferences

Front: how does misunderstanding the working of your own mind prevent the mind from working?
Back: it doesn't
Words As Hidden Inferences

Front: how does your brain not treat words?
Back: as logical definitions with no empirical consequences
Words As Hidden Inferences

Front: what can the mere act of creating a word cause?
Back: your mind to allocate a category
Words As Hidden Inferences

Front: what happens when your brain creates a category?
Back: it will trigger unconscious inferences of similarity
Words As Hidden Inferences

Front: what can I do by creating two labels?
Back: get your mind to allocate two categories
Words As Hidden Inferences

Front: what can be the consequence of your mind's having allocated two categories?
Back: blocking inferences of similarity
Words As Hidden Inferences

Front: what is an example of two categories causing your mind to block inferences of similarity?
Back: the Robbers Cave Experiment
Words As Hidden Inferences

Front: how does making errors about the inside of your head change what's there?
Back: it doesn't
Words As Hidden Inferences

Front: what can philosophical mistakes severely mess up?
Back: the deliberate thinking processes that we use to try to correct our first impressions
Words As Hidden Inferences

Front: if you believe that you can "define a word any way you like" without realizing that your brain goes on categorizing without your conscious thought, what won't you do?
Back: take the effort to choose your definitions wisely
Words As Hidden Inferences

Front: 5. what's wrong with: "Oh, a blegg."
Back: 5. The act of labeling something with a word, disguises a challengable inductive inference you are making.
37 Ways That Words Can Be Wrong

Front: how did Steven Harnad describe the Symbol Grounding Problem?
Back: as trying to learn Chinese from a Chinese/Chinese dictionary
Extensions and Intensions

Front: what happens when you start asking what words mean and getting definitions in terms of other words?
Back: The Symbol Grounding Problem
Extensions and Intensions

Front: what is the alternative to defining words in terms of other words?
Back: pointing to examples
Extensions and Intensions

Front: what does it mean to give an "intensional definition"?
Back: to define a word or phrase in terms of other words
Extensions and Intensions

Front: what does it mean to give an "extensional definition"?
Back: to point to examples
Extensions and Intensions

Front: how does Hollywood Rationality and popular culture depict "rationalists" relating to words?
Back: it paints them as word-obsessed, floating in endless verbal space disconnected from reality
Extensions and Intensions

Front: how have actual Traditional Rationalists long insisted on using words?
Back: maintaining a tight connection to experience
Extensions and Intensions

Front: what is a treasure map?
Back: an intensionally defined procedure which, when executed, will lead you to an extensional example
Extensions and Intensions

Front: what is the actual intension of my "tiger" concept?
Back: the neural pattern (in my temporal cortex) that inspects an incoming signal from the visual cortex to determine whether or not
it is a tiger
Extensions and Intensions

Front: what is the actual extension of my "tiger" concept?
Back: everything I call a tiger
Extensions and Intensions

Front: how do intensional defintions capture intensions?
Back: they don't capture them entirely
Extensions and Intensions

Front: how do extensional definitions capture extensions?
Back: they don't capture them entirely
Extensions and Intensions

Front: why can't you capture all the details of your cognitive concept of a tiger in words?
Back: it's too large
Extensions and Intensions

Front: how do the strongest definitions work?
Back: they use a crossfire of intensional and extensional communication to nail down a concept
Extensions and Intensions

Front: how do you communicate the actual categories as they exist in your mind or in the world?
Back: you don't--you communicate maps to them
(Yes, with enough creativity you can construct exceptions to this rule, like "Sentences Eliezer Yudkowsky has published containing the term 'huragaloni' as of Feb 4, 2008".  I've just shown you this concept's entire extension.)
Extensions and Intensions

Front: what's another reason you can't "define a word any way you like"?
Back: you can't directly program concepts into someone else's brain
Extensions and Intensions

Front: Even [...], you don't have simultaneous freedom of intension and extension.
Back: Even within the Aristotelian paradigm, where we pretend that the definitions are the actual concepts, you don't have simultaneous freedom of intension and extension.
Extensions and Intensions

Front: Even within the Aristotelian paradigm, where we pretend that the definitions are the actual concepts, you don't have [...].
Back: Even within the Aristotelian paradigm, where we pretend that the definitions are the actual concepts, you don't have simultaneous freedom of intension and extension.
Extensions and Intensions

Front: how does most of the brain's work of applying intensions happen?
Back: sub-deliberately
Extensions and Intensions

Front: when you take into account the way the human mind actually, pragmatically works, what does the notion "I can define a word any way I like" soon become?
Back: "I can believe anything I want about a fixed set of objects" or "I can move any object I want in or out of a fixed membership test"
Extensions and Intensions

Front: why is arguing that XYZ is true "by definition" so popular?
Back: abuse definitions just a little, and they turn into magic wands - in arguments, of course; not in reality
Extensions and Intensions

Front: 6. what is wrong with: "What is red?"  "Red is a color."  "What is a color?"  "It's a property of a thing."  "What's a thing?  What's a property?"
Back: 6. You try to define a word using words, in turn defined with ever-more-abstract words, without being able to point to an example.
37 Ways That Words Can Be Wrong

Front: 6. followup point: "What is red?"  "Red is a color."  "What is a color?"  "It's a property of a thing."  "What's a thing?  What's a property?"
Back: 6. It never occurs to you to point to a stop sign and an apple.
37 Ways That Words Can Be Wrong

Front: 7. what's wrong with: "Mars" is "The God of War".
Back: 7. The extension doesn't match the intension.
37 Ways That Words Can Be Wrong

Front: 7. followup point: "Mars" is "The God of War".
Back: 7. We aren't consciously aware of our identification of a red light in the sky as "Mars", which will probably happen regardless of your attempt to define "Mars" as "The God of War".
37 Ways That Words Can Be Wrong

Front: 8. what's wrong with: When the philosophers of Plato's Academy claimed that the best definition of a human was a "featherless biped".
Back: 8. Your verbal definition doesn't capture more than a tiny fraction of the category's shared characteristics, but you try to reason as if it does.
37 Ways That Words Can Be Wrong

Front: 8. followup point: When the philosophers of Plato's Academy claimed that the best definition of a human was a "featherless biped".
Back: Diogenes the Cynic is said to have exhibited a plucked chicken and declared "Here is Plato's Man."  (The Platonists promptly changed their definition to "a featherless biped with broad nails".)
37 Ways That Words Can Be Wrong

Front: once upon a time, what did the philosophers of Plato's Academy claim was the best definition of human?
Back: featherless biped
Similarity Clusters

Front: what did Diogenes of Sinope, also called Diogenes the Cynic, do in response to the definition of man as a "featherless biped"
Back: exhibited a plucked chicken and declared, "Here is Plato's man."
Similarity Clusters

Front: how did the Platonists respond to the plucked chicken?
Back: they changed their definition to "featherless biped with broad nails"
Similarity Clusters

Front: what has no dictionary or encyclopedia ever done to define a human?
Back: listed all the things that humans have in common
Similarity Clusters

Front: what are some things humans have in common with each other?
Back: We have red blood, five fingers on each of two hands, bony skulls, 23 pairs of chromosomes - but the same might be said of other animal species.  We make complex tools to make complex tools, we use syntactical combinatorial language, we harness critical fission reactions as a source of energy: these things may serve out to single out only humans, but not all humans - many of us have never built a fission reactor.
Similarity Clusters

Front: With the right set of necessary-and-sufficient gene sequences you could single out all humans, and only humans - at least for now - [...].
Back: With the right set of necessary-and-sufficient gene sequences you could single out all humans, and only humans - at least for now - but it would still be far from all that humans have in common.
Similarity Clusters

Front: 4. followup point: "Bob is a human."
Back: 4. You know perfectly well that Bob is "human", even though, on your definition, you can never call Bob "human" without first observing him to be mortal.
37 Ways That Words Can Be Wrong

Front: except in mathematics, what are defintions usually?
Back: treasure maps, not treasure
Extensions and Intensions

Front: But so long as [...], saying "Look for featherless bipeds" may serve to pick out a few dozen of the particular things that are humans, as opposed to houses, vases, sandwiches, cats, colors, or mathematical theorems.
Back: But so long as you don't happen to be near a plucked chicken, saying "Look for featherless bipeds" may serve to pick out a few dozen of the particular things that are humans, as opposed to houses, vases, sandwiches, cats, colors, or mathematical theorems.
Similarity Clusters

Front: But so long as you don't happen to be near a plucked chicken, saying [...] may serve to pick out a few dozen of the particular things that are humans, as opposed to houses, vases, sandwiches, cats, colors, or mathematical theorems.
Back: But so long as you don't happen to be near a plucked chicken, saying "Look for featherless bipeds" may serve to pick out a few dozen of the particular things that are humans, as opposed to houses, vases, sandwiches, cats, colors, or mathematical theorems.
Similarity Clusters

Front: But so long as you don't happen to be near a plucked chicken, saying "Look for featherless bipeds" may serve to [...].
Back: But so long as you don't happen to be near a plucked chicken, saying "Look for featherless bipeds" may serve to pick out a few dozen of the particular things that are humans, as opposed to houses, vases, sandwiches, cats, colors, or mathematical theorems.
Similarity Clusters

Front: once the definition "featherless biped" has been bound to some particular featherless bipeds, what can you do?
Back: you can look over the group, and begin harvesting some of the other characteristics - beyond mere featherfree twolegginess - that the "featherless bipeds" seem to share in common
Similarity Clusters

Front: what do the particular featherless bipeds that you see seem to do?
Back: also use language, build complex tools, speak combinatorial language with syntax, bleed red blood if poked, die when they drink hemlock
Similarity Clusters

Front: why are we not fooled by Diogenes chicken?
Back: This plucked chicken is obviously not similar to the other "featherless bipeds".
Similarity Clusters

Front: if Aristotelian logic were a good model of human psychology, what would the Platonists have said when they looked at the plucked chicken?
Back: "Yes, that's a human; what's your point?"
Similarity Clusters

Front: what does the initial clue have to do?
Back: only to lead the user to a similarty cluster
Similarity Clusters

Front: what is a similarity cluster?
Back: a group of things that have many characteristics in common
Similarity Clusters

Front: after it has pointed to a similarity cluster, what is the purpose of the initial clue?
Back: it has no further purpose
Similarity Clusters

Front: A [...] is best thought of, not as a book of Aristotelian class definitions, but a book of hints for matching verbal labels to similarity clusters, or matching labels to properties that are useful in distinguishing similarity clusters.
Back: A dictionary is best thought of, not as a book of Aristotelian class definitions, but a book of hints for matching verbal labels to similarity clusters, or matching labels to properties that are useful in distinguishing similarity clusters.
Similarity Clusters

Front: A dictionary is best thought of, not as a [...], but a book of hints for matching verbal labels to similarity clusters, or matching labels to properties that are useful in distinguishing similarity clusters.
Back: A dictionary is best thought of, not as a book of Aristotelian class definitions, but a book of hints for matching verbal labels to similarity clusters, or matching labels to properties that are useful in distinguishing similarity clusters.
Similarity Clusters

Front: A dictionary is best thought of, not as a book of Aristotelian class definitions, but a [...], or matching labels to properties that are useful in distinguishing similarity clusters.
Back: A dictionary is best thought of, not as a book of Aristotelian class definitions, but a book of hints for matching verbal labels to similarity clusters, or matching labels to properties that are useful in distinguishing similarity clusters.
Similarity Clusters

Front: A dictionary is best thought of, not as a book of Aristotelian class definitions, but a book of hints for matching verbal labels to similarity clusters, or [...].
Back: A dictionary is best thought of, not as a book of Aristotelian class definitions, but a book of hints for matching verbal labels to similarity clusters, or matching labels to properties that are useful in distinguishing similarity clusters.
Similarity Clusters

Front: Most people would say that a [...] is a more typical bird, and a desk chair is a more typical chair.
Back: Most people would say that a robin is a more typical bird, and a desk chair is a more typical chair.
Typicality and Asymmetrical Similarity

Front: Most people would say that a robin is a [...] bird, and a desk chair is a [...] chair.
Back: Most people would say that a robin is a more typical bird, and a desk chair is a more typical chair.
Typicality and Asymmetrical Similarity

Front: Most people would say that a robin is a more typical [...], and a desk chair is a more typical chair.
Back: Most people would say that a robin is a more typical bird, and a desk chair is a more typical chair.
Typicality and Asymmetrical Similarity

Front: Most people would say that a robin is a more typical bird, and a [...] is a more typical chair.
Back: Most people would say that a robin is a more typical bird, and a desk chair is a more typical chair.
Typicality and Asymmetrical Similarity

Front: Most people would say that a robin is a more typical bird, and a desk chair is a more typical [...].
Back: Most people would say that a robin is a more typical bird, and a desk chair is a more typical chair.
Typicality and Asymmetrical Similarity

Front: If you [...] in response to statements like "A robin is a bird" or "A penguin is a bird", reaction times are faster for more central examples.
Back: If you ask subjects to press a button to indicate "true" or "false" in response to statements like "A robin is a bird" or "A penguin is a bird", reaction times are faster for more central examples.
Typicality and Asymmetrical Similarity

Front: If you ask subjects to press a button to indicate "true" or "false" [...], reaction times are faster for more central examples.
Back: If you ask subjects to press a button to indicate "true" or "false" in response to statements like "A robin is a bird" or "A penguin is a bird", reaction times are faster for more central examples.
Typicality and Asymmetrical Similarity

Front: If you ask subjects to press a button to indicate "true" or "false" in response to statements like "A robin is a bird" or "A penguin is a bird", [...].
Back: If you ask subjects to press a button to indicate "true" or "false" in response to statements like "A robin is a bird" or "A penguin is a bird", reaction times are faster for more central examples.
Typicality and Asymmetrical Similarity

Front: [...] correlate well using different investigative methods - reaction times are one example; you can also ask people to directly rate, on a scale of 1 to 10, how well an example (like a specific robin) fits a category (like "bird").
Back: Typicality measures correlate well using different investigative methods - reaction times are one example; you can also ask people to directly rate, on a scale of 1 to 10, how well an example (like a specific robin) fits a category (like "bird").
Typicality and Asymmetrical Similarity

Front: Typicality measures [...]- reaction times are one example; you can also ask people to directly rate, on a scale of 1 to 10, how well an example (like a specific robin) fits a category (like "bird").
Back: Typicality measures correlate well using different investigative methods - reaction times are one example; you can also ask people to directly rate, on a scale of 1 to 10, how well an example (like a specific robin) fits a category (like "bird").
Typicality and Asymmetrical Similarity

Front: Typicality measures correlate well using different investigative methods - [...] are one example; you can also ask people to directly rate, on a scale of 1 to 10, how well an example (like a specific robin) fits a category (like "bird").
Back: Typicality measures correlate well using different investigative methods - reaction times are one example; you can also ask people to directly rate, on a scale of 1 to 10, how well an example (like a specific robin) fits a category (like "bird").
Typicality and Asymmetrical Similarity

Front: Typicality measures correlate well using different investigative methods - reaction times are one example; you can also ask people to [...].
Back: Typicality measures correlate well using different investigative methods - reaction times are one example; you can also ask people to directly rate, on a scale of 1 to 10, how well an example (like a specific robin) fits a category (like "bird").
Typicality and Asymmetrical Similarity

Front: which do most people find more natural, "98 is approximately 100", or "100 is approximately 98"?
Back: "98 is approximately 100"
Typicality and Asymmetrical Similarity

Front: what do most people rate as more similar, Mexico to the United States or the United States to Mexico?
Back: Mexico to the United States
Typicality and Asymmetrical Similarity

Front: if the examples about 99 to 100 and Mexico and the United States are harmless, what other example is more clearly problematic?
Back: a study by Rips (1975) showed that people were more likely to expect a disease would spread from robins to ducks on an island, than from ducks to robins
Typicality and Asymmetrical Similarity

Front: is it a logical impossibility that a disease would spread from robins to ducks on an island, than from ducks to robins?
Back: no
Typicality and Asymmetrical Similarity

Front: in a pragmatic sense, how should we view the truth of the duck/robin disease spreading discrepancy?
Back: whatever difference separates a duck from a robin and would make a disease less likely to spread from a duck to a robin, must also be a difference between a robin and a duck, and would make a disease less likely to spread from a robin to a duck
Typicality and Asymmetrical Similarity

Front: though there are possible rationalizations about ducks and robins (like "Well, there could be more neighboring species of the robins, which would make the disease more likely to spread initially, etc.,"), what is a simpler interpretation?
Back: that people are using the (demonstrated) similarity heuristic as a proxy for the probability that a disease spreads, and this heuristic is (demonstrably) asymmetrical
Typicality and Asymmetrical Similarity

Front: is the similarity heuristic symmentrical?
Back: no
Typicality and Asymmetrical Similarity

Front: Kansas is unusually [...] the center of the United States, and Alaska is unusually far from the center of the United States; so Kansas is probably closer to most places in the US and Alaska is probably farther.
Back: Kansas is unusually close to the center of the United States, and Alaska is unusually far from the center of the United States; so Kansas is probably closer to most places in the US and Alaska is probably farther.
Typicality and Asymmetrical Similarity

Front: Kansas is unusually close to the center of the United States, and Alaska is unusually [...] the center of the United States; so Kansas is probably closer to most places in the US and Alaska is probably farther.
Back: Kansas is unusually close to the center of the United States, and Alaska is unusually far from the center of the United States; so Kansas is probably closer to most places in the US and Alaska is probably farther.
Typicality and Asymmetrical Similarity

Front: Kansas is unusually close to the center of the United States, and Alaska is unusually far from the center of the United States; so Kansas is probably [...] to most places in the US and Alaska is probably farther.
Back: Kansas is unusually close to the center of the United States, and Alaska is unusually far from the center of the United States; so Kansas is probably closer to most places in the US and Alaska is probably farther.
Typicality and Asymmetrical Similarity

Front: Kansas is unusually close to the center of the United States, and Alaska is unusually far from the center of the United States; so Kansas is probably closer to most places in the US and Alaska is probably [...].
Back: Kansas is unusually close to the center of the United States, and Alaska is unusually far from the center of the United States; so Kansas is probably closer to most places in the US and Alaska is probably farther.
Typicality and Asymmetrical Similarity

Front: Kansas is unusually close to the center of the United States, and Alaska is unusually far from the center of the United States; so Kansas is probably closer to most places in the US and Alaska is probably farther.
What does not follow from this?
Back: that Kansas is closer to Alaska than is Alaska to Kansas
Typicality and Asymmetrical Similarity

Front: But people seem to reason (metaphorically speaking) as if [...] and distance is an inherent property of Alaska; so that Kansas is still close, even to Alaska; and Alaska is still distant, even from Kansas.
Back: But people seem to reason (metaphorically speaking) as if closeness is an inherent property of Kansas and distance is an inherent property of Alaska; so that Kansas is still close, even to Alaska; and Alaska is still distant, even from Kansas.
Typicality and Asymmetrical Similarity

Front: But people seem to reason (metaphorically speaking) as if closeness is an inherent property of Kansas and [...]; so that Kansas is still close, even to Alaska; and Alaska is still distant, even from Kansas.
Back: But people seem to reason (metaphorically speaking) as if closeness is an inherent property of Kansas and distance is an inherent property of Alaska; so that Kansas is still close, even to Alaska; and Alaska is still distant, even from Kansas.
Typicality and Asymmetrical Similarity

Front: But people seem to reason (metaphorically speaking) as if closeness is an inherent property of Kansas and distance is an inherent property of Alaska; so that [...].
Back: But people seem to reason (metaphorically speaking) as if closeness is an inherent property of Kansas and distance is an inherent property of Alaska; so that Kansas is still close, even to Alaska; and Alaska is still distant, even from Kansas.
Typicality and Asymmetrical Similarity

Front: what is Aristotle's notion of categories?
Back: logical classes with membership determined by a collection of properties that are individually strictly necessary, and together strictly sufficient
Typicality and Asymmetrical Similarity

Front: So once again we see that [...] is not a good model of human cognitive psychology.
Back: So once again we see that Aristotle's notion of categories is not a good model of human cognitive psychology.
Typicality and Asymmetrical Similarity

Front: So once again we see that Aristotle's notion of categories is [...] of human cognitive psychology.
Back: So once again we see that Aristotle's notion of categories is not a good model of human cognitive psychology.
Typicality and Asymmetrical Similarity

Front: So once again we see that Aristotle's notion of categories is not a good model of [...].
Back: So once again we see that Aristotle's notion of categories is not a good model of human cognitive psychology.
Typicality and Asymmetrical Similarity

Front: We don't even reason as if [...]:  Statements of set membership can be more or less true.
Back: We don't even reason as if set membership is a true-or-false property:  Statements of set membership can be more or less true.
Typicality and Asymmetrical Similarity

Front: We don't even reason as if set membership is a true-or-false property:  [...].
Back: We don't even reason as if set membership is a true-or-false property:  Statements of set membership can be more or less true.
Typicality and Asymmetrical Similarity

Front: when we say things are more or less members of sets, is that the same as being more or less probable?
Back: no
Typicality and Asymmetrical Similarity

Front: what's the notion of a "configuration space"?
Back: a way of translating object descriptions into object positions
The Cluster Structure of Thingspace

Front: how could we see a robin instead of as robin - brown tail, red breast, standard robin shape, maximum flying speed when unladen, its species-typical DNA and individual alleles?
Back: as a single point in a configuration space whose dimensions described everything we knew, or could know, about the robin
The Cluster Structure of Thingspace

Front: what is the benefit of viewing robins as points in space?
Back: You couldn't see the linear lineup as easily if you were just imagining the robins as cute little wing-flapping creatures.
The Cluster Structure of Thingspace

Front: Just like the coordinate point 0:0:5 contains the same information as the actual HTML color blue, we shouldn't actually [...] when we see robins as points in space.
Back: Just like the coordinate point 0:0:5 contains the same information as the actual HTML color blue, we shouldn't actually lose information when we see robins as points in space.
The Cluster Structure of Thingspace

Front: If we're not sure of the robin's exact mass and volume, then we can think of [...], within which the robin might be.
Back: If we're not sure of the robin's exact mass and volume, then we can think of a little cloud in thingspace, a volume of uncertainty, within which the robin might be.
The Cluster Structure of Thingspace

Front: how do cognitive psychologists describe the non-Aristotelian boundaries of words?
Back: "Radial categories"
The Cluster Structure of Thingspace

Front: what's an example of "radial categories" with mothers?
Back: The central "mother" conceives her child, gives birth to it, and supports it. Is an egg donor who never sees her child a mother?  She is the "genetic mother".  What about a woman who is implanted with a foreign embryo and bears it to term?  She is a "surrogate mother".  And the woman who raises a child that isn't hers genetically?  Why, she's an "adoptive mother".
The Cluster Structure of Thingspace

Front: The Aristotelian syllogism would run, "Humans have ten fingers, Fred has nine fingers, therefore Fred is not a human".  How do we actually think?
Back: "Humans have ten fingers, Fred is a human, therefore Fred is a 'nine-fingered human'."
The Cluster Structure of Thingspace

Front: what are the ways to think about the radialness of categories?
Back: in intensional terms, in extensional terms, or simultaneously in both
The Cluster Structure of Thingspace

Front: how can we think of the radial-ness of "mother" in intensional terms?
Back: It might be like a distributed glow in thingspace, a glow whose intensity matches the degree to which that volume of thingspace matches the category "mother".  The glow is concentrated in the center of genetics and birth and child-raising; the volume of egg donors would also glow, but less brightly.
The Cluster Structure of Thingspace

Front: how can we think of the radial-ness of birds in extensional terms?
Back: Then the center of all birdness would be densely populated by many neighboring tight clusters, robins and sparrows and canaries and pigeons and many other species.  Eagles and falcons and other large predatory birds would occupy a nearby cluster.  Penguins would be in a more distant cluster, and likewise chickens and ostriches.
The result might look, indeed, something like an astronomical cluster: many galaxies orbiting the center, and a few outliers.
The Cluster Structure of Thingspace

Front: how would it look to think about thinking simultaneously about both the intension of the cognitive category "bird", and its extension in real-world birds?
Back: The central clusters of robins and sparrows glowing brightly with highly typical birdness; satellite clusters of ostriches and penguins glowing more dimly with atypical birdness, and Abraham Lincoln a few megaparsecs away and glowing not at all.
The Cluster Structure of Thingspace

Front: as Eliezer sees it, how which came first, the extension of "bird" or the intension?
Back: the structure of the cognitive intension followed from the extensional cluster structure
The Cluster Structure of Thingspace

Front: First came the [...], the empirical distribution of birds over thingspace; then, by observing it, we formed a category whose intensional glow roughly overlays this structure.
Back: First came the structure-in-the-world, the empirical distribution of birds over thingspace; then, by observing it, we formed a category whose intensional glow roughly overlays this structure.
The Cluster Structure of Thingspace

Front: First came the structure-in-the-world, the [...]; then, by observing it, we formed a category whose intensional glow roughly overlays this structure.
Back: First came the structure-in-the-world, the empirical distribution of birds over thingspace; then, by observing it, we formed a category whose intensional glow roughly overlays this structure.
The Cluster Structure of Thingspace

Front: First came the structure-in-the-world, the empirical distribution of birds over thingspace; then, by [...], we formed a category whose intensional glow roughly overlays this structure.
Back: First came the structure-in-the-world, the empirical distribution of birds over thingspace; then, by observing it, we formed a category whose intensional glow roughly overlays this structure.
The Cluster Structure of Thingspace

Front: First came the structure-in-the-world, the empirical distribution of birds over thingspace; then, by observing it, we [...].
Back: First came the structure-in-the-world, the empirical distribution of birds over thingspace; then, by observing it, we formed a category whose intensional glow roughly overlays this structure.
The Cluster Structure of Thingspace

Front: what other view does thinking about clusters of similarity in thingspace give us of why words are not Aristotelian classes?
Back: the empirical clustered structure of the real universe is not so crystalline
The Cluster Structure of Thingspace

Front: A natural cluster, [...], may have no set of necessary and sufficient properties.
Back: A natural cluster, a group of things highly similar to each other, may have no set of necessary and sufficient properties.
The Cluster Structure of Thingspace

Front: A natural cluster, a group of things highly similar to each other, may have [...].
Back: A natural cluster, a group of things highly similar to each other, may have no set of necessary and sufficient properties.
The Cluster Structure of Thingspace

Front: what can definitions NOT be expected to match?
Back: the exact empirical structure of thingspace
The Cluster Structure of Thingspace

Front: why can definitions not be expected to exactly match the empirical structure of thingspace?
Back: the map is smaller and much less complicated than the territory
The Cluster Structure of Thingspace

Front: what is the point of the definition "feathered flying things"?
Back: to lead the listener to the bird cluster
The Cluster Structure of Thingspace

Front: what is NOT the point of the definition "feathered flying things"?
Back: to give a total description of every existing bird down to the molecular level
The Cluster Structure of Thingspace

Front: When you [...] a group of extensional points empirically clustered in thingspace, you may find at least one exception to every simple intensional rule you can invent.
Back: When you draw a boundary around a group of extensional points empirically clustered in thingspace, you may find at least one exception to every simple intensional rule you can invent.
The Cluster Structure of Thingspace

Front: When you draw a boundary around [...], you may find at least one exception to every simple intensional rule you can invent.
Back: When you draw a boundary around a group of extensional points empirically clustered in thingspace, you may find at least one exception to every simple intensional rule you can invent.
The Cluster Structure of Thingspace

Front: When you draw a boundary around a group of extensional points empirically clustered in thingspace, you may find [...].
Back: When you draw a boundary around a group of extensional points empirically clustered in thingspace, you may find at least one exception to every simple intensional rule you can invent.
The Cluster Structure of Thingspace

Front: if a definition works well enough in practice to point out the intended empirical cluster, what can objecting to it justifiably be called?
Back: "nitpicking"
The Cluster Structure of Thingspace

Front: when is arguing about definitions "nitpicking"?
Back: when a definition works well enough in practice to point out the intended empirical cluster
The Cluster Structure of Thingspace

Front: The question "Is this object a blegg?" may stand in for [...].
Back: The question "Is this object a blegg?" may stand in for different queries on different occasions.
Disguised Queries

Front: how do we know that the question "is this object a blegg?" is standing in for some query?
Back: because otherwise you'd have no reason to care
Disguised Queries

Front: People who argue that [...] are really trying to argue (I think) that the reasoning methods used in atheism are on a par with the reasoning methods used in religion, or that atheism is no safer than religion in terms of the probability of causally engendering violence, etc...
Back: People who argue that atheism is a religion "because it states beliefs about God" are really trying to argue (I think) that the reasoning methods used in atheism are on a par with the reasoning methods used in religion, or that atheism is no safer than religion in terms of the probability of causally engendering violence, etc...
Disguised Queries

Front: People who argue that atheism is a religion "because it states beliefs about God" are really trying to argue (I think) that [...], or that atheism is no safer than religion in terms of the probability of causally engendering violence, etc...
Back: People who argue that atheism is a religion "because it states beliefs about God" are really trying to argue (I think) that the reasoning methods used in atheism are on a par with the reasoning methods used in religion, or that atheism is no safer than religion in terms of the probability of causally engendering violence, etc...
Disguised Queries

Front: People who argue that atheism is a religion "because it states beliefs about God" are really trying to argue (I think) that the reasoning methods used in atheism are on a par with the reasoning methods used in religion, or that [...], etc...
Back: People who argue that atheism is a religion "because it states beliefs about God" are really trying to argue (I think) that the reasoning methods used in atheism are on a par with the reasoning methods used in religion, or that atheism is no safer than religion in terms of the probability of causally engendering violence, etc...
Disguised Queries

Front: when people try to say that atheism is a religion, what is really at stake?
Back: an atheist's claim of substantial difference and superiority relative to religion
Disguised Queries

Front: how can a religious person dismiss claims of atheism's superiority?
Back: by denying the difference rather than the superiority
Disguised Queries

Front: what is the irrational part of the "is atheism a religion" argument?
Back: The a priori irrational part is where, in the course of the argument, someone pulls out a dictionary and looks up the definition of "atheism" or "religion".
Disguised Queries

Front: why is it irrational to solve "is atheism a religion" by looking it up in a dictionary?
Back: How could a dictionary possibly decide whether an empirical cluster of atheists is really substantially different from an empirical cluster of theologians?  How can reality vary with the meaning of a word?
Disguised Queries

Front: how do the points in thingspace move when we redraw a boundary?
Back: they don't!
Disguised Queries

Front: what is a disguised query?
Back: when an argument about a definitional boundary is really a dispute over whether to infer a characteristic shared by most things inside an empirical cluster
Disguised Queries

Front: 9. what's wrong with: Ducks and penguins are birds.
Back: 9. You try to treat category membership as all-or-nothing, ignoring the existence of more and less typical subclusters.
37 Ways That Words Can Be Wrong

Front: 10. what is wrong with: saying a nine-fingered human isn't human
Back: 10. A verbal definition works well enough in practice to point out the intended cluster of similar things, but you nitpick exceptions.
37 Ways That Words Can Be Wrong

Front: 10. followup point: saying a nine-fingered human isn't human
Back: 10. Not every human has ten fingers, or wears clothes, or uses language; but if you look for an empirical cluster of things which share these characteristics, you'll get enough information that the occasional nine-fingered human won't fool you.
37 Ways That Words Can Be Wrong

Front: 11. what is wrong with: What is a "man"?  Is Barney the Baby Boy a "man"?
Back: 11. You ask whether something "is" or "is not" a category member but can't name the question you really want answered.
37 Ways That Words Can Be Wrong

Front: 11. followup point: What is a "man"?  Is Barney the Baby Boy a "man"?
Back: 11. The "correct" answer may depend considerably on whether the query you really want answered is "Would hemlock be a good thing to feed Barney?" or "Will Barney make a good husband?"
37 Ways That Words Can Be Wrong

Front: what do popular science books say about neural networks?
Back: that they are distributed, emegent, and parallel just like the human brain!!
Neural Categories

Front: how might someone who was fairly naive about Artificial Neural Networks design a neural network to predict unobserved blegg characteristics from observed blegg characteristics?
Back: making a network with a connection between every characteristic (color, shape, texture, luminance, interior)
Neural Categories

Front: what is Hebb's rule?
Back: when two nodes in a neural network are often active at the same time, we should strengthen the connection between them
Neural Categories

Front: what is one of the first rules ever proposed for training a neural network?
Back: Hebb's rule
Neural Categories

Front: what does a neural network need to work?
Back: a learning rule
Neural Categories

Front: using Hebb's rule, what would happen if you often saw things that were both blue and furred?
Back: the connection would strengthen between color and texture (so that + colors activated + textures and vice versa)
Neural Categories

Front: AT FIRST, how will Network 1 behave when you see something that is furred, egg-shaped (blegg characteristics) and reddish purple (color -2/3 and closer to a rube characteristic)
Back: the activation levels will bounce around a bit
Neural Categories

Front: what stable state will the neural network eventually reach after seeing something that is furred, egg-shaped (blegg characteristics) and reddish purple (color -2/3 and closer to a rube characteristic)?
Back: one with high positive activation from "luminance" and "interior"
Neural Categories

Front: what does it mean that Network 1 settles into a stable state with high positive activation for "luminance" and "interior"?
Back: that the network may be said to "expect" that the object will glow in the dark and that it contains vanadium
Neural Categories

Front: Network 1 can [...] even though there's no explicit node that says whether the object is a blegg or not.
Back: Network 1 can predict blegg characteristics even though there's no explicit node that says whether the object is a blegg or not.
Neural Categories

Front: Network 1 can predict blegg characteristics even though [...].
Back: Network 1 can predict blegg characteristics even though there's no explicit node that says whether the object is a blegg or not.
Neural Categories

Front: with Network 1, where does the judgment about whether something is a blegg come from?
Back: it is implicit in the whole network
Neural Categories

Front: in Network 1, how can we describe blegness?
Back: as an attractor
Neural Categories

Front: in Network 1, how does the bleggness attractor arise?
Back: as the result of emergent behavior from the distributed learning rule
Neural Categories

Front: in real life, how do recurrent networks (like Network 1) do?
Back: they run into all sorts of problems
Neural Categories

Front: what sorts of problems do recurrent networks run into in real life?
Back: they can oscillate, exhibit chaotic behavior, or just take a very long time to settle down
Neural Categories

Front: what is the primary practical disadvantage of the problems of recurrent networks?
Back: it's not real-time enough:
This is a Bad Thing when you see something big and yellow and striped, and you have to wait five minutes for your distributed neural network to settle into the "tiger" attractor.  Asynchronous and parallel it may be, but it's not real-time.
Neural Categories

Front: what's an additional (they take a long time to settle down) real-life problem that recurrent networks have?
Back: the problem of double-counting the evidence when messages bounce back and forth
Neural Categories

Front: what is an example of a recurrent neural network double counting evidence when you suspect that an object glows in the dark?
Back: your suspicion will activate belief that the object contains vanadium, which in turn will activate belief that the object glows in the dark
Neural Categories

Front: what does scaling up the Network 1 design require?
Back: [$]O(N^2)[/$]  connections, where [$]N[/$] is the total number of observables
Neural Categories

Front: what is a more realistic neural network design than Network 1?
Back: Network 2, where a central Cateory node (with +BLEGG and -RUBE) has one connection to each observable characteristic
Neural Categories

Front: what is a clamped node?
Back: an observed node
Neural Categories

Front: what is an unclamped node?
Back: an unobserved node
Neural Categories

Front: how does Network 2 work?
Back: a wave of activation converges on the central node from any clamped nodes, and then surges back out again to any unclamped nodes
Neural Categories

Front: what is the consequence of the way Network 2 operates as far as speed?
Back: we can compute the answer in one step, rather than waiting for the network to settle
Neural Categories

Front: what about biology makes it important that we be able to compute the answer in one step, rather than waiting for the network to settle?
Back: it's an important requirement when the neurons only run at 20Hz
Neural Categories

Front: in biology, how fast do the neurons run?
Back: about 20Hz
Neural Categories

Front: how does the Network 2 neural architecture scale?
Back: as [$]O(N)[/$] rather than [$]O(N^2)[/$]
Neural Categories

Front: what is an advantage of the first network architecture over the second?
Back: there are some things you can notice more easily with it
Neural Categories

Front: why can Network 1 notice some things that Network 2 cannot?
Back: Network 1 has a direct connection between every two nodes
Neural Categories

Front: what is an example of something Network 1 can notice that Network 2 cannot?
Back: if red objects never glow in the dark, but red furred objects usually have the other blegg characteristics like egg-shape and vanadium
Neural Categories

Front: if red objects never glow in the dark, but red furred objects usually have the other blegg characteristics like egg-shape and vanadium, how can Network 1 represent this?
Back: a very strong direct negative connection from color to luminance, but more powerful positive connections from texture to all other nodes except luminance
Neural Categories

Front: with Network 1, if red objects never glow in the dark, but red furred objects usually have the other blegg characteristics like egg-shape and vanadium, is its representation of this a "special" exception to the general rule that bleggs glow?
Back: no--in Network 1 there is no unit that represents bleggness
Neural Categories

Front: what do the [$]N^2[/$] connections that Network 1 has buy us?
Back: something, but not very much
Neural Categories

Front: how do the advantages of Network 1 relate to real-world problems?
Back: Network 1 is not more useful on most real-world problems
Neural Categories

Front: what is it about the real-world that means that Network 1 isn't much more useful than Network 2?
Back: you rarely find an animal stuck halfway between being a cat and a dog
Neural Categories

Front: are there facts that you can't easily represent in Network 1 or Network 2?
Back: yes
Neural Categories

Front: what is in example of a fact that cannot be easily represented in either Network 1 or Network 2?
Back: Let's say sea-blue color and spheroid shape, when found together, always indicate the presence of palladium; but when found individually, without the other, they are each very strong evidence for vanadium.
Neural Categories

Front: Let's say sea-blue color and spheroid shape, when found together, always indicate the presence of palladium; but when found individually, without the other, they are each very strong evidence for vanadium.
This fact is hard to represent with the architecture of Network 1 or Network 2, without [...].
Back: This fact is hard to represent with the architecture of Network 1 or Network 2, without extra nodes.
Neural Categories

Front: Both Network 1 and Network 2 embody [...]. (nothing about blegness)
Back: Both Network 1 and Network 2 embody implicit assumptions about what kind of environmental structure is likely to exist.
Neural Categories

Front: what can we say about the biological realism of Network 1 and Network 2?
Back: neither is biologically realistic
Neural Categories

Front: what is a fair guess about whether the brain is more like: Network 1 or Network 2?
Back: that however the brain really works, it is in some sense closer to Network 2 than Network 1
Neural Categories

Front: why is it a fair guess that Network 2 is closer to how the brain works than Network 1?
Back: Fast, cheap, scalable, works well to distinguish dogs and cats: natural selection goes for that sort of thing like water running down a fitness landscape.
Neural Categories

Front: It seems like an ordinary enough task to classify objects as either bleggs or rubes, tossing them into the appropriate bin.  But would you notice if sea-blue objects never glowed in the dark?
Back: Maybe, if someone presented you with twenty objects that were alike only in being sea-blue, and then switched off the light, and none of the objects glowed.  If you got hit over the head with it, in other words.
Neural Categories

Front: how would noticing that sea-blue objects never glowed in the dark not be?
Back: easy or intuitive, the way that distinguishing cats and dogs is easy and intuitive
Neural Categories

Front: does it seem easy and intuitive to ask how much wearing clothes, as opposed to using language, is associated with morality?
Back: no
Neural Categories

Front: what is the easy and intuitive way to reason about Socrates's mortality?
Back: Just, "things that wear clothes and use language are human" and "humans are mortal".
Neural Categories

Front: Are there biases associated with trying to classify things into categories once and for all?
Back: Of course there are.
Neural Categories

Front: what is an example of a bias associated with trying to classify things into categories once and for all?
Back: Cultish Countercultishness
Neural Categories

Front: 12. what's wrong with: It's much easier for a human to notice whether an object is a "blegg" or "rube"; than for a human to notice that red objects never glow in the dark, but red furred objects have all the other characteristics of bleggs.
Back: 12. You treat intuitively perceived hierarchical categories like the only correct way to parse the world, without realizing that other forms of statistical inference are possible even though your brain doesn't use them.
37 Ways That Words Can Be Wrong

Front: 12. followup point: You treat intuitively perceived hierarchical categories like the only correct way to parse the world, without realizing that other forms of statistical inference are possible even though your brain doesn't use them.
Back: 12. Other statistical algorithms work differently.
37 Ways That Words Can Be Wrong

Front: "It makes a sound, just like any other falling tree!"
"But how can there be a sound that no one hears?"
what is the standard rationalist view of what each person means?
Back: The standard rationalist view would be that the first person is speaking as if "sound" means acoustic vibrations in the air; the second person is speaking as if "sound" means an auditory experience in a brain.
How an Algorithm Feels From Inside

Front: "It makes a sound, just like any other falling tree!"
"But how can there be a sound that no one hears?"
what is the standard rationalist view about how they should resolve this?
Back: If you ask "Are there acoustic vibrations?" or "Are there auditory experiences?", the answer is at once obvious.
How an Algorithm Feels From Inside

Front: "It makes a sound, just like any other falling tree!"
"But how can there be a sound that no one hears?"
what is the standard rationalist view of what this disagreement is really about?
Back: the definition of the word "sound"
How an Algorithm Feels From Inside

Front: does Eliezer believe the standard rationalist view that the
"It makes a sound, just like any other falling tree!"
"But how can there be a sound that no one hears?"
argument is really about the definition of the word "sound" to be correct?
Back: essentially yes
How an Algorithm Feels From Inside

Front: A [...] is that mistakes are often more revealing of cognition than correct answers.
Back: A key idea of the heuristics and biases program is that mistakes are often more revealing of cognition than correct answers.
How an Algorithm Feels From Inside

Front: A key idea of the heuristics and biases program is that [...] are often more revealing of cognition than correct answers.
Back: A key idea of the heuristics and biases program is that mistakes are often more revealing of cognition than correct answers.
How an Algorithm Feels From Inside

Front: A key idea of the heuristics and biases program is that mistakes are often [...] than correct answers.
Back: A key idea of the heuristics and biases program is that mistakes are often more revealing of cognition than correct answers.
How an Algorithm Feels From Inside

Front: A key idea of the heuristics and biases program is that mistakes are often more revealing of cognition than [...].
Back: A key idea of the heuristics and biases program is that mistakes are often more revealing of cognition than correct answers.
How an Algorithm Feels From Inside

Front: Getting into a heated dispute about whether, if a tree falls in a deserted forest, it makes a sound, is [...].
Back: Getting into a heated dispute about whether, if a tree falls in a deserted forest, it makes a sound, is traditionally considered a mistake.
How an Algorithm Feels From Inside

Front: Getting into a heated dispute about whether, if a tree falls in a deserted forest, it makes a sound, is traditionally considered a mistake.
What question can we ask about that mistake?
Back: So what kind of mind design corresponds to that error?
How an Algorithm Feels From Inside

Front: Which is to say that even after you know [...], it feels like there's a leftover, unanswered question:  But is it really a blegg?
Back: Which is to say that even after you know whether an object is blue or red, egg or cube, furred or smooth, bright or dark, and whether it contains vanadium or palladium, it feels like there's a leftover, unanswered question:  But is it really a blegg?
How an Algorithm Feels From Inside

Front: Which is to say that even after you know whether an object is blue or red, egg or cube, furred or smooth, bright or dark, and whether it contains vanadium or palladium, [...]:  But is it really a blegg?
Back: Which is to say that even after you know whether an object is blue or red, egg or cube, furred or smooth, bright or dark, and whether it contains vanadium or palladium, it feels like there's a leftover, unanswered question:  But is it really a blegg?
How an Algorithm Feels From Inside

Front: Which is to say that even after you know whether an object is blue or red, egg or cube, furred or smooth, bright or dark, and whether it contains vanadium or palladium, it feels like there's a leftover, unanswered question:  [...]
Back: Which is to say that even after you know whether an object is blue or red, egg or cube, furred or smooth, bright or dark, and whether it contains vanadium or palladium, it feels like there's a leftover, unanswered question:  But is it really a blegg?
How an Algorithm Feels From Inside

Front: [...], acoustic vibrations and auditory experience go together.  But a tree falling in a deserted forest unbundles this common association.
Back: Usually, in our daily experience, acoustic vibrations and auditory experience go together.  But a tree falling in a deserted forest unbundles this common association.
How an Algorithm Feels From Inside

Front: Usually, in our daily experience, [...].  But a tree falling in a deserted forest unbundles this common association.
Back: Usually, in our daily experience, acoustic vibrations and auditory experience go together.  But a tree falling in a deserted forest unbundles this common association.
How an Algorithm Feels From Inside

Front: Usually, in our daily experience, acoustic vibrations and auditory experience go together.  But [...] unbundles this common association.
Back: Usually, in our daily experience, acoustic vibrations and auditory experience go together.  But a tree falling in a deserted forest unbundles this common association.
How an Algorithm Feels From Inside

Front: Usually, in our daily experience, acoustic vibrations and auditory experience go together.  But a tree falling in a deserted forest [...].
Back: Usually, in our daily experience, acoustic vibrations and auditory experience go together.  But a tree falling in a deserted forest unbundles this common association.
How an Algorithm Feels From Inside

Front: even after knowing that the falling tree creates acoustic vibrations but not auditory experience, how does it feel?
Back: as if there's as a leftover question: Did it make a sound?
How an Algorithm Feels From Inside

Front: when we look at Network 2, as Eliezer laid it out in his blog post, how do we see it?
Back: we're seeing the algorithm from the outside
How an Algorithm Feels From Inside

Front: looking at Network 2, do people think to themselves, "Should the central unit fire, or not?" (answer and analogy)
Back: no, not any more than you think "Should neuron #12,234,320,242 in my visual cortex fire, or not?"
How an Algorithm Feels From Inside

Front: It takes a [...] to visualize your brain from the outside - and then you still don't see your actual brain; you imagine what you think is there, hopefully based on science, but regardless, you don't have any direct access to neural network structures from introspection.
Back: It takes a deliberate effort to visualize your brain from the outside - and then you still don't see your actual brain; you imagine what you think is there, hopefully based on science, but regardless, you don't have any direct access to neural network structures from introspection.
How an Algorithm Feels From Inside

Front: It takes a deliberate effort to [...] - and then you still don't see your actual brain; you imagine what you think is there, hopefully based on science, but regardless, you don't have any direct access to neural network structures from introspection.
Back: It takes a deliberate effort to visualize your brain from the outside - and then you still don't see your actual brain; you imagine what you think is there, hopefully based on science, but regardless, you don't have any direct access to neural network structures from introspection.
How an Algorithm Feels From Inside

Front: It takes a deliberate effort to visualize your brain from the outside - and then you still don't [...]; you imagine what you think is there, hopefully based on science, but regardless, you don't have any direct access to neural network structures from introspection.
Back: It takes a deliberate effort to visualize your brain from the outside - and then you still don't see your actual brain; you imagine what you think is there, hopefully based on science, but regardless, you don't have any direct access to neural network structures from introspection.
How an Algorithm Feels From Inside

Front: It takes a deliberate effort to visualize your brain from the outside - and then you still don't see your actual brain; you [...], but regardless, you don't have any direct access to neural network structures from introspection.
Back: It takes a deliberate effort to visualize your brain from the outside - and then you still don't see your actual brain; you imagine what you think is there, hopefully based on science, but regardless, you don't have any direct access to neural network structures from introspection.
How an Algorithm Feels From Inside

Front: It takes a deliberate effort to visualize your brain from the outside - and then you still don't see your actual brain; you imagine what you think is there, hopefully based on science, but regardless, you don't have any direct access to [...] from introspection.
Back: It takes a deliberate effort to visualize your brain from the outside - and then you still don't see your actual brain; you imagine what you think is there, hopefully based on science, but regardless, you don't have any direct access to neural network structures from introspection.
How an Algorithm Feels From Inside

Front: It takes a deliberate effort to visualize your brain from the outside - and then you still don't see your actual brain; you imagine what you think is there, hopefully based on science, but regardless, you don't have any direct access to neural network structures from [...].
Back: It takes a deliberate effort to visualize your brain from the outside - and then you still don't see your actual brain; you imagine what you think is there, hopefully based on science, but regardless, you don't have any direct access to neural network structures from introspection.
How an Algorithm Feels From Inside

Front: why didn't the the ancient Greeks invent computational neuroscience?
Back: you don't have any direct access to neural network structures from introspection
How an Algorithm Feels From Inside

Front: what does it mean to feel a neural network structure from the inside?
Back: to imagine you yourself are a brain running that algorithm
How an Algorithm Feels From Inside

Front: how does the neural network structure of Network 2 feel from the inside?
Back: even after you know every characteristic of the object, you still find yourself wondering:  "But is it a blegg, or not?"
How an Algorithm Feels From Inside

Front: how do we instinctively see our intuitions?
Back: as the world
How an Algorithm Feels From Inside

Front: how do we instinctively NOT see our intuitions?
Back: as "intuitions"
How an Algorithm Feels From Inside

Front: when you look at a green cup, what is what you are seeing?
Back: a picture reconstructed in your visual cortex
How an Algorithm Feels From Inside

Front: when you look at a green cup, how do we think of what we are seeing?
Back: we just see a green cup
How an Algorithm Feels From Inside

Front: instead of thinking "The picture in my visual cortex of this cup in green", what do we think?
Back: "Why, look, this cup is green."
How an Algorithm Feels From Inside

Front: when people argue over whether the falling tree makes a sound, or whether Pluto is a planet, how DO they see the situation?
Back: It seems like either the tree makes a sound, or not.
How an Algorithm Feels From Inside

Front: when people argue over whether the falling tree makes a sound, or whether Pluto is a planet, how do they NOT see the situation?
Back: as themselves arguing over whether a categorization should be active in their neural networks
How an Algorithm Feels From Inside

Front: We know where Pluto is, and where it's going; we know Pluto's shape, and Pluto's mass - but is it a planet?
what sort of a perspective is it to say that this is a fight over definitions?
Back: a Network 2 sort of perspective
How an Algorithm Feels From Inside

Front: We know where Pluto is, and where it's going; we know Pluto's shape, and Pluto's mass - but is it a planet?
why is classifying this as an argument over definitions a Network 2 perspective?
Back: because you're arguing about how the central unit ought to be wired up
How an Algorithm Feels From Inside

Front: We know where Pluto is, and where it's going; we know Pluto's shape, and Pluto's mass - but is it a planet?
if you were a mind constructed along the lines of Network 1, what would you NOT say?
Back: "It depends on how you define 'planet'."
How an Algorithm Feels From Inside

Front: We know where Pluto is, and where it's going; we know Pluto's shape, and Pluto's mass - but is it a planet?
if you were a mind constructed along the lines of Network 1, what WOULD you say?
Back: "Given that we know Pluto's orbit and shape and mass, there is no question left to ask."
How an Algorithm Feels From Inside

Front: We know where Pluto is, and where it's going; we know Pluto's shape, and Pluto's mass - but is it a planet?
if you were a mind constructed along the lines of Network 1, how would you feel about this argument?
Back: it would feel like there was no question left
How an Algorithm Feels From Inside

Front: before you can question your intuitions, what do you have to realize?
Back: that what your mind's eye is looking at is an intuition
How an Algorithm Feels From Inside

Front: what is an intuition?
Back: some cognitive algorithm, as seen from the inside
How an Algorithm Feels From Inside

Front: what is what your mind's eye is looking at NOT?
Back: a direct perception of the Way Things Really Are
How an Algorithm Feels From Inside

Front: according to Eliezer, what is the way people don't see intuitions that causes them to cling to their intuitions?
Back: because they can't see their intuitions as the way their cognitive algorithms happen to look from the inside
How an Algorithm Feels From Inside

Front: according to Eliezer, what is NOT the reason that people cling to their intuitions?
Back: that they believe their cognitive algorithms are perfectly reliable
How an Algorithm Feels From Inside

Front: what is everything you try to say to people about how the native cognitive algorithm goes astray usually contrasted with?
Back: their direct perception of the Way Things Really Are
How an Algorithm Feels From Inside

Front: after contrasting what you try to say about how the native cognitive algorithm goes astray with their direction perception of the Way Things Really Are, what do people do?
Back: discard it as obviously wrong
How an Algorithm Feels From Inside

Front: what is Network 2 with logistic units exactly equivalent to?
Back: Naive Bayes
How An Algorithm Feels From Inside

Front: what does the Naive Bayes algorithm for classifying bleggs and rubes assume?
Back: within the blegg cluster, or within the rube cluster, all remaining variance in the characteristics is independent
How An Algorithm Feels From Inside

Front: what is another way of saying that "within the blegg cluster, or within the rube cluster, all remaining variance in the characteristics is independent"
Back: once we know whether an object is a blegg or a rube, this screens off any other information that its shape could tell us about its color
How An Algorithm Feels From Inside

Front: is saying that once we know whether an object is a blegg or a rube, this screens off any other information that its shape could tell us about its color the same as assuming that the only casual influence on a blegg's shape is its blegg-ness?
Back: no! in fact, there may not be anything that corresponds to blegg-ness
How An Algorithm Feels From Inside

Front: what's the first reason Naive Bayes does work pretty well in practice for classifying stuff?
Back: a lot of objects in the real world do have causal essences
How An Algorithm Feels From Inside

Front: what's an example of something in the world with a causal essence that gives rise to surface characteristics?
Back: cat DNA (which doesn't mix with dog DNA) is the causal essence that gives rise to all the surface characteristics that distinguish cats from dogs
How An Algorithm Feels From Inside

Front: what is the second reason Naive Bayes works pretty well in practice for classifying stuff? (first is that many things have casual essences)
Back: it often successfully chops up a probability distribution into clusters even when the real causal structure looks nothing like a central influence
How An Algorithm Feels From Inside

Front: how did Eliezer relpy to: Don't the sayings suggest that recognizing this bug in oneself or others doesn't require any neural-level understanding of cognition?
Back: Clearly, bug-recognition at the level described in this blog post does not so require, because I have no idea what the biological circuitry that actually recognizes a tiger looks like, though I know it happens in the temporal lobe.
How An Algorithm Feels From Inside

Front: 13. what's wrong with: The ancient philosophers said "Socrates is a man", not, "My brain perceptually classifies Socrates as a match against the 'human' concept".
Back: 13. You talk about categories as if they are manna fallen from the Platonic Realm, rather than inferences implemented in a real brain.
37 Ways That Words Can Be Wrong

Front: 14. what's wrong with: After you observe that an object is blue, egg-shaped, furred, flexible, opaque, luminescent, and palladium-containing, what's left to ask by arguing, "Is it a blegg?"
Back: 14. You argue about a category membership even after screening off all questions that could possibly depend on a category-based inference.
37 Ways That Words Can Be Wrong

Front: 14. followup point: After you observe that an object is blue, egg-shaped, furred, flexible, opaque, luminescent, and palladium-containing, what's left to ask by arguing, "Is it a blegg?"
Back: 14. But if your brain's categorizing neural network contains a (metaphorical) central unit corresponding to the inference of blegg-ness, it may still feel like there's a leftover question.
37 Ways That Words Can Be Wrong

Front: what are the ways an argument the "If a tree falls..." argument could start?
1. [...]
Back: they have genuinely different inutitions
Disputing Definitions

Front: what are the ways an argument the "If a tree falls..." argument could start?
1. they have genuinely different intuitions
2. [...]
Back: Barry could have a motive for rejecting Albert's conclusion
Disputing Definitions

Front: what are the ways an argument the "If a tree falls..." argument could start?
1. they have genuinely different intuitions
2. Barry could have a motive for rejecting Albert's conclusion
3. [...]
Back: Barry could be a skeptic who, upon hearing Albert's argument reflexively scrutinizes it for flaws (add link); and then, on finding a counterargument, automatically accepted it without applying a second layer of search for a counter-counterargument; thereby arguing himself into the opposite position
Disputing Definitions

Front: did Barry necessarily have a different intuition about the meaning of the word "sound" before he started arguing with Albert?
Back: not necessarily
Disputing Definitions

Front: does Barry have a different intuition about the meaning of the word "sound" now that he has started arguing with Albert?
Back: yes
Disputing Definitions

Front: what sort of arguments do Albert and Barry recruit?
Back: those that feel like support for their respective positions
Disputing Definitions

Front: what makes Albert and Barry's arguments feel like support for their respective positions?
Back: they describe in more detail the thoughts that caused their "sound"-detectors (add link) to fire or stay silent
Disputing Definitions

Front: do Albert and Barry disagree about what happens with the tree in the forest?
Back: no
Disputing Definitions

Front: what happens when insult has been proffered and accepted?
Back: now neither party can back down without losing face
Disputing Definitions

Front: are insults part of the argument, as rationalists account such things?
Back: technically no
Disputing Definitions

Front: whenever you feel tempted to say the words "by definition" in an argument that is not literally pure mathematics, what should you remember? (not just that it's a bad idea--why it's stupid)
Back: that anything which is true "by definition" is true in all possible worlds, and so observing its truth can never constrain which world you live in (add links)
Disputing Definitions

Front: Albert deploys an argument that feels like support for the word "sound" having a particular meaning. This is a different kind of question from whether acoustic vibrations take place in a forest - but [...].
Back: Albert deploys an argument that feels like support for the word "sound" having a particular meaning. This is a different kind of question from whether acoustic vibrations take place in a forest - but the shift usually passes unnoticed.
Disputing Definitions

Front: what does the dictionary serve as an alternative to in the argument about the tree in the forest?
Back: to being curious about things
Disputing Definitions

Front: what sort of things could you be curious about in the tree falling in the forest scenario instead of consulting the dictionary?
Back: I could go into the forest and look at trees, or learn how to derive the wave equation for changes of air pressure, or examine the anatomy of an ear, or study the neuroanatomy of the auditory cortex.
Disputing Definitions

Front: what are dictionary editors?
Back: historians of usage
Disputing Definitions

Front: what are dictionary editors not?
Back: legislators of language
Disputing Definitions

Front: if there's more than one usage, what do dictionary editors do?
Back: write own more than one definition
Disputing Definitions

Front: specific rationality errors aside, what does Eliezer point out in a mournful sort of way about Albert and Barry's tree argument?
Back: that Albert and Barry seem to agree on virtually every question of what is actually going on inside the forest, and yet it doesn't seem to generate any feeling of agreement
Disputing Definitions

Front: [...] is a garden path; people wouldn't go down the path if they saw at the outset where it led.
Back: Arguing about definitions is a garden path; people wouldn't go down the path if they saw at the outset where it led.
Disputing Definitions

Front: Arguing about definitions is a [...]; people wouldn't go down the path if they saw at the outset where it led.
Back: Arguing about definitions is a garden path; people wouldn't go down the path if they saw at the outset where it led.
Disputing Definitions

Front: Arguing about definitions is a garden path; [...].
Back: Arguing about definitions is a garden path; people wouldn't go down the path if they saw at the outset where it led.
Disputing Definitions

Front: what does Eliezer suggest as his first piece of advice for eliminating disputing definitions?
Back: Personally I'd say that if the issue arises, both sides should switch to describing the event in unambiguous lower-level constituents, like acoustic vibrations or auditory experiences.
Disputing Definitions

Front: what does Eliezer suggest as an alternative to his first idea for eliminating disputing definitions?
Back: Or each side could designate a new word, like 'alberzle' and 'bargulum', to use for what they respectively used to call 'sound'; and then both sides could use the new words consistently.
Disputing Definitions

Front: what is Eliezer's reason for his two ideas about how to avoid disputing the definition?
Back: That way neither side has to back down or lose face, but they can still communicate.
Disputing Definitions

Front: what does Eliezer add at the end about how else they should argue the tree question?
Back: And of course you should try to keep track, at all times, of some testable proposition that the argument is actually about.
Disputing Definitions

Front: how often does Eliezer's remedy for disputing definitions work?
Back: This remedy doesn't destroy every dispute over categorizations.  But it destroys a substantial fraction.
Disputing Definitions

Front: Suppose I order a blegg from a mail-order catalog. As it turns out, the object I received is blue and is furry, but it is cube-shaped, does not glow in the dark, and contains neither vanadium nor palladium. I am disappointed and attempt to return the object, claiming that it is not, in fact, a blegg. The seller refuses to give me a refund or exchange the object for another. Annoyed, I decide to take the seller to court.
Would I win the lawsuit?
(This is why arguments over definitions have real-world consequences.)
what does Eliezer say about how this relates to disputing definitions?
Back: Technically, that's not so much an argument over a definition, as an argument over cognitive history
Disputing Definitions

Front: Suppose I order a blegg from a mail-order catalog. As it turns out, the object I received is blue and is furry, but it is cube-shaped, does not glow in the dark, and contains neither vanadium nor palladium. I am disappointed and attempt to return the object, claiming that it is not, in fact, a blegg. The seller refuses to give me a refund or exchange the object for another. Annoyed, I decide to take the seller to court.
Would I win the lawsuit?
(This is why arguments over definitions have real-world consequences.)
why does Eliezer say this is an argument about cognitive history?
Back: The seller's expectation of your expectation of what you would get in the mail; and the application of the law to those expectations.
Disputing Definitions

Front: what will prevent Eliezer's remedy for disputing words from working?
Back: If people have already taken actions, based on their previous communications, then the consequences are already set in motion - you can't go back in time and use the remedy.
Disputing Definitions

Front: why doesn't Eliezer's remedy for disputing definitions doesn't work for: Abortion is murder because it's evil to kill a poor defenseless baby.
Back: Another time you can't just generate new words is when a category boundary like "person" or "human" or "baby" makes a direct appearance in your utility function.
Disputing Definitions

Front: 15. what's wrong with: If, before a dispute started about whether a tree falling in a deserted forest makes a "sound", you asked the two soon-to-be arguers whether they thought a "sound" should be defined as "acoustic vibrations" or "auditory experiences", they'd probably tell you to flip a coin.
Back: 15. You allow an argument to slide into being about definitions, even though it isn't what you originally wanted to argue about.
37 Ways That Words Can Be Wrong

Front: 15. followup point: If, before a dispute started about whether a tree falling in a deserted forest makes a "sound", you asked the two soon-to-be arguers whether they thought a "sound" should be defined as "acoustic vibrations" or "auditory experiences", they'd probably tell you to flip a coin.
Back: 15. Only after the argument starts does the definition of a word become politically charged.
37 Ways That Words Can Be Wrong

Front: When I [...] h s s, "O, l, a b," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Back: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Feel the Meaning

Front: When I hear someone say, "Oh, look, a butterfly," [...] t s p "b" e m e a v o m e d, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Back: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Feel the Meaning

Front: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, [...] b t t t c, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Back: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Feel the Meaning

Front: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, [...] t a n t t a s t t a c, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Back: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Feel the Meaning

Front: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, [...] w p p b, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Back: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Feel the Meaning

Front: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, [...] a w r o w, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Back: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Feel the Meaning

Front: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and [...] r o s (a by no means serial process), and all manner of other complications.
Back: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Feel the Meaning

Front: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax ([...]), and all manner of other complications.
Back: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Feel the Meaning

Front: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and [...] a m o o c.
Back: When I hear someone say, "Oh, look, a butterfly," the spoken phonemes "butterfly" enter my ear and vibrate on my ear drum, being transmitted to the cochlea, tickling auditory nerves that transmit activation spikes to the auditory cortex, where phoneme processing begins, along with recognition of words, and reconstruction of syntax (a by no means serial process), and all manner of other complications.
Feel the Meaning

Front: at the end of the day, when my friend say "Oh, look a butterfly," what happens?
Back: I am primed to look where my friend is pointing and see a visual pattern that I will recognize as a butterfly
Feel the Meaning

Front: at the end of the day, when my friend says "Oh, look a butterfly," what would surprise me?
Back: seeing a wolf instead
Feel the Meaning

Front: are we telepathic?
Back: yes
Feel the Meaning

Front: what is our telephathy called?
Back: language
Feel the Meaning

Front: what does our telepathy (language) accomplish?
Back: partial thought transfer
Feel the Meaning

Front: how complicated is our partial thought transfer ability?
Back: more complicated than it looks
Feel the Meaning

Front: what would be an inconvenient way to think of language?
"N I s p t s f o m t i a l s o p w w i s t i m c p..."
Back: "Now I shall partially transduce some features of my thoughts into a linear sequence of phonemes which will invoke similar thoughts in my conversational partner..."
Feel the Meaning

Front: what does our brain do about the complexity of language?
Back: it hides the complexity - or rather, never represents it in the first place
Feel the Meaning

Front: what happens because our brain hides the complexity of language from us?
Back: it leads people to think some peculiar thoughts about words
Feel the Meaning

Front: how would natural selection treat an organism that dealt with the full complexity of how language?
Back: it would not favor it
Feel the Meaning

Front: what sort of an understanding of language would natural selection favor?
Back: it wouldn't want any extra steps between when your auditory cortex recognizes the syllables "tiger", and when the tiger concept gets activated
Feel the Meaning

Front: how should we think of minimzation of steps between "tiger!" and our thinking of the animal?
Back: as a design constraint on the human cognitive architecture
Feel the Meaning

Front: what is the way that the label and the concept are very nearly identified yet another case of?
Back: E. T. Jayne's "Mind Projection Fallacy"
Feel the Meaning

Front: how does the brain distinguish between a word and its meaning?
Back: it rarely distinguishes at all
Feel the Meaning

Front: if you're not sure what Susan is talking about when she says "Blegg!", what DO you think?
Back: I wonder what "blegg" means.
Feel the Meaning

Front: if you're not sure what Susan is talking about when she says "Blegg!", what do you NOT think?
Back: I wonder what mental category Susan associates to the auditory label "blegg".
Feel the Meaning

Front: what pseudocode represents Barry's thoughts about why the tree in the forest doesn't make a sound?
Back: sound.meaning == auditory experiences
forest.sound == false
Feel the Meaning

Front: what pseudocode does NOT represent Barry's thoughts about the tree in the forest?
Back: myBrain.FindConcept("sound") == concept_AuditoryExperience
concept_AuditoryExperience.match(forest) == false
Feel the Meaning

Front: 16. what's wrong with: When someone shouts, "Yikes!  A tiger!", evolution would not favor an organism that thinks, "Hm... I have just heard the syllables 'Tie' and 'Grr' which my fellow tribemembers associate with their internal analogues of my own tiger concept and which aiiieeee CRUNCH CRUNCH GULP."
Back: 16. You think a word has a meaning, as a property of the word itself; rather than there being a label that your brain associates to a particular concept.
37 Ways That Words Can Be Wrong

Front: 16. followup point: When someone shouts, "Yikes!  A tiger!", evolution would not favor an organism that thinks, "Hm... I have just heard the syllables 'Tie' and 'Grr' which my fellow tribemembers associate with their internal analogues of my own tiger concept and which aiiieeee CRUNCH CRUNCH GULP."
Back: 16. So the brain takes a shortcut, and it seems that the meaning of tigerness is a property of the label itself.  People argue about the correct meaning of a label like "sound".
37 Ways That Words Can Be Wrong

Front: Not all definitional disputes progress as far as [...].
Back: Not all definitional disputes progress as far as recognizing the notion of common usage.
The Argument from Common Usage

Front: more often than consulting a dictionary to determine "common usage", why do people do it?
Back: because they believe that words have meanings and the dictionary faithfully records (even determines) what they are
The Argument from Common Usage

Front: The [...] may be comprehensive, but never authoritative.
Back: The Oxford English Dictionary may be comprehensive, but never authoritative.
The Argument from Common Usage

Front: The Oxford English Dictionary may be [...], but never authoritative.
Back: The Oxford English Dictionary may be comprehensive, but never authoritative.
The Argument from Common Usage

Front: The Oxford English Dictionary may be comprehensive, but never [...].
Back: The Oxford English Dictionary may be comprehensive, but never authoritative.
The Argument from Common Usage

Front: knowing that words do not have intrinsic meanings and that dictionaries are not authoritative, why may people consult them anyway?
Back: believing in a social imperitive to use words in a commonly understood way--mutual coordination
The Argument from Common Usage

Front: why might we believe that it would be a good idea to treat dictionary editors as supreme arbiters?
Back: in order to maintain the quiet cooperation on which all speech depends
The Argument from Common Usage

Front: how is the phrase "authoritative dictionary" used?
Back: it is almost never used correctly
The Argument from Common Usage

Front: what is an example of proper usage of the phrase "authoritative dictionary"?
Back: the Authoritative Dictionary of IEEE Standards
The Argument from Common Usage

Front: what is the IEEE?
Back: a body of voting members who have a professional need for exact agreement on terms and definitions
The Argument from Common Usage

Front: what is the Authoritative Dictionary of IEEE Standards?
Back: actual, negotiated legislation, which exerts whatever authority one regards as residing in the IEEE
The Argument from Common Usage

Front: in everyday life, does language usually arise from a deliberate agreement, as of the IEEE?
Back: no
The Argument from Common Usage

Front: in everyday life, how does shared language actually arise?
Back: It's more a matter of infection, as words are invented and diffuse through the culture.
The Argument from Common Usage

Front: what does the IEEE example show about what argeement on language can be?
Back: a cooperatively established public good
The Argument from Common Usage

Front: what does our shared interest in concept labels indicate about what we should do internally?
Back: it's not strictly necessary that you and I use the same labels internally; it is only convenient
The Argument from Common Usage

Front: how does knowing that we're using different words affect our ability to communicate?
Back: it requires extra thought, but we can do it if we want
The Argument from Common Usage

Front: When humans [...], we're hard to stop!
Back: When humans really want to communicate with each other, we're hard to stop!
The Argument from Common Usage

Front: When humans really want to communicate with each other, [...]!
Back: When humans really want to communicate with each other, we're hard to stop!
The Argument from Common Usage

Front: if we're stuck on a deserted island with no common language, what will we do to communicate?
Back: take up sticks and draw pictures in the sand
The Argument from Common Usage

Front: what does Albert's appeal to the Argument from Common Usage assume?
Back: that agreement on language is a cooperatively established public good
The Argument from Common Usage

Front: why does Albert assume that agreement on language is a cooperatively established public good?
Back: for the sole purpose of rhetorically accusing Barry of breaking the agreement, and endangering the public good
The Argument from Common Usage

Front: Now the falling-tree argument has gone all the way from [...]; and so Barry responds by challenging Albert for the authority to define the word.
Back: Now the falling-tree argument has gone all the way from botany to semantics to politics; and so Barry responds by challenging Albert for the authority to define the word.
The Argument from Common Usage

Front: Now the falling-tree argument has gone all the way from botany to semantics to politics; and so Barry responds by [...].
Back: Now the falling-tree argument has gone all the way from botany to semantics to politics; and so Barry responds by challenging Albert for the authority to define the word.
The Argument from Common Usage

Front: When [...], and both sides accuse the other side of defecting from "common usage", then whatever it is they are about, it is clearly not working out a way to communicate with each other.
Back: When both sides know what the other side wants to say, and both sides accuse the other side of defecting from "common usage", then whatever it is they are about, it is clearly not working out a way to communicate with each other.
The Argument from Common Usage

Front: When both sides know what the other side wants to say, and [...], then whatever it is they are about, it is clearly not working out a way to communicate with each other.
Back: When both sides know what the other side wants to say, and both sides accuse the other side of defecting from "common usage", then whatever it is they are about, it is clearly not working out a way to communicate with each other.
The Argument from Common Usage

Front: When both sides know what the other side wants to say, and both sides accuse the other side of defecting from "common usage", then whatever it is they are about, it is clearly [...].
Back: When both sides know what the other side wants to say, and both sides accuse the other side of defecting from "common usage", then whatever it is they are about, it is clearly not working out a way to communicate with each other.
The Argument from Common Usage

Front: what is the whole benefit that common usage provides in the first place?
Back: making it easier to communicate
The Argument from Common Usage

Front: how can you solve the argument about whether blacks are "people" by generating two words?
Back: you can't--what's at stake is exactly the idea that you shouldn't draw a moral distinction
The Argument from Common Usage

Front: what sort of stakes prevent you from appealing to common usage?
Back: once any empirical proposition is at stake, or any moral proposition
The Argument from Common Usage

Front: If the question is [...], empirical predictions will depend on the answer; which means that definitions can be wrong.
Back: If the question is how to cluster together similar things for purposes of inference, empirical predictions will depend on the answer; which means that definitions can be wrong.
The Argument from Common Usage

Front: If the question is how to cluster together similar things for purposes of inference, [...]; which means that definitions can be wrong.
Back: If the question is how to cluster together similar things for purposes of inference, empirical predictions will depend on the answer; which means that definitions can be wrong.
The Argument from Common Usage

Front: If the question is how to cluster together similar things for purposes of inference, empirical predictions will depend on the answer; which means that [...].
Back: If the question is how to cluster together similar things for purposes of inference, empirical predictions will depend on the answer; which means that definitions can be wrong.
The Argument from Common Usage

Front: how can a conflict of predictions never be settled?
Back: by an opinion poll
The Argument from Common Usage

Front: There is [...] to using words; even when definitions are not literally true or false, they are often wiser or more foolish.
Back: There is an art to using words; even when definitions are not literally true or false, they are often wiser or more foolish.
The Argument from Common Usage

Front: There is an art to using words; even when definitions are not [...], they are often wiser or more foolish.
Back: There is an art to using words; even when definitions are not literally true or false, they are often wiser or more foolish.
The Argument from Common Usage

Front: There is an art to using words; even when definitions are not literally true or false, they are often [...].
Back: There is an art to using words; even when definitions are not literally true or false, they are often wiser or more foolish.
The Argument from Common Usage

Front: how is it limiting to treat dictionaries as supreme arbiters of meaning?
Back: it binds you to the wisdom of the past, forbidding you to do better
The Argument from Common Usage

Front: what should you take care to ensure (if you must depart from the wisdom of the past)?
Back: that people can figure out what you're trying to say
The Argument from Common Usage

Front: 17. what's wrong with: The human ability to associate labels to concepts is a tool for communication.  When people want to communicate, we're hard to stop; if we have no common language, we'll draw pictures in sand.
Back: 17. You argue over the meanings of a word, even after all sides understand perfectly well what the other sides are trying to say.
37 Ways That Words Can Be Wrong

Front: 17. followup point: The human ability to associate labels to concepts is a tool for communication.  When people want to communicate, we're hard to stop; if we have no common language, we'll draw pictures in sand.
Back: 17. When you each understand what is in the other's mind, you are done.
37 Ways That Words Can Be Wrong

Front: 18. what's wrong with: Dictionary editors are historians of usage, not legislators of language.
Back: 18. You pull out a dictionary in the middle of an empirical or moral argument.
37 Ways That Words Can Be Wrong

Front: 18. followup point: Dictionary editors are historians of usage, not legislators of language.
Back: 18. If the common definition contains a problem - if "Mars" is defined as the God of War, or a "dolphin" is defined as a kind of fish, or "Negroes" are defined as a separate category from humans, the dictionary will reflect the standard mistake.
37 Ways That Words Can Be Wrong

Front: 19. what's wrong with: Seriously, what the heck makes you think that dictionary editors are an authority on whether "atheism" is a "religion" or whatever?
Back: 19. You pull out a dictionary in the middle of any argument ever.
37 Ways That Words Can Be Wrong

Front: 19. followup point: Seriously, what the heck makes you think that dictionary editors are an authority on whether "atheism" is a "religion" or whatever?
Back: 19. If you have any substantive issue whatsoever at stake, do you really think dictionary editors have access to ultimate wisdom that settles the argument?
37 Ways That Words Can Be Wrong

Front: 20. what's wrong with: Fast stand up plutonium, with bagels without handle.
Back: 20. You defy common usage without a reason, making it gratuitously hard for others to understand you.
37 Ways That Words Can Be Wrong

Front: when Eliezer talks about "feels" with meanings of words, what does he mean?
Back: "falsely believes that" or "mistakenly intuits that"
Feel the Meaning

Front: And the thing to remember about the [...] is that [A, C, D] is the entire information of "zawa".
Back: And the thing to remember about the Aristotelian idea of categories is that [A, C, D] is the entire information of "zawa".
Empty Labels

Front: And the thing to remember about the Aristotelian idea of categories is that [...] is the entire information of "zawa".
Back: And the thing to remember about the Aristotelian idea of categories is that [A, C, D] is the entire information of "zawa".
Empty Labels

Front: And the thing to remember about the Aristotelian idea of categories is that [A, C, D] is [...].
Back: And the thing to remember about the Aristotelian idea of categories is that [A, C, D] is the entire information of "zawa".
Empty Labels

Front: It's not just that I can [...], but that I can get along just fine without any label at all - the rules for Aristotelian classes work purely on structures like [A, C, D].
Back: It's not just that I can vary the label, but that I can get along just fine without any label at all - the rules for Aristotelian classes work purely on structures like [A, C, D].
Empty Labels

Front: It's not just that I can vary the label, but that I can [...] - the rules for Aristotelian classes work purely on structures like [A, C, D].
Back: It's not just that I can vary the label, but that I can get along just fine without any label at all - the rules for Aristotelian classes work purely on structures like [A, C, D].
Empty Labels

Front: It's not just that I can vary the label, but that I can get along just fine without any label at all - [...].
Back: It's not just that I can vary the label, but that I can get along just fine without any label at all - the rules for Aristotelian classes work purely on structures like [A, C, D].
Empty Labels

Front: To [...], or attach any other label to it, is a human convenience (or inconvenience) which makes not the slightest difference to the Aristotelian rules.
Back: To call one of these structures "zawa", or attach any other label to it, is a human convenience (or inconvenience) which makes not the slightest difference to the Aristotelian rules.
Empty Labels

Front: To call one of these structures "zawa", or [...], is a human convenience (or inconvenience) which makes not the slightest difference to the Aristotelian rules.
Back: To call one of these structures "zawa", or attach any other label to it, is a human convenience (or inconvenience) which makes not the slightest difference to the Aristotelian rules.
Empty Labels

Front: To call one of these structures "zawa", or attach any other label to it, is [...] which makes not the slightest difference to the Aristotelian rules.
Back: To call one of these structures "zawa", or attach any other label to it, is a human convenience (or inconvenience) which makes not the slightest difference to the Aristotelian rules.
Empty Labels

Front: To call one of these structures "zawa", or attach any other label to it, is a human convenience (or inconvenience) which [...].
Back: To call one of these structures "zawa", or attach any other label to it, is a human convenience (or inconvenience) which makes not the slightest difference to the Aristotelian rules.
Empty Labels

Front: convert: "All men are mortal.  Socrates is a man.  Therefore Socrates is mortal."
Back: All [mortal, ~feathers, bipedal] are mortal.
Socrates is a [mortal, ~feathers, bipedal].
Therefore, Socrates is mortal.
Empty Labels

Front: what makes the classic syllogism about Socrates being mortal less impressive?
Back: replacing labels with definitions
Empty Labels

Front: where does the illusion of inference come from?
Back: the labels
Empty Labels

Front: what do labels do that create the illusion of inference?
Back: conceal the premises and pretend novelty in the conclusion
Empty Labels

Front: what can we do by replacing Aristotelian labels with definitions?
Back: reveal the illusion and make visible the tautalogy's empirical unhelpfulness
Empty Labels

Front: where did the idea that "you can define a word any way you like" come from?
Back: the Aristotelian notion of categories
Empty Labels

Front: But it's not so much that [...] in the Aristotelian system, as that the Aristotelian system works fine without any labels at all - it cranks out exactly the same stream of tautologies, they just look a lot less impressive.
Back: But it's not so much that labels are arbitrary in the Aristotelian system, as that the Aristotelian system works fine without any labels at all - it cranks out exactly the same stream of tautologies, they just look a lot less impressive.
Empty Labels

Front: But it's not so much that labels are arbitrary in the Aristotelian system, as that the Aristotelian system [...] - it cranks out exactly the same stream of tautologies, they just look a lot less impressive.
Back: But it's not so much that labels are arbitrary in the Aristotelian system, as that the Aristotelian system works fine without any labels at all - it cranks out exactly the same stream of tautologies, they just look a lot less impressive.
Empty Labels

Front: But it's not so much that labels are arbitrary in the Aristotelian system, as that the Aristotelian system works fine without any labels at all - [...], they just look a lot less impressive.
Back: But it's not so much that labels are arbitrary in the Aristotelian system, as that the Aristotelian system works fine without any labels at all - it cranks out exactly the same stream of tautologies, they just look a lot less impressive.
Empty Labels

Front: But it's not so much that labels are arbitrary in the Aristotelian system, as that the Aristotelian system works fine without any labels at all - it cranks out exactly the same stream of tautologies, [...].
Back: But it's not so much that labels are arbitrary in the Aristotelian system, as that the Aristotelian system works fine without any labels at all - it cranks out exactly the same stream of tautologies, they just look a lot less impressive.
Empty Labels

Front: why are the labels there (in Aristotelian logic)?
Back: to create the illusion of inference
Empty Labels

Front: So if you're going to [...], the proverb should be, not "I can define a word any way I like," nor even, "Defining a word never has any consequences," but rather, "Definitions don't need words."
Back: So if you're going to have an Aristotelian proverb at all, the proverb should be, not "I can define a word any way I like," nor even, "Defining a word never has any consequences," but rather, "Definitions don't need words."
Empty Labels

Front: So if you're going to have an Aristotelian proverb at all, the proverb should be, not "[...]," nor even, "Defining a word never has any consequences," but rather, "Definitions don't need words."
Back: So if you're going to have an Aristotelian proverb at all, the proverb should be, not "I can define a word any way I like," nor even, "Defining a word never has any consequences," but rather, "Definitions don't need words."
Empty Labels

Front: So if you're going to have an Aristotelian proverb at all, the proverb should be, not "I can define a word any way I like," nor even, "[...]," but rather, "Definitions don't need words."
Back: So if you're going to have an Aristotelian proverb at all, the proverb should be, not "I can define a word any way I like," nor even, "Defining a word never has any consequences," but rather, "Definitions don't need words."
Empty Labels

Front: So if you're going to have an Aristotelian proverb at all, the proverb should be, not "I can define a word any way I like," nor even, "Defining a word never has any consequences," but rather, "[...]."
Back: So if you're going to have an Aristotelian proverb at all, the proverb should be, not "I can define a word any way I like," nor even, "Defining a word never has any consequences," but rather, "Definitions don't need words."
Empty Labels

Front: 21. what's wrong with: Is a "human" defined as a "mortal featherless biped"?  Then write:  "All [mortal featherless bipeds] are mortal; Socrates is a [mortal featherless biped]; therefore, Socrates is mortal."
Back: 21. You use complex renamings to create the illusion of inference.
37 Ways That Words Can Be Wrong

Front: 21. followup point: Is a "human" defined as a "mortal featherless biped"?  Then write:  "All [mortal featherless bipeds] are mortal; Socrates is a [mortal featherless biped]; therefore, Socrates is mortal."
Back: 21. Looks less impressive that way, doesn't it?
37 Ways That Words Can Be Wrong

Front: what is the objective of the party game Taboo?
Back: for a player to have their partner guess a word written on a card, without using that word or five additional words listed on the card
Taboo Your Words

Front: dereference pointer for sound: "A tree falling in a deserted forest makes a sound."
Back: "A tree falling in a deserted forest matches [membership test: this event generates acoustic vibrations]."
Taboo Your Words

Front: dereference pointer for sound: "A tree falling in a deserted forest does not make a sound."
Back: "A tree falling in a deserted forest does not match [membership test: this event generates auditory experiences]."
Taboo Your Words

Front: what can we do to resolve the apparent collision in meaning about the tree falling in the forest?
Back: prohibit the use of the word sound
Taboo Your Words

Front: if "acoustic vibrations" came into dispute, what could we do?
Back: just play Taboo again and say "pressure waves in a material medium"
Taboo Your Words

Front: taboo: wave
Back: the wave equation
Taboo Your Words

Front: taboo: auditory experience
Back: That form of sensory processing, within the human brain, which takes as input a linear time series of frequency mixes.
Taboo Your Words

Front: You get a [...], depending on whether you take a label's-eye-view (Albert says "sound" and Barry says "not sound", so they must disagree) or taking the test's-eye-view (Albert's membership test is acoustic vibrations, Barry's is auditory experience).
Back: You get a very different picture of what people agree or disagree about, depending on whether you take a label's-eye-view (Albert says "sound" and Barry says "not sound", so they must disagree) or taking the test's-eye-view (Albert's membership test is acoustic vibrations, Barry's is auditory experience).
Taboo Your Words

Front: You get a very different picture of what people agree or disagree about, depending on whether you take a [...] (Albert says "sound" and Barry says "not sound", so they must disagree) or taking the test's-eye-view (Albert's membership test is acoustic vibrations, Barry's is auditory experience).
Back: You get a very different picture of what people agree or disagree about, depending on whether you take a label's-eye-view (Albert says "sound" and Barry says "not sound", so they must disagree) or taking the test's-eye-view (Albert's membership test is acoustic vibrations, Barry's is auditory experience).
Taboo Your Words

Front: You get a very different picture of what people agree or disagree about, depending on whether you take a label's-eye-view ([...]) or taking the test's-eye-view (Albert's membership test is acoustic vibrations, Barry's is auditory experience).
Back: You get a very different picture of what people agree or disagree about, depending on whether you take a label's-eye-view (Albert says "sound" and Barry says "not sound", so they must disagree) or taking the test's-eye-view (Albert's membership test is acoustic vibrations, Barry's is auditory experience).
Taboo Your Words

Front: You get a very different picture of what people agree or disagree about, depending on whether you take a label's-eye-view (Albert says "sound" and Barry says "not sound", so they must disagree) or taking the [...] (Albert's membership test is acoustic vibrations, Barry's is auditory experience).
Back: You get a very different picture of what people agree or disagree about, depending on whether you take a label's-eye-view (Albert says "sound" and Barry says "not sound", so they must disagree) or taking the test's-eye-view (Albert's membership test is acoustic vibrations, Barry's is auditory experience).
Taboo Your Words

Front: You get a very different picture of what people agree or disagree about, depending on whether you take a label's-eye-view (Albert says "sound" and Barry says "not sound", so they must disagree) or taking the test's-eye-view ([...]).
Back: You get a very different picture of what people agree or disagree about, depending on whether you take a label's-eye-view (Albert says "sound" and Barry says "not sound", so they must disagree) or taking the test's-eye-view (Albert's membership test is acoustic vibrations, Barry's is auditory experience).
Taboo Your Words

Front: what can a featureless standard word hide (in argument)?
Back: quite a conflict of expectations
Taboo Your Words

Front: according to Eliezer, how can we dispell the illusion of unity across religions?
Back: making the terms "God" and "faith" taboo
Taboo Your Words

Front: why does playing Taboo not fix profession of belief?
Back: you cannot cognitively zoom in on an audio recording
Taboo Your Words

Front: When you [...], the first line of defense is not to define your problematic terms, but to see whether you can think without using those terms at all.
Back: When you find yourself in philosophical difficulties, the first line of defense is not to define your problematic terms, but to see whether you can think without using those terms at all.
Taboo Your Words

Front: When you find yourself in philosophical difficulties, the first line of defense is not to [...], but to see whether you can think without using those terms at all.
Back: When you find yourself in philosophical difficulties, the first line of defense is not to define your problematic terms, but to see whether you can think without using those terms at all.
Taboo Your Words

Front: When you find yourself in philosophical difficulties, the first line of defense is not to define your problematic terms, but to [...].
Back: When you find yourself in philosophical difficulties, the first line of defense is not to define your problematic terms, but to see whether you can think without using those terms at all.
Taboo Your Words

Front: what do we have to be careful not to do when playing Taboo?
Back: use a short synonyms or invent a new word (use any single handle)
Taboo Your Words

Front: what two categories of things SHOULD we describe when playing Taboo?
Back: outward observables and interior mechanisms
Taboo Your Words

Front: what is one of the most important rules in Rationalist Taboo?
Back: You can't Taboo math.
Taboo Your Words

Front: you can't Taboo math, but what can you do?
Back: point to an element of a mathematical formula and ask "What does this term apply to? Answer without saying..."
Taboo Your Words

Front: 22. what's wrong with: If Albert and Barry aren't allowed to use the word "sound", then Albert will have to say "A tree falling in a deserted forest generates acoustic vibrations", and Barry will say "A tree falling in a deserted forest generates no auditory experiences".
Back: 22. You get into arguments that you could avoid if you just didn't use the word.
37 Ways That Words Can Be Wrong

Front: 22. followup point: If Albert and Barry aren't allowed to use the word "sound", then Albert will have to say "A tree falling in a deserted forest generates acoustic vibrations", and Barry will say "A tree falling in a deserted forest generates no auditory experiences".
Back: 22. When a word poses a problem, the simplest solution is to eliminate the word and its synonyms.
37 Ways That Words Can Be Wrong

Front: what action does it take the describe things without their standard concept-handles?
Back: visualizing and making your mind's eye see the details, as though looking for the first time
Replace the Symbol with the Substance

Front: what is it called when you see something as if for the first time?
Back: performing an Original Seeing
Replace the Symbol with the Substance

Front: taboo: bat
a l, r, t, w r, n a o e s t a h c g a s i
Back: a long, round, tapering, wooden rod, narrowing at one end so that a human can grasp and swing it
Replace the Symbol with the Substance

Front: taboo: ball
a l-c s w a s s p, h b n m-h, w s c g a t, o s w t w r, o c
Back: a leather-covered spheroid with a symmetrical stitching pattern, hard but not metal-hard, which someone can grasp and throw, or strike with the wooden rod, or catch
Replace the Symbol with the Substance

Front: taboo: bases
f p o a g f, t p t t r t a q a p b o t s w t g's a r
Back: fixed positions on a game field, that players try to run to as quickly as possible because of their safety within the game's artificial rules
Replace the Symbol with the Substance

Front: what is the chief obstacle to performing an original seeing?
Back: that your mind already has a nice neat summary, a nice little easy-to-use concept handle
Replace the Symbol with the Substance

Front: It [...] to stop your mind from sliding down the familiar path, the easy path, the path of least resistance, where the small featureless word rushes in and obliterates the details you're trying to see.
Back: It takes an effort to stop your mind from sliding down the familiar path, the easy path, the path of least resistance, where the small featureless word rushes in and obliterates the details you're trying to see.
Replace the Symbol with the Substance

Front: It takes an effort to [...], where the small featureless word rushes in and obliterates the details you're trying to see.
Back: It takes an effort to stop your mind from sliding down the familiar path, the easy path, the path of least resistance, where the small featureless word rushes in and obliterates the details you're trying to see.
Replace the Symbol with the Substance

Front: It takes an effort to stop your mind from sliding down the familiar path, the easy path, the path of least resistance, where the [...] rushes in and obliterates the details you're trying to see.
Back: It takes an effort to stop your mind from sliding down the familiar path, the easy path, the path of least resistance, where the small featureless word rushes in and obliterates the details you're trying to see.
Replace the Symbol with the Substance

Front: It takes an effort to stop your mind from sliding down the familiar path, the easy path, the path of least resistance, where the small featureless word rushes in and [...].
Back: It takes an effort to stop your mind from sliding down the familiar path, the easy path, the path of least resistance, where the small featureless word rushes in and obliterates the details you're trying to see.
Replace the Symbol with the Substance

Front: A [...] can have the destructive force of cliche; a word itself can carry the poison of a cached thought.
Back: A word itself can have the destructive force of cliche; a word itself can carry the poison of a cached thought.
Replace the Symbol with the Substance

Front: A word itself can [...]; a word itself can carry the poison of a cached thought.
Back: A word itself can have the destructive force of cliche; a word itself can carry the poison of a cached thought.
Replace the Symbol with the Substance

Front: A word itself can have the destructive force of cliche; a word itself can [...].
Back: A word itself can have the destructive force of cliche; a word itself can carry the poison of a cached thought.
Replace the Symbol with the Substance

Front: what capability do you get from playing Taboo?
Back: being able to describe without using the standard pointer/label/handle
Replace the Symbol with the Substance

Front: how important is Taboo?
Back: it is one of the fundamental rationalist capabilities
Replace the Symbol with the Substance

Front: Taboo occupies the [...] as the habit of constantly asking "Why?" or "What does this belief make me anticipate?"
Back: Taboo occupies the same primordial level as the habit of constantly asking "Why?" or "What does this belief make me anticipate?"
Replace the Symbol with the Substance

Front: Taboo occupies the same primordial level as [...] or "What does this belief make me anticipate?"
Back: Taboo occupies the same primordial level as the habit of constantly asking "Why?" or "What does this belief make me anticipate?"
Replace the Symbol with the Substance

Front: Taboo occupies the same primordial level as the habit of constantly asking "Why?" or "[...]"
Back: Taboo occupies the same primordial level as the habit of constantly asking "Why?" or "What does this belief make me anticipate?"
Replace the Symbol with the Substance

Front: why is Taboo related to Pragmatism?
Back: seeing in this way often gives you a much closer connection to anticipated experience, rather than propositional belief
Replace the Symbol with the Substance

Front: why is Taboo related to Reductionism?
Back: seeing in this way often forces you to drop down to a lower level of organization, look at the parts instead of your eye skipping over the whole
Replace the Symbol with the Substance

Front: why is Taboo related to Hugging the query?
Back: words often distract you from the question you really want to ask
Replace the Symbol with the Substance

Front: why is Taboo related to Avoiding cached thoughts?
Back: they will rush in using standard words, so you can block them by tabooing standard words
Replace the Symbol with the Substance

Front: what writer's rule has power among rationalists?
Back: "Show, don't tell!"
Replace the Symbol with the Substance

Front: what's an example that illustrates how taboo can help you not lose sight of your purpose?
Back: Why are you going to "school"?  To get an "education" ending in a "degree".  Blank out the forbidden words and all their obvious synonyms, visualize the actual details, and you're much more likely to notice that "school" currently seems to consist of sitting next to bored teenagers listening to material you already know, that a "degree" is a piece of paper with some writing on it, and that "education" is forgetting the material as soon as you're tested on it.
Replace the Symbol with the Substance

Front: how do leaky generalizations often manifest?
Back: through categorizations
Replace the Symbol with the Substance

Front: what is the danger of a mental category that imperfectly matches your goal?
Back: it can lead to an internal incentive failure similar to teaching to the test
Replace the Symbol with the Substance

Front: To [...] is to throw away information.
Back: To categorize is to throw away information.
Replace the Symbol with the Substance

Front: To categorize is to [...].
Back: To categorize is to throw away information.
Replace the Symbol with the Substance

Front: why might we want to categorize to throw out information?
Back: to sift the gold from the dust and ignore irrelevant information
Replace the Symbol with the Substance

Front: what sort of information does standard categorization end up throwing out?
Back: often relevant information as well as irrelevant
Replace the Symbol with the Substance

Front: what is the first and most obvious solution to throwing out relevant information when you categorize?
Back: playing Taboo
Replace the Symbol with the Substance

Front: what is the danger of just thinking "play Taboo to force original seeing"?
Back: you'll start thinking that anything that counts as playing Taboo must count as original seeing
Replace the Symbol with the Substance

Front: what is the rationalist version of Taboo NOT?
Back: a game
Replace the Symbol with the Substance

Front: what follows from the fact that rationalist Taboo is not actually a game?
Back: you can't win by trying to be clever and stretching the rules
Replace the Symbol with the Substance

Front: You are trying to [...], not rename the cities; dereference the pointer, not allocate a new pointer; see the events as they happen, not rewrite the cliche in a different wording.
Back: You are trying to zoom in on your map, not rename the cities; dereference the pointer, not allocate a new pointer; see the events as they happen, not rewrite the cliche in a different wording.
Replace the Symbol with the Substance

Front: You are trying to zoom in on your map, not [...]; dereference the pointer, not allocate a new pointer; see the events as they happen, not rewrite the cliche in a different wording.
Back: You are trying to zoom in on your map, not rename the cities; dereference the pointer, not allocate a new pointer; see the events as they happen, not rewrite the cliche in a different wording.
Replace the Symbol with the Substance

Front: You are trying to zoom in on your map, not rename the cities; [...], not allocate a new pointer; see the events as they happen, not rewrite the cliche in a different wording.
Back: You are trying to zoom in on your map, not rename the cities; dereference the pointer, not allocate a new pointer; see the events as they happen, not rewrite the cliche in a different wording.
Replace the Symbol with the Substance

Front: You are trying to zoom in on your map, not rename the cities; dereference the pointer, not [...]; see the events as they happen, not rewrite the cliche in a different wording.
Back: You are trying to zoom in on your map, not rename the cities; dereference the pointer, not allocate a new pointer; see the events as they happen, not rewrite the cliche in a different wording.
Replace the Symbol with the Substance

Front: You are trying to zoom in on your map, not rename the cities; dereference the pointer, not allocate a new pointer; [...], not rewrite the cliche in a different wording.
Back: You are trying to zoom in on your map, not rename the cities; dereference the pointer, not allocate a new pointer; see the events as they happen, not rewrite the cliche in a different wording.
Replace the Symbol with the Substance

Front: You are trying to zoom in on your map, not rename the cities; dereference the pointer, not allocate a new pointer; see the events as they happen, not [...].
Back: You are trying to zoom in on your map, not rename the cities; dereference the pointer, not allocate a new pointer; see the events as they happen, not rewrite the cliche in a different wording.
Replace the Symbol with the Substance

Front: If you can [...], you will see yourself doing things that you would never dream of doing if they were not habits.
Back: If you can look with fresh eyes, as though for the first time, you will see yourself doing things that you would never dream of doing if they were not habits.
Replace the Symbol with the Substance

Front: If you can look with fresh eyes, as though for the first time, you will [...].
Back: If you can look with fresh eyes, as though for the first time, you will see yourself doing things that you would never dream of doing if they were not habits.
Replace the Symbol with the Substance

Front: lesswrong: when do we lose purpose?
Back: whenever the substance (learning, knowledge, health) is displaced by the symbol (a degree, a test score, medical care)
Replace the Symbol with the Substance

Front: what must we do to heal a lost purpose?
Back: replace the symbol with the substance
Replace the Symbol with the Substance

Front: [...]; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Back: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Replace the Symbol with the Substance

Front: Replace the symbol with the substance; replace [...]; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Back: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Replace the Symbol with the Substance

Front: Replace the symbol with the substance; replace the signifier with the signified; replace [...]; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Back: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Replace the Symbol with the Substance

Front: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace [...]; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Back: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Replace the Symbol with the Substance

Front: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace [...]; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Back: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Replace the Symbol with the Substance

Front: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace [...]; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Back: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Replace the Symbol with the Substance

Front: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace [...]; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Back: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Replace the Symbol with the Substance

Front: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; [...]; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Back: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Replace the Symbol with the Substance

Front: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; [...]; mentally simulate the process instead of naming it; zoom in on your map.
Back: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Replace the Symbol with the Substance

Front: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; [...]; zoom in on your map.
Back: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Replace the Symbol with the Substance

Front: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; [...].
Back: Replace the symbol with the substance; replace the signifier with the signified; replace the property with the membership test; replace the word with the meaning; replace the label with the concept; replace the summary with the details; replace the proxy question with the real question; dereference the pointer; drop into a lower level of organization; mentally simulate the process instead of naming it; zoom in on your map.
Replace the Symbol with the Substance

Front: why is it problematic to overuse the word "rationality"?
Back: our true understanding is measured by your ability to describe what you're doing and why, without using that word or any of its synonyms
Replace the Symbol with the Substance

Front: what measures your true understanding of "rationality"?
Back: your ability to describe what you're doing and why, without using that word or any of its synonyms
Replace the Symbol with the Substance

Front: 23. what's wrong with: What actually goes on in schools once you stop calling it "education"? What's a degree, once you stop calling it a "degree"?  If a coin lands "heads", what's its radial orientation?  What is "truth", if you can't say "accurate" or "correct" or "represent" or "reflect" or "semantic" or "believe" or "knowledge" or "map" or "real" or any other simple term?
Back: 23. The existence of a neat little word prevents you from seeing the details of the thing you're trying to think about.
37 Ways That Words Can Be Wrong

Front: what is the other saying about the map and the territory?
Back: "The map is not the territory, but you can't fold up the territory and put it in your glove compartment."
Fallacies of Compression

Front: what is the consequence of reality being much, much larger than our map of it?
Back: certain things that are distinct in reality, will be compressed into the same point on your map
Fallacies of Compression

Front: how does the map being smaller than the territory feel from the inside?
Back: that there is just one thing, and you are seeing it
Fallacies of Compression

Front: how does the map being smaller than the territory NOT feel from the inside?
Back: "Oh, look, I'm compressing two things into one point on my map."
Fallacies of Compression

Front: To realize that there are [...], underlying one point on your map, is an essentially scientific challenge - a big, difficult scientific challenge.
Back: To realize that there are two distinct events, underlying one point on your map, is an essentially scientific challenge - a big, difficult scientific challenge.
Fallacies of Compression

Front: To realize that there are two distinct events, underlying [...], is an essentially scientific challenge - a big, difficult scientific challenge.
Back: To realize that there are two distinct events, underlying one point on your map, is an essentially scientific challenge - a big, difficult scientific challenge.
Fallacies of Compression

Front: To realize that there are two distinct events, underlying one point on your map, is an essentially [...].
Back: To realize that there are two distinct events, underlying one point on your map, is an essentially scientific challenge - a big, difficult scientific challenge.
Fallacies of Compression

Front: what is the less dangerous source of fallacies of compression?
Back: confusing two known things under the same label
Fallacies of Compression

Front: what's an example of confusing two known things under the same label?
Back: you know about acoustic vibrations, and you know about auditory processing in brains, but you call them both "sound" and so confuse yourself
Fallacies of Compression

Front: what is the more dangerous source of fallacies of compression?
Back: having no idea whatsoever that two distinct entities even exist
Fallacies of Compression

Front: what's an example of having no idea that two distinct entities even exist?
Back: having just one mental folder in the filing system, labeled "sound", and everything thought about "sound" drops into that one folder
Fallacies of Compression

Front: The [...], unlike a paper map of California, can expand dynamically when we write down more detailed descriptions.
Back: The map of reality contained in a human brain, unlike a paper map of California, can expand dynamically when we write down more detailed descriptions.
Fallacies of Compression

Front: The map of reality contained in a human brain, unlike [...], can expand dynamically when we write down more detailed descriptions.
Back: The map of reality contained in a human brain, unlike a paper map of California, can expand dynamically when we write down more detailed descriptions.
Fallacies of Compression

Front: The map of reality contained in a human brain, unlike a paper map of California, can [...] when we write down more detailed descriptions.
Back: The map of reality contained in a human brain, unlike a paper map of California, can expand dynamically when we write down more detailed descriptions.
Fallacies of Compression

Front: The map of reality contained in a human brain, unlike a paper map of California, can expand dynamically when we [...].
Back: The map of reality contained in a human brain, unlike a paper map of California, can expand dynamically when we write down more detailed descriptions.
Fallacies of Compression

Front: what does expanding our map of reality not particularly feel like from the inside?
Back: like zooming in on a map
Fallacies of Compression

Front: what does expanding our map of reality feel like from the inside?
Back: fissioning an indivisible atom - taking one thing (it felt like one thing) and splitting it into two or more things
Fallacies of Compression

Front: what does allocating two buckets often result in?
Back: creating new words to describe the different concepts
Fallacies of Compression

Front: is expanding the map always as simple an generating new city names?
Back: no
Fallacies of Compression

Front: It is a [...] to realize that such things as acoustic vibrations, or auditory experiences, even exist.
Back: It is a stroke of scientific insight to realize that such things as acoustic vibrations, or auditory experiences, even exist.
Fallacies of Compression

Front: It is a stroke of scientific insight to realize that [...].
Back: It is a stroke of scientific insight to realize that such things as acoustic vibrations, or auditory experiences, even exist.
Fallacies of Compression

Front: what two words are modern-day illustrations of the confusion created by lumping concepts under single labels?
Back: "intelligence" and "consciousness"
Fallacies of Compression

Front: what underlie the bait-and-switch technique in philosophy?
Back: fallacies of compression
Fallacies of Compression

Front: what is the bait-and-switch technique in philosophy?
Back: you argue about "consciousness" under one definition (like the ability to think about thinking) and then apply the conclusions to "consciousness" under a different definition (like subjectivity)
Fallacies of Compression

Front: Where you see [...], with protean and self-contradictory attributes, it is a good guess that your map is cramming too much into one point - you need to pry it apart and allocate some new buckets.
Back: Where you see a single confusing thing, with protean and self-contradictory attributes, it is a good guess that your map is cramming too much into one point - you need to pry it apart and allocate some new buckets.
Fallacies of Compression

Front: Where you see a single confusing thing, with [...], it is a good guess that your map is cramming too much into one point - you need to pry it apart and allocate some new buckets.
Back: Where you see a single confusing thing, with protean and self-contradictory attributes, it is a good guess that your map is cramming too much into one point - you need to pry it apart and allocate some new buckets.
Fallacies of Compression

Front: Where you see a single confusing thing, with protean and self-contradictory attributes, it is a good guess that [...] - you need to pry it apart and allocate some new buckets.
Back: Where you see a single confusing thing, with protean and self-contradictory attributes, it is a good guess that your map is cramming too much into one point - you need to pry it apart and allocate some new buckets.
Fallacies of Compression

Front: Where you see a single confusing thing, with protean and self-contradictory attributes, it is a good guess that your map is cramming too much into one point - you need to [...].
Back: Where you see a single confusing thing, with protean and self-contradictory attributes, it is a good guess that your map is cramming too much into one point - you need to pry it apart and allocate some new buckets.
Fallacies of Compression

Front: what is prying apart a concept and allocating some new buckets NOT like? (not zooming in on a map)
Back: defining the single thing you see
Fallacies of Compression

Front: what does prying apart a concept and allocating some new buckets often follow from?
Back: figuring out how to talk about the thing without using a single mental handle
Fallacies of Compression

Front: Words often represent [...], the labels under which we file our propositions and the buckets into which we drop our information.
Back: Words often represent the points on our map, the labels under which we file our propositions and the buckets into which we drop our information.
Fallacies of Compression

Front: Words often represent the points on our map, [...] and the buckets into which we drop our information.
Back: Words often represent the points on our map, the labels under which we file our propositions and the buckets into which we drop our information.
Fallacies of Compression

Front: Words often represent the points on our map, the labels under which we file our propositions and [...].
Back: Words often represent the points on our map, the labels under which we file our propositions and the buckets into which we drop our information.
Fallacies of Compression

Front: [...], or allocating new ones, is often part of the skill of expanding the map.
Back: Avoiding a single word, or allocating new ones, is often part of the skill of expanding the map.
Fallacies of Compression

Front: Avoiding a single word, or [...], is often part of the skill of expanding the map.
Back: Avoiding a single word, or allocating new ones, is often part of the skill of expanding the map.
Fallacies of Compression

Front: Avoiding a single word, or allocating new ones, is often part of [...].
Back: Avoiding a single word, or allocating new ones, is often part of the skill of expanding the map.
Fallacies of Compression

Front: what is especially odd about the Japanese blood type theory of personality?
Back: blood types have never been mysterious, not in Japan and not anywhere
Categorizing Has Consequences

Front: because blood types have never actually been mysterious, what can we conclude about where the blood type theory of personality came from?
Back: The stereotypes must have arisen strictly from the mere existence of the labels.
Categorizing Has Consequences

Front: does Eliezer think stereotypes would arise from categorizing plants, or rocks, or office furniture?
Back: yes
Categorizing Has Consequences

Front: once you draw a boundary around a group, what does the mind do?
Back: the mind starts trying to harvest similarities from the group
Categorizing Has Consequences

Front: how do human pattern-detectors seem to operate?
Back: in such overdrive that we see patterns whether they're there or not
Categorizing Has Consequences

Front: how might we interpret a weakly negative correlation?
Back: a weakly negative correlation can be mistaken for a strong positive one with a bit of selective memory
Categorizing Has Consequences

Front: in terms of neural algorithms, what is creating a name for a set of things like?
Back: allocating a subnetwork to find patterns in them
Categorizing Has Consequences

Front: as soon as you name the category, what can you do?
Back: start making up stuff about it
Categorizing Has Consequences

Front: what has to be true of the category for us to start making stuff up about it?
Back: not much--it doesn't have to be perceptible; it doesn't have to exist; it doesn't even have to be coherent
Categorizing Has Consequences

Front: [...], drawing a boundary in thingspace is not a neutral act.
Back: Any way you look at it, drawing a boundary in thingspace is not a neutral act.
Categorizing Has Consequences

Front: Any way you look at it, [...] is not a neutral act.
Back: Any way you look at it, drawing a boundary in thingspace is not a neutral act.
Categorizing Has Consequences

Front: Any way you look at it, drawing a boundary in thingspace is [...].
Back: Any way you look at it, drawing a boundary in thingspace is not a neutral act.
Categorizing Has Consequences

Front: [...] are not static things in the context of a human brain; as soon as you actually think of them, they exert force on your mind.
Back: Categories are not static things in the context of a human brain; as soon as you actually think of them, they exert force on your mind.
Categorizing Has Consequences

Front: Categories are not [...] in the context of a human brain; as soon as you actually think of them, they exert force on your mind.
Back: Categories are not static things in the context of a human brain; as soon as you actually think of them, they exert force on your mind.
Categorizing Has Consequences

Front: Categories are not static things [...]; as soon as you actually think of them, they exert force on your mind.
Back: Categories are not static things in the context of a human brain; as soon as you actually think of them, they exert force on your mind.
Categorizing Has Consequences

Front: Categories are not static things in the context of a human brain; [...], they exert force on your mind.
Back: Categories are not static things in the context of a human brain; as soon as you actually think of them, they exert force on your mind.
Categorizing Has Consequences

Front: Categories are not static things in the context of a human brain; as soon as you actually think of them, [...].
Back: Categories are not static things in the context of a human brain; as soon as you actually think of them, they exert force on your mind.
Categorizing Has Consequences

Front: 24. what's wrong with: It's part of a detective's ordinary work to observe that Carol wore red last night, or that she has black hair; and it's part of a detective's ordinary work to wonder if maybe Carol dyes her hair.
Back: 24. You have only one word, but there are two or more different things-in-reality, so that all the facts about them get dumped into a single undifferentiated mental bucket.
37 Ways That Words Can Be Wrong

Front: 24. followup point: It's part of a detective's ordinary work to observe that Carol wore red last night, or that she has black hair; and it's part of a detective's ordinary work to wonder if maybe Carol dyes her hair.
Back: 24. But it takes a subtler detective to wonder if there are two Carols, so that the Carol who wore red is not the same as the Carol who had black hair.
37 Ways That Words Can Be Wrong

Front: 25. what's wrong with: In Japan, it is thought that people of blood type A are earnest and creative, blood type Bs are wild and cheerful, blood type Os are agreeable and sociable, and blood type ABs are cool and controlled.
Back: 25. You see patterns where none exist, harvesting other characteristics from your definitions even when there is no similarity along that dimension.
37 Ways That Words Can Be Wrong

Front: [...] passes from observed characteristics to inferred characteristics via the medium of words.
Back: The human mind passes from observed characteristics to inferred characteristics via the medium of words.
Sneaking in Connotations

Front: The human mind passes from [...] to inferred characteristics via the medium of words.
Back: The human mind passes from observed characteristics to inferred characteristics via the medium of words.
Sneaking in Connotations

Front: The human mind passes from observed characteristics to [...] via the medium of words.
Back: The human mind passes from observed characteristics to inferred characteristics via the medium of words.
Sneaking in Connotations

Front: The human mind passes from observed characteristics to inferred characteristics via [...].
Back: The human mind passes from observed characteristics to inferred characteristics via the medium of words.
Sneaking in Connotations

Front: In "All humans are mortal, Socrates is a human, therefore Socrates is mortal", what are the observed characteristics?
Back: Socrates's clothes, speech, tool use, and generally human shape
Sneaking in Connotations

Front: In "All humans are mortal, Socrates is a human, therefore Socrates is mortal", what is the categorization?
Back: "human"
Sneaking in Connotations

Front: In "All humans are mortal, Socrates is a human, therefore Socrates is mortal", what is the inferred characteristics?
Back: poisonability by hemlock
Sneaking in Connotations

Front: is there a hard distinction between "observed characteristics" and "inferred characteristics"?
Back: no
Sneaking in Connotations

Front: do some properties tend to be more inferred than observed?
Back: yes
Sneaking in Connotations

Front: You're more likely to [...], and will therefore burn if exposed to open flame, than carry through the inference the other way around.
Back: You're more likely to decide that someone is human, and will therefore burn if exposed to open flame, than carry through the inference the other way around.
Sneaking in Connotations

Front: You're more likely to decide that someone is human, and will [...], than carry through the inference the other way around.
Back: You're more likely to decide that someone is human, and will therefore burn if exposed to open flame, than carry through the inference the other way around.
Sneaking in Connotations

Front: You're more likely to decide that someone is human, and will therefore burn if exposed to open flame, than [...].
Back: You're more likely to decide that someone is human, and will therefore burn if exposed to open flame, than carry through the inference the other way around.
Sneaking in Connotations

Front: what sorts of characteristics are we likely to find in a dictionary definition of human?
Back: characteristics that are useful for quickly eyeballing what is and isn't a human
Sneaking in Connotations

Front: what sorts of characteristics are we NOT likely to find in a dictionary definition of human?
Back: the ten thousand connotations, from vulnerability to hemlock, to overconfidence, that we can infer from someone's being human
Sneaking in Connotations

Front: why are two reasons dictionaries more likely to talk about observed characteristics than inferrered characteristics?
Back: Perhaps dictionaries are intended to let you match up labels to similarity groups, and so are designed to quickly isolate clusters in thingspace.  Or perhaps the big, distinguishing characteristics are the most salient, and therefore first to pop into a dictionary editor's mind.
Sneaking in Connotations

Front: what will the OED say about "wiggin"?
Back: the first-glance characteristics that distinguish a wiggin:  Green eyes and black hair
Sneaking in Connotations

Front: what will the OED NOT say about "wiggin"?
Back: the many minor connotations that have come to attach to this term, such as criminal proclivities, culinary peculiarities, and some unfortunate childhood activities
Sneaking in Connotations

Front: As soon as you [...], the word will begin acquiring connotations.
Back: As soon as you call some people "wiggins", the word will begin acquiring connotations.
Sneaking in Connotations

Front: As soon as you call some people "wiggins", the word will [...].
Back: As soon as you call some people "wiggins", the word will begin acquiring connotations.
Sneaking in Connotations

Front: whenever someone pulls out a dictionary, what are they generally trying to do?
Back: sneak in a connotation, not the actual definition written down in the dictionary
Sneaking in Connotations

Front: Oh, but arguing the [...] would require work.
Back: Oh, but arguing the real question would require work.
Sneaking in Connotations

Front: Oh, but arguing the real question would require [...].
Back: Oh, but arguing the real question would require work.
Sneaking in Connotations

Front: what does arguing "by definition" allow you to do simultaneously?
Back: sit in your living room with your eyes closed
Sneaking in Connotations

Front: why do people like to argue that they get to sit in their living room with their eyes closed and argue "by definition"?
Back: people are lazy
Sneaking in Connotations

Front: how does "Look, he's got green eyes and black hair.  He's a wiggin!  It says so right there in the dictionary! - therefore, he's got black hair.  Argue with that, if you can!" sound?
Back: empty
Sneaking in Connotations

Front: If the [...] actually was contained in the dictionary definition - if the argument genuinely was logically valid - then the argument would feel empty; it would either say nothing new, or beg the question.
Back: If the real point of the argument actually was contained in the dictionary definition - if the argument genuinely was logically valid - then the argument would feel empty; it would either say nothing new, or beg the question.
Sneaking in Connotations

Front: If the real point of the argument actually was [...] - if the argument genuinely was logically valid - then the argument would feel empty; it would either say nothing new, or beg the question.
Back: If the real point of the argument actually was contained in the dictionary definition - if the argument genuinely was logically valid - then the argument would feel empty; it would either say nothing new, or beg the question.
Sneaking in Connotations

Front: If the real point of the argument actually was contained in the dictionary definition - [...] - then the argument would feel empty; it would either say nothing new, or beg the question.
Back: If the real point of the argument actually was contained in the dictionary definition - if the argument genuinely was logically valid - then the argument would feel empty; it would either say nothing new, or beg the question.
Sneaking in Connotations

Front: If the real point of the argument actually was contained in the dictionary definition - if the argument genuinely was logically valid - then the argument would [...]; it would either say nothing new, or beg the question.
Back: If the real point of the argument actually was contained in the dictionary definition - if the argument genuinely was logically valid - then the argument would feel empty; it would either say nothing new, or beg the question.
Sneaking in Connotations

Front: If the real point of the argument actually was contained in the dictionary definition - if the argument genuinely was logically valid - then the argument would feel empty; it would either [...], or beg the question.
Back: If the real point of the argument actually was contained in the dictionary definition - if the argument genuinely was logically valid - then the argument would feel empty; it would either say nothing new, or beg the question.
Sneaking in Connotations

Front: If the real point of the argument actually was contained in the dictionary definition - if the argument genuinely was logically valid - then the argument would feel empty; it would either say nothing new, or [...].
Back: If the real point of the argument actually was contained in the dictionary definition - if the argument genuinely was logically valid - then the argument would feel empty; it would either say nothing new, or beg the question.
Sneaking in Connotations

Front: what is the only thing that makes people think they can score a point by consulting a dictionary?
Back: the attempt to smuggle in connotations not explicitly listed in the definition
Sneaking in Connotations

Front: how does Eliezer respond to AnneC's objection to the shortcut of assuming things about her because she's a girl?
Back: What you're objecting to isn't so much the shortcut, it seems to me, as the way-too-short, much-shorter-than-necessary cut.
Sneaking in Connotations

Front: is Eliezer trying to say "Don't categorize"?
Back: no
Sneaking in Connotations

Front: what is Eliezer trying to say instead of "Don't categorize"?
Back: "Since you have no choice but to categorize, do it right."
Sneaking in Connotations

Front: how does Eliezer think it's important for AnneC to see the type of categorization she doesn't like?
Back: as 'inept categorization'
Sneaking in Connotations

Front: what does 'inept categorization' include?
Back: attempts to infer from the category things that have already been observed and hence ought properly to be screened off
Sneaking in Connotations

Front: what is less useful than thinking of something as 'inept categorization'?
Back: thinking of it as 'forbidden categorization'
Sneaking in Connotations

Front: 26. what's wrong with: A "wiggin" is defined in the dictionary as a person with green eyes and black hair.  The word "wiggin" also carries the connotation of someone who commits crimes and launches cute baby squirrels, but that part isn't in the dictionary.
Back: 26. You try to sneak in the connotations of a word, by arguing from a definition that doesn't include the connotations.
37 Ways That Words Can Be Wrong

Front: 26. followup point: A "wiggin" is defined in the dictionary as a person with green eyes and black hair.  The word "wiggin" also carries the connotation of someone who commits crimes and launches cute baby squirrels, but that part isn't in the dictionary.
Back: 26. So you point to someone and say:  "Green eyes?  Black hair?  See, told you he's a wiggin!  Watch, next he's going to steal the silverware."
37 Ways That Words Can Be Wrong

Front: what doesn't have to be true of the category for us to start making stuff up about it?
Back: it doesn't have to be perceptible; it doesn't have to exist; it doesn't even have to be coherent
Arguing "By Definition"

Front: when people argue definitions, where do they usually start?
Back: with some visible, known, or at least widely believed set of characteristics
Arguing "By Definition"

Front: when arguing definitions, after starting with some visible, known, or at least widely believed set of characteristics, what do they usually do?
Back: then pull out a dictionary, and point out that these characteristics fit the dictionary definition
Arguing "By Definition"

Front: once people point out that characteristics fit the dictionary definition, what do they say?
Back: they conclude, "Therefore, by definition, atheism is a religion!"
Arguing "By Definition"

Front: But [...], known, widely believed characteristics are rarely the real point of a dispute.
Back: But visible, known, widely believed characteristics are rarely the real point of a dispute.
Arguing "By Definition"

Front: But visible, [...], widely believed characteristics are rarely the real point of a dispute.
Back: But visible, known, widely believed characteristics are rarely the real point of a dispute.
Arguing "By Definition"

Front: But visible, known, [...] characteristics are rarely the real point of a dispute.
Back: But visible, known, widely believed characteristics are rarely the real point of a dispute.
Arguing "By Definition"

Front: But visible, known, widely believed [...] are rarely the real point of a dispute.
Back: But visible, known, widely believed characteristics are rarely the real point of a dispute.
Arguing "By Definition"

Front: But visible, known, widely believed characteristics are [...] of a dispute.
Back: But visible, known, widely believed characteristics are rarely the real point of a dispute.
Arguing "By Definition"

Front: what does the fact that someone thinks Socrates's two legs are evident enough to make a good premise for the argument, "Therefore, by definition, Socrates is human!" indicate?
Back: that bipedalism probably isn't really what's at stake
Arguing "By Definition"

Front: if bipedalism were really at stake in the argument about whether Socrates was human, how would the listener reply?
Back: "Whaddaya mean Socrates is bipedal?  That's what we're arguing about in the first place!"
Arguing "By Definition"

Front: is there an important sense in which we can legitimately move from evident characteristics to not-so-evident characteristics?
Back: yes
Arguing "By Definition"

Front: You can, [...], see that Socrates is human-shaped, and predict his vulnerability to hemlock.
Back: You can, legitimately, see that Socrates is human-shaped, and predict his vulnerability to hemlock.
Arguing "By Definition"

Front: You can, legitimately, see that Socrates is [...], and predict his vulnerability to hemlock.
Back: You can, legitimately, see that Socrates is human-shaped, and predict his vulnerability to hemlock.
Arguing "By Definition"

Front: You can, legitimately, see that Socrates is human-shaped, and predict [...].
Back: You can, legitimately, see that Socrates is human-shaped, and predict his vulnerability to hemlock.
Arguing "By Definition"

Front: what does the probablistic inference that Socrates is human-shaped and therefore probably vulnerable to hemlock rely on?
Back: the universe containing empirical structures of similar things
Arguing "By Definition"

Front: what does the probablistic inference that Socrates is human-shaped and therefore probably vulnerable to hemlock NOT rely on?
Back: dictionary definitions or common usage
Arguing "By Definition"

Front: how does the cluster change when you define your words?
Back: it doesn't
Arguing "By Definition"

Front: if the dictionary definition says "all featherless bipeds except Socrates", how does that change the actual degree to which Socrates is similar to the rest of us featherless bipeds?
Back: it doesn't
Arguing "By Definition"

Front: classify: "Socrates has two arms, two feet, a nose and tongue, speaks fluent Greek, uses tools, and in every aspect I've been able to observe him, seems to have every major and minor property that characterizes Homo sapiens; so I'm going to guess that he has human DNA, human biochemistry, and is vulnerable to hemlock just like all other Homo sapiens in whom hemlock has been clinically tested for lethality."
Back: correctly arguing from cluster structure
Arguing "By Definition"

Front: what's an example of correctly arguing from cluster structure?
Back: "Socrates has two arms, two feet, a nose and tongue, speaks fluent Greek, uses tools, and in every aspect I've been able to observe him, seems to have every major and minor property that characterizes Homo sapiens; so I'm going to guess that he has human DNA, human biochemistry, and is vulnerable to hemlock just like all other Homo sapiens in whom hemlock has been clinically tested for lethality."
Arguing "By Definition"

Front: And suppose I reply, "[...]"
Now there's not much point in arguing over whether Socrates is "human" or not.
Back: And suppose I reply, "But I saw Socrates out in the fields with some herbologists; I think they were trying to prepare an antidote. Therefore I don't expect Socrates to keel over after he drinks the hemlock - he will be an exception to the general behavior of objects in his cluster: they did not take an antidote, and he did."
Now there's not much point in arguing over whether Socrates is "human" or not.
Arguing "By Definition"

Front: And suppose I reply, "But I saw Socrates out in the fields with some herbologists; I think they were trying to prepare an antidote. Therefore I don't expect Socrates to keel over after he drinks the hemlock - he will be an exception to the general behavior of objects in his cluster: they did not take an antidote, and he did."
Now there's [...] arguing over whether Socrates is "human" or not.
Back: And suppose I reply, "But I saw Socrates out in the fields with some herbologists; I think they were trying to prepare an antidote. Therefore I don't expect Socrates to keel over after he drinks the hemlock - he will be an exception to the general behavior of objects in his cluster: they did not take an antidote, and he did."
Now there's not much point in arguing over whether Socrates is "human" or not.
Arguing "By Definition"

Front: And suppose I reply, "But I saw Socrates out in the fields with some herbologists; I think they were trying to prepare an antidote. Therefore I don't expect Socrates to keel over after he drinks the hemlock - he will be an exception to the general behavior of objects in his cluster: they did not take an antidote, and he did."
Now there's not much point in [...].
Back: And suppose I reply, "But I saw Socrates out in the fields with some herbologists; I think they were trying to prepare an antidote. Therefore I don't expect Socrates to keel over after he drinks the hemlock - he will be an exception to the general behavior of objects in his cluster: they did not take an antidote, and he did."
Now there's not much point in arguing over whether Socrates is "human" or not.
Arguing "By Definition"

Front: once we have more specific information about Socrates and his vulnerability to hemlock, what must happen?
Back: the conversation has to more to a more detailed level
Arguing "By Definition"

Front: The conversation has to [...], poke around inside the details that make up the "human" category - talk about human biochemistry, and specifically, the neurotoxic effects of coniine.
Back: The conversation has to move to a more detailed level, poke around inside the details that make up the "human" category - talk about human biochemistry, and specifically, the neurotoxic effects of coniine.
Arguing "By Definition"

Front: The conversation has to move to a more detailed level, [...] - talk about human biochemistry, and specifically, the neurotoxic effects of coniine.
Back: The conversation has to move to a more detailed level, poke around inside the details that make up the "human" category - talk about human biochemistry, and specifically, the neurotoxic effects of coniine.
Arguing "By Definition"

Front: The conversation has to move to a more detailed level, poke around inside the details that make up the "human" category - talk about [...], and specifically, the neurotoxic effects of coniine.
Back: The conversation has to move to a more detailed level, poke around inside the details that make up the "human" category - talk about human biochemistry, and specifically, the neurotoxic effects of coniine.
Arguing "By Definition"

Front: The conversation has to move to a more detailed level, poke around inside the details that make up the "human" category - talk about human biochemistry, and specifically, [...].
Back: The conversation has to move to a more detailed level, poke around inside the details that make up the "human" category - talk about human biochemistry, and specifically, the neurotoxic effects of coniine.
Arguing "By Definition"

Front: if you go on insisting, "But Socrates is a human and humans, by definition, are mortal!" then what are you trying to do?
Back: blur out everything you know about Socrates except the fact of his humanity
Arguing "By Definition"

Front: what does it mean to try to blur out everything you know about Socrates except the fact of his humanity?
Back: insist that the only correct prediction is the one you would make if you knew nothing about Socrates except that he was human
Arguing "By Definition"

Front: why is it illegal under Bayesian probability theory to insist that the only correct prediction is the one you would make if you knew nothing about Socrates except that he was human?
Back: You can't just refuse to condition on new evidence.
Arguing "By Definition"

Front: what is it like to insist that the only correct prediction is the one you would make if you knew nothing about Socrates except that he was human?
Back: like insisting that a coin is 50% likely to be showing heads or tails, because it is a "fair coin", after you've actually looked at the coin and it's showing heads.  It's like insisting that Frodo has ten fingers, because most hobbits have ten fingers, after you've already looked at his hands and seen nine fingers.
Arguing "By Definition"

Front: when can you just keep one categorization and make estimates based on that, while deliberately throwing out everything else you know?
Back: never
Arguing "By Definition"

Front: what can you not do with your estimates based on one categorization?
Back: just keep that and throw out everything else you know
Arguing "By Definition"

Front: does every new piece of evidence make a significant difference?
Back: no
Arguing "By Definition"

Front: will seeing that Socrates has nine fingers noticeably change my estimate of his vulnerability to hemlock?
Back: no
Arguing "By Definition"

Front: WHY will seeing that Socrates has nine fingers not noticeably change my estimate of his vulnerability to hemlock?
Back: I'll expect that the way Socrates lost his finger didn't change the rest of his biochemistry
Arguing "By Definition"

Front: The [...] is based on the cluster structure of the environment, and the causal structure of biology; not what the dictionary editor writes down, nor even "common usage".
Back: The legal inference is based on the cluster structure of the environment, and the causal structure of biology; not what the dictionary editor writes down, nor even "common usage".
Arguing "By Definition"

Front: The legal inference is based on [...], and the causal structure of biology; not what the dictionary editor writes down, nor even "common usage".
Back: The legal inference is based on the cluster structure of the environment, and the causal structure of biology; not what the dictionary editor writes down, nor even "common usage".
Arguing "By Definition"

Front: The legal inference is based on the cluster structure of the environment, and [...]; not what the dictionary editor writes down, nor even "common usage".
Back: The legal inference is based on the cluster structure of the environment, and the causal structure of biology; not what the dictionary editor writes down, nor even "common usage".
Arguing "By Definition"

Front: The legal inference is based on the cluster structure of the environment, and the causal structure of biology; not [...], nor even "common usage".
Back: The legal inference is based on the cluster structure of the environment, and the causal structure of biology; not what the dictionary editor writes down, nor even "common usage".
Arguing "By Definition"

Front: The legal inference is based on the cluster structure of the environment, and the causal structure of biology; not what the dictionary editor writes down, nor even [...].
Back: The legal inference is based on the cluster structure of the environment, and the causal structure of biology; not what the dictionary editor writes down, nor even "common usage".
Arguing "By Definition"

Front: Now ordinarily, when you're [...] you just say, "The coniine alkaloid found in hemlock produces muscular paralysis in humans, resulting in death by asphyxiation."
Back: Now ordinarily, when you're doing this right - in a legitimate way - you just say, "The coniine alkaloid found in hemlock produces muscular paralysis in humans, resulting in death by asphyxiation."
Arguing "By Definition"

Front: Now ordinarily, when you're doing this right - in a legitimate way - you just say, "[...]." T c a f i h p m p i h, r i d b a
Back: Now ordinarily, when you're doing this right - in a legitimate way - you just say, "The coniine alkaloid found in hemlock produces muscular paralysis in humans, resulting in death by asphyxiation."
Arguing "By Definition"

Front: how is the argument that Socrates will die when he drinks hemlock usually presented legitimately?
Back: "Humans are vulnerable to hemlock."
Arguing "By Definition"

Front: When would someone feel the need to strengthen the argument with the emphatic phrase "by definition"?
Back: Why, when the inferred characteristic has been called into doubt - Socrates has been seen consulting herbologists - and so the speaker feels the need to tighten the vise of logic
Arguing "By Definition"

Front: translate: "Humans are vulnerable to hemlock by definition!"
Back: "Forget what you've heard about Socrates consulting herbologists - humans, by definition, are mortal!"
Arguing "By Definition"

Front: People feel the need to [...] by saying "Any P, by definition, has property Q!", on exactly those occasions when they see, and prefer to dismiss out of hand, additional arguments that call into doubt the default inference based on clustering.
Back: People feel the need to squeeze the argument onto a single course by saying "Any P, by definition, has property Q!", on exactly those occasions when they see, and prefer to dismiss out of hand, additional arguments that call into doubt the default inference based on clustering.
Arguing "By Definition"

Front: People feel the need to squeeze the argument onto a single course by saying "[...]", on exactly those occasions when they see, and prefer to dismiss out of hand, additional arguments that call into doubt the default inference based on clustering.
Back: People feel the need to squeeze the argument onto a single course by saying "Any P, by definition, has property Q!", on exactly those occasions when they see, and prefer to dismiss out of hand, additional arguments that call into doubt the default inference based on clustering.
Arguing "By Definition"

Front: People feel the need to squeeze the argument onto a single course by saying "Any P, by definition, has property Q!", on [...] when they see, and prefer to dismiss out of hand, additional arguments that call into doubt the default inference based on clustering.
Back: People feel the need to squeeze the argument onto a single course by saying "Any P, by definition, has property Q!", on exactly those occasions when they see, and prefer to dismiss out of hand, additional arguments that call into doubt the default inference based on clustering.
Arguing "By Definition"

Front: People feel the need to squeeze the argument onto a single course by saying "Any P, by definition, has property Q!", on exactly those occasions when they see, and [...], additional arguments that call into doubt the default inference based on clustering.
Back: People feel the need to squeeze the argument onto a single course by saying "Any P, by definition, has property Q!", on exactly those occasions when they see, and prefer to dismiss out of hand, additional arguments that call into doubt the default inference based on clustering.
Arguing "By Definition"

Front: People feel the need to squeeze the argument onto a single course by saying "Any P, by definition, has property Q!", on exactly those occasions when they see, and prefer to dismiss out of hand, [...] that call into doubt the default inference based on clustering.
Back: People feel the need to squeeze the argument onto a single course by saying "Any P, by definition, has property Q!", on exactly those occasions when they see, and prefer to dismiss out of hand, additional arguments that call into doubt the default inference based on clustering.
Arguing "By Definition"

Front: People feel the need to squeeze the argument onto a single course by saying "Any P, by definition, has property Q!", on exactly those occasions when they see, and prefer to dismiss out of hand, additional arguments that [...] the default inference based on clustering.
Back: People feel the need to squeeze the argument onto a single course by saying "Any P, by definition, has property Q!", on exactly those occasions when they see, and prefer to dismiss out of hand, additional arguments that call into doubt the default inference based on clustering.
Arguing "By Definition"

Front: People feel the need to squeeze the argument onto a single course by saying "Any P, by definition, has property Q!", on exactly those occasions when they see, and prefer to dismiss out of hand, additional arguments that call into doubt the [...].
Back: People feel the need to squeeze the argument onto a single course by saying "Any P, by definition, has property Q!", on exactly those occasions when they see, and prefer to dismiss out of hand, additional arguments that call into doubt the default inference based on clustering.
Arguing "By Definition"

Front: why would you feel the need to say "Hinduism, by definition, is a religion!"
Back: you wouldn't
Arguing "By Definition"

Front: why wouldn't you feel the need to say "Hinduism, by definition, is a religion!"
Back: because, well, of course Hinduism is a religion
Arguing "By Definition"

Front: what sort of a religion is Hinduism?
Back: It's not just a religion "by definition", it's, like, an actual religion.
Arguing "By Definition"

Front: if it wasn't for the fact that atheism was a religion by definition, what might you go around thinking?
Back: that atheism wasn't a religion
Arguing "By Definition"

Front: why might people go around thinking that atheism wasn't a religion if it weren't for its being one by definition?
Back: atheism does not resemble the central members of the "religion" cluster
Arguing "By Definition"

Front: why do you have to crush all opposition by pointing out that "Atheism" is a religion is true by definition?
Back: because it isn't true any other way
Arguing "By Definition"

Front: People insist that "[...]" on those occasions when they're trying to sneak in a connotation of Y that isn't directly in the definition, and X doesn't look all that much like other members of the Y cluster.
Back: People insist that "X, by definition, is a Y!" on those occasions when they're trying to sneak in a connotation of Y that isn't directly in the definition, and X doesn't look all that much like other members of the Y cluster.
Arguing "By Definition"

Front: People insist that "X, by definition, is a Y!" on those occasions when [...] that isn't directly in the definition, and X doesn't look all that much like other members of the Y cluster.
Back: People insist that "X, by definition, is a Y!" on those occasions when they're trying to sneak in a connotation of Y that isn't directly in the definition, and X doesn't look all that much like other members of the Y cluster.
Arguing "By Definition"

Front: People insist that "X, by definition, is a Y!" on those occasions when they're trying to sneak in a connotation of Y that [...], and X doesn't look all that much like other members of the Y cluster.
Back: People insist that "X, by definition, is a Y!" on those occasions when they're trying to sneak in a connotation of Y that isn't directly in the definition, and X doesn't look all that much like other members of the Y cluster.
Arguing "By Definition"

Front: People insist that "X, by definition, is a Y!" on those occasions when they're trying to sneak in a connotation of Y that isn't directly in the definition, and [...].
Back: People insist that "X, by definition, is a Y!" on those occasions when they're trying to sneak in a connotation of Y that isn't directly in the definition, and X doesn't look all that much like other members of the Y cluster.
Arguing "By Definition"

Front: over the last thirteen years, what has Eliezer observed the track record of the phrase by definition (outside of math) to be?
Back: it's among the most alarming signls of flawed argument he's ever found
Arguing "By Definition"

Front: Arguing "[...]" is right up there with "Hitler", "God", "absolutely certain" and "can't prove that".
Back: Arguing "by definition" is right up there with "Hitler", "God", "absolutely certain" and "can't prove that".
Arguing "By Definition"

Front: Arguing "by definition" is right up there with "[...]", "God", "absolutely certain" and "can't prove that".
Back: Arguing "by definition" is right up there with "Hitler", "God", "absolutely certain" and "can't prove that".
Arguing "By Definition"

Front: Arguing "by definition" is right up there with "Hitler", "[...]", "absolutely certain" and "can't prove that".
Back: Arguing "by definition" is right up there with "Hitler", "God", "absolutely certain" and "can't prove that".
Arguing "By Definition"

Front: Arguing "by definition" is right up there with "Hitler", "God", "[...]" and "can't prove that".
Back: Arguing "by definition" is right up there with "Hitler", "God", "absolutely certain" and "can't prove that".
Arguing "By Definition"

Front: Arguing "by definition" is right up there with "Hitler", "God", "absolutely certain" and "[...]".
Back: Arguing "by definition" is right up there with "Hitler", "God", "absolutely certain" and "can't prove that".
Arguing "By Definition"

Front: is the heuristic of the failure of "by definition" perfect?
Back: no
Arguing "By Definition"

Front: are you better off just deleting the phrase "by definition" from your vocabulary?
Back: probably yes
Arguing "By Definition"

Front: when do you know to always delete the phrase "by definition"?
Back: where you might be tempted to say it in italics or followed with an exclamation mark
Arguing "By Definition"

Front: classify: "If I had a hammer that seemed to me to work really well, but no one was willing to pay me the going rate for hammers of that quality"
.. then by definition you have mis-estimated the going rate.
Back: correct use of "by definition"
Arguing "By Definition"

Front: Frank, I think I would say, not so much that [...], but that we should sharply distinguish between communication problems and single-player inference problems.
Back: Frank, I think I would say, not so much that definitions are useless, but that we should sharply distinguish between communication problems and single-player inference problems.
Arguing "By Definition"

Front: Frank, I think I would say, not so much that definitions are useless, but that we should sharply distinguish between [...] and single-player inference problems.
Back: Frank, I think I would say, not so much that definitions are useless, but that we should sharply distinguish between communication problems and single-player inference problems.
Arguing "By Definition"

Front: Frank, I think I would say, not so much that definitions are useless, but that we should sharply distinguish between communication problems and [...].
Back: Frank, I think I would say, not so much that definitions are useless, but that we should sharply distinguish between communication problems and single-player inference problems.
Arguing "By Definition"

Front: When people start [...] while they (a) understand what each other are trying to say and (b) are trying to do an inference that they could theoretically do as single players, something has gone wrong.
Back: When people start violently arguing over their communication signals while they (a) understand what each other are trying to say and (b) are trying to do an inference that they could theoretically do as single players, something has gone wrong.
Arguing "By Definition"

Front: When people start violently arguing over their communication signals while they (a) [...] and (b) are trying to do an inference that they could theoretically do as single players, something has gone wrong.
Back: When people start violently arguing over their communication signals while they (a) understand what each other are trying to say and (b) are trying to do an inference that they could theoretically do as single players, something has gone wrong.
Arguing "By Definition"

Front: When people start violently arguing over their communication signals while they (a) understand what each other are trying to say and (b) [...], something has gone wrong.
Back: When people start violently arguing over their communication signals while they (a) understand what each other are trying to say and (b) are trying to do an inference that they could theoretically do as single players, something has gone wrong.
Arguing "By Definition"

Front: When people start violently arguing over their communication signals while they (a) understand what each other are trying to say and (b) are trying to do an inference that they could theoretically do as single players, [...].
Back: When people start violently arguing over their communication signals while they (a) understand what each other are trying to say and (b) are trying to do an inference that they could theoretically do as single players, something has gone wrong.
Arguing "By Definition"

Front: 27. what's wrong with: You define "human" as a "featherless biped", and point to Socrates and say, "No feathers - two legs - he must be human!"  But what you really care about is something else, like mortality.
Back: 27. You claim "X, by definition, is a Y!"  On such occasions you're almost certainly trying to sneak in a connotation of Y that wasn't in your given definition.
37 Ways That Words Can Be Wrong

Front: 27. followup point: You define "human" as a "featherless biped", and point to Socrates and say, "No feathers - two legs - he must be human!"  But what you really care about is something else, like mortality.
Back: 27. If what was in dispute was Socrates's number of legs, the other fellow would just reply, "Whaddaya mean, Socrates's got two legs?  That's what we're arguing about in the first place!"
37 Ways That Words Can Be Wrong

Front: 28. what's wrong with: If you see Socrates out in the field with some biologists, gathering herbs that might confer resistance to hemlock, there's no point in arguing "Men, by definition, are mortal!"
Back: 28. You claim "Ps, by definition, are Qs!"
37 Ways That Words Can Be Wrong

Front: 28. followup point: If you see Socrates out in the field with some biologists, gathering herbs that might confer resistance to hemlock, there's no point in arguing "Men, by definition, are mortal!"
Back: 28. The main time you feel the need to tighten the vise by insisting that something is true "by definition" is when there's other information that calls the default inference into doubt.
37 Ways That Words Can Be Wrong

Front: 29. what's wrong with: You wouldn't feel the need to say, "Hinduism, by definition, is a religion!" because, well, of course Hinduism is a religion.
Back: 29. You try to establish membership in an empirical cluster "by definition".
37 Ways That Words Can Be Wrong

Front: 29. followup point: You wouldn't feel the need to say, "Hinduism, by definition, is a religion!" because, well, of course Hinduism is a religion. It's not just a religion "by definition", it's, like, an actual religion.
Back: 29. Atheism does not resemble the central members of the "religion" cluster, so if it wasn't for the fact that atheism is a religion by definition, you might go around thinking that atheism wasn't a religion.  That's why you've got to crush all opposition by pointing out that "Atheism is a religion" is true by definition, because it isn't true any other way.
37 Ways That Words Can Be Wrong

Front: Just because [...] "art" doesn't mean that it has a meaning, floating out there in the void, which you can discover by finding the right definition.
Back: Just because there's a word "art" doesn't mean that it has a meaning, floating out there in the void, which you can discover by finding the right definition.
Where to Draw the Boundary?

Front: Just because there's a word "art" doesn't mean that it [...], floating out there in the void, which you can discover by finding the right definition.
Back: Just because there's a word "art" doesn't mean that it has a meaning, floating out there in the void, which you can discover by finding the right definition.
Where to Draw the Boundary?

Front: Just because there's a word "art" doesn't mean that it has a meaning, [...], which you can discover by finding the right definition.
Back: Just because there's a word "art" doesn't mean that it has a meaning, floating out there in the void, which you can discover by finding the right definition.
Where to Draw the Boundary?

Front: Just because there's a word "art" doesn't mean that it has a meaning, floating out there in the void, which you can [...] by finding the right definition.
Back: Just because there's a word "art" doesn't mean that it has a meaning, floating out there in the void, which you can discover by finding the right definition.
Where to Draw the Boundary?

Front: Just because there's a word "art" doesn't mean that it has a meaning, floating out there in the void, which you can discover by [...].
Back: Just because there's a word "art" doesn't mean that it has a meaning, floating out there in the void, which you can discover by finding the right definition.
Where to Draw the Boundary?

Front: [...] define a word means you're looking at the problem the wrong way - searching for the mysterious essence of what is, in fact, a communication signal.
Back: Wondering how to define a word means you're looking at the problem the wrong way - searching for the mysterious essence of what is, in fact, a communication signal.
Where to Draw the Boundary?

Front: Wondering how to [...] means you're looking at the problem the wrong way - searching for the mysterious essence of what is, in fact, a communication signal.
Back: Wondering how to define a word means you're looking at the problem the wrong way - searching for the mysterious essence of what is, in fact, a communication signal.
Where to Draw the Boundary?

Front: Wondering how to define a word means you're [...] - searching for the mysterious essence of what is, in fact, a communication signal.
Back: Wondering how to define a word means you're looking at the problem the wrong way - searching for the mysterious essence of what is, in fact, a communication signal.
Where to Draw the Boundary?

Front: Wondering how to define a word means you're looking at the problem the wrong way - searching for the [...] of what is, in fact, a communication signal.
Back: Wondering how to define a word means you're looking at the problem the wrong way - searching for the mysterious essence of what is, in fact, a communication signal.
Where to Draw the Boundary?

Front: Wondering how to define a word means you're looking at the problem the wrong way - searching for the mysterious essence of what is, in fact, a [...].
Back: Wondering how to define a word means you're looking at the problem the wrong way - searching for the mysterious essence of what is, in fact, a communication signal.
Where to Draw the Boundary?

Front: is finding a satisfactory definition of a word a real challenge that a rationalist can attack?
Back: no
Where to Draw the Boundary?

Front: instead of finding a satisfactory definition of a word, in what manner does rationalist attack the real challenge?
Back: as a single-player game, without speaking aloud
Where to Draw the Boundary?

Front: The challenge is [...] which things are similar to each other - which things are clustered together - and sometimes, which things have a common cause.
Back: The challenge is figuring out which things are similar to each other - which things are clustered together - and sometimes, which things have a common cause.
Where to Draw the Boundary?

Front: The challenge is figuring out which things are [...] - which things are clustered together - and sometimes, which things have a common cause.
Back: The challenge is figuring out which things are similar to each other - which things are clustered together - and sometimes, which things have a common cause.
Where to Draw the Boundary?

Front: The challenge is figuring out which things are similar to each other - which things are [...] - and sometimes, which things have a common cause.
Back: The challenge is figuring out which things are similar to each other - which things are clustered together - and sometimes, which things have a common cause.
Where to Draw the Boundary?

Front: The challenge is figuring out which things are similar to each other - which things are clustered together - and sometimes, which things [...].
Back: The challenge is figuring out which things are similar to each other - which things are clustered together - and sometimes, which things have a common cause.
Where to Draw the Boundary?

Front: If you define "eluctromugnetism" to include [...], include compasses, exclude light, and include Mesmer's "animal magnetism" (what we now call hypnosis), then you will have some trouble asking "How does electromugnetism work?"
Back: If you define "eluctromugnetism" to include lightning, include compasses, exclude light, and include Mesmer's "animal magnetism" (what we now call hypnosis), then you will have some trouble asking "How does electromugnetism work?"
Where to Draw the Boundary?

Front: If you define "eluctromugnetism" to include lightning, include [...], exclude light, and include Mesmer's "animal magnetism" (what we now call hypnosis), then you will have some trouble asking "How does electromugnetism work?"
Back: If you define "eluctromugnetism" to include lightning, include compasses, exclude light, and include Mesmer's "animal magnetism" (what we now call hypnosis), then you will have some trouble asking "How does electromugnetism work?"
Where to Draw the Boundary?

Front: If you define "eluctromugnetism" to include lightning, include compasses, exclude [...], and include Mesmer's "animal magnetism" (what we now call hypnosis), then you will have some trouble asking "How does electromugnetism work?"
Back: If you define "eluctromugnetism" to include lightning, include compasses, exclude light, and include Mesmer's "animal magnetism" (what we now call hypnosis), then you will have some trouble asking "How does electromugnetism work?"
Where to Draw the Boundary?

Front: If you define "eluctromugnetism" to include lightning, include compasses, exclude light, and include [...], then you will have some trouble asking "How does electromugnetism work?"
Back: If you define "eluctromugnetism" to include lightning, include compasses, exclude light, and include Mesmer's "animal magnetism" (what we now call hypnosis), then you will have some trouble asking "How does electromugnetism work?"
Where to Draw the Boundary?

Front: If you define "eluctromugnetism" to include lightning, include compasses, exclude light, and include Mesmer's "animal magnetism" (what we now call hypnosis), then you will have [...] asking "How does electromugnetism work?"
Back: If you define "eluctromugnetism" to include lightning, include compasses, exclude light, and include Mesmer's "animal magnetism" (what we now call hypnosis), then you will have some trouble asking "How does electromugnetism work?"
Where to Draw the Boundary?

Front: If you define "eluctromugnetism" to include lightning, include compasses, exclude light, and include Mesmer's "animal magnetism" (what we now call hypnosis), then you will have some trouble asking "[...]?"
Back: If you define "eluctromugnetism" to include lightning, include compasses, exclude light, and include Mesmer's "animal magnetism" (what we now call hypnosis), then you will have some trouble asking "How does electromugnetism work?"
Where to Draw the Boundary?

Front: We could say that [...] is a wrong word, a boundary in thingspace that loops around and swerves through the clusters, a cut that fails to carve reality along its natural joints.
Back: We could say that electromugnetism is a wrong word, a boundary in thingspace that loops around and swerves through the clusters, a cut that fails to carve reality along its natural joints.
Where to Draw the Boundary?

Front: We could say that electromugnetism is a [...], a boundary in thingspace that loops around and swerves through the clusters, a cut that fails to carve reality along its natural joints.
Back: We could say that electromugnetism is a wrong word, a boundary in thingspace that loops around and swerves through the clusters, a cut that fails to carve reality along its natural joints.
Where to Draw the Boundary?

Front: We could say that electromugnetism is a wrong word, a [...] that loops around and swerves through the clusters, a cut that fails to carve reality along its natural joints.
Back: We could say that electromugnetism is a wrong word, a boundary in thingspace that loops around and swerves through the clusters, a cut that fails to carve reality along its natural joints.
Where to Draw the Boundary?

Front: We could say that electromugnetism is a wrong word, a boundary in thingspace that [...] through the clusters, a cut that fails to carve reality along its natural joints.
Back: We could say that electromugnetism is a wrong word, a boundary in thingspace that loops around and swerves through the clusters, a cut that fails to carve reality along its natural joints.
Where to Draw the Boundary?

Front: We could say that electromugnetism is a wrong word, a boundary in thingspace that loops around and swerves through the [...], a cut that fails to carve reality along its natural joints.
Back: We could say that electromugnetism is a wrong word, a boundary in thingspace that loops around and swerves through the clusters, a cut that fails to carve reality along its natural joints.
Where to Draw the Boundary?

Front: We could say that electromugnetism is a wrong word, a boundary in thingspace that loops around and swerves through the clusters, a [...] that fails to carve reality along its natural joints.
Back: We could say that electromugnetism is a wrong word, a boundary in thingspace that loops around and swerves through the clusters, a cut that fails to carve reality along its natural joints.
Where to Draw the Boundary?

Front: We could say that electromugnetism is a wrong word, a boundary in thingspace that loops around and swerves through the clusters, a cut that [...] along its natural joints.
Back: We could say that electromugnetism is a wrong word, a boundary in thingspace that loops around and swerves through the clusters, a cut that fails to carve reality along its natural joints.
Where to Draw the Boundary?

Front: We could say that electromugnetism is a wrong word, a boundary in thingspace that loops around and swerves through the clusters, a cut that fails to carve reality along its [...].
Back: We could say that electromugnetism is a wrong word, a boundary in thingspace that loops around and swerves through the clusters, a cut that fails to carve reality along its natural joints.
Where to Draw the Boundary?

Front: Figuring [...] in order to carve along the joints - this is the problem worthy of a rationalist.
Back: Figuring where to cut reality in order to carve along the joints - this is the problem worthy of a rationalist.
Where to Draw the Boundary?

Front: Figuring where to cut reality in order to [...] - this is the problem worthy of a rationalist.
Back: Figuring where to cut reality in order to carve along the joints - this is the problem worthy of a rationalist.
Where to Draw the Boundary?

Front: Figuring where to cut reality in order to carve along the joints - this is the [...].
Back: Figuring where to cut reality in order to carve along the joints - this is the problem worthy of a rationalist.
Where to Draw the Boundary?

Front: what should people be trying to do, when they set out in search of the floating essence of a word?
Back: figure out where to cut reality in order to carve along the joints
Where to Draw the Boundary?

Front: what sort of challenge is it to realize that you need a single word to describe breathing and fire?
Back: a scientific challenge
Where to Draw the Boundary?

Front: Or you could stop [...] and admit that dolphins don't belong on the fish list.
Back: Or you could stop playing nitwit games and admit that dolphins don't belong on the fish list.
Where to Draw the Boundary?

Front: Or you could stop playing nitwit games and [...] dolphins don't belong on the fish list.
Back: Or you could stop playing nitwit games and admit that dolphins don't belong on the fish list.
Where to Draw the Boundary?

Front: Or you could stop playing nitwit games and admit that [...] don't belong on the fish list.
Back: Or you could stop playing nitwit games and admit that dolphins don't belong on the fish list.
Where to Draw the Boundary?

Front: Or you could stop playing nitwit games and admit that dolphins don't belong on the [...] list.
Back: Or you could stop playing nitwit games and admit that dolphins don't belong on the fish list.
Where to Draw the Boundary?

Front: You [...] a comforting shield of correct-by-definition.
Back: You cannot hide behind a comforting shield of correct-by-definition.
Where to Draw the Boundary?

Front: You cannot hide behind a [...] of correct-by-definition.
Back: You cannot hide behind a comforting shield of correct-by-definition.
Where to Draw the Boundary?

Front: You cannot hide behind a comforting shield of [...].
Back: You cannot hide behind a comforting shield of correct-by-definition.
Where to Draw the Boundary?

Front: Both [...] and intensional definitions can be wrong, can fail to carve reality at the joints.
Back: Both extensional definitions and intensional definitions can be wrong, can fail to carve reality at the joints.
Where to Draw the Boundary?

Front: Both extensional definitions and [...] can be wrong, can fail to carve reality at the joints.
Back: Both extensional definitions and intensional definitions can be wrong, can fail to carve reality at the joints.
Where to Draw the Boundary?

Front: Both extensional definitions and intensional definitions can be [...], can fail to carve reality at the joints.
Back: Both extensional definitions and intensional definitions can be wrong, can fail to carve reality at the joints.
Where to Draw the Boundary?

Front: Both extensional definitions and intensional definitions can be wrong, can [...].
Back: Both extensional definitions and intensional definitions can be wrong, can fail to carve reality at the joints.
Where to Draw the Boundary?

Front: [...] is a guessing endeavor, in which you can make mistakes; so it's wise to be able to admit, from a theoretical standpoint, that your definition-guesses can be "mistaken".
Back: Categorizing is a guessing endeavor, in which you can make mistakes; so it's wise to be able to admit, from a theoretical standpoint, that your definition-guesses can be "mistaken".
Where to Draw the Boundary?

Front: Categorizing is a [...], in which you can make mistakes; so it's wise to be able to admit, from a theoretical standpoint, that your definition-guesses can be "mistaken".
Back: Categorizing is a guessing endeavor, in which you can make mistakes; so it's wise to be able to admit, from a theoretical standpoint, that your definition-guesses can be "mistaken".
Where to Draw the Boundary?

Front: Categorizing is a guessing endeavor, in which you can [...]; so it's wise to be able to admit, from a theoretical standpoint, that your definition-guesses can be "mistaken".
Back: Categorizing is a guessing endeavor, in which you can make mistakes; so it's wise to be able to admit, from a theoretical standpoint, that your definition-guesses can be "mistaken".
Where to Draw the Boundary?

Front: Categorizing is a guessing endeavor, in which you can make mistakes; so it's [...] to be able to admit, from a theoretical standpoint, that your definition-guesses can be "mistaken".
Back: Categorizing is a guessing endeavor, in which you can make mistakes; so it's wise to be able to admit, from a theoretical standpoint, that your definition-guesses can be "mistaken".
Where to Draw the Boundary?

Front: Categorizing is a guessing endeavor, in which you can make mistakes; so it's wise to [...], from a theoretical standpoint, that your definition-guesses can be "mistaken".
Back: Categorizing is a guessing endeavor, in which you can make mistakes; so it's wise to be able to admit, from a theoretical standpoint, that your definition-guesses can be "mistaken".
Where to Draw the Boundary?

Front: Categorizing is a guessing endeavor, in which you can make mistakes; so it's wise to be able to admit, from a [...] standpoint, that your definition-guesses can be "mistaken".
Back: Categorizing is a guessing endeavor, in which you can make mistakes; so it's wise to be able to admit, from a theoretical standpoint, that your definition-guesses can be "mistaken".
Where to Draw the Boundary?

Front: Categorizing is a guessing endeavor, in which you can make mistakes; so it's wise to be able to admit, from a theoretical standpoint, that your [...] can be "mistaken".
Back: Categorizing is a guessing endeavor, in which you can make mistakes; so it's wise to be able to admit, from a theoretical standpoint, that your definition-guesses can be "mistaken".
Where to Draw the Boundary?

Front: Categorizing is a guessing endeavor, in which you can make mistakes; so it's wise to be able to admit, from a theoretical standpoint, that your definition-guesses can be "[...]".
Back: Categorizing is a guessing endeavor, in which you can make mistakes; so it's wise to be able to admit, from a theoretical standpoint, that your definition-guesses can be "mistaken".
Where to Draw the Boundary?

Front: 30. what's wrong with: You can claim, if you like, that you are defining the word "fish" to refer to salmon, guppies, sharks, dolphins, and trout, but not jellyfish or algae.  You can claim, if you like, that this is merely a list, and there is no way a list can be "wrong".
Back: 30. Your definition draws a boundary around things that don't really belong together.
37 Ways That Words Can Be Wrong

Front: 30. followup point: You can claim, if you like, that you are defining the word "fish" to refer to salmon, guppies, sharks, dolphins, and trout, but not jellyfish or algae.  You can claim, if you like, that this is merely a list, and there is no way a list can be "wrong".
Back: 30. Or you can stop playing nitwit games and admit that you made a mistake and that dolphins don't belong on the fish list.
37 Ways That Words Can Be Wrong

Front: what does it mean that the entropy of X is 3 bits?
Back: on average, we'll have to ask 3 yes-or-no questions to find out X's value
Entropy and Short Codes

Front: what does it mean that the entropy of Y is 1.75 bits?
Back: we can find out its value by asking 1.75 yes-or-no questions
Entropy and Short Codes

Front: if X has 8 states that are all equally likely, what is the entropy of X?
Back: 3 bits
Entropy and Short Codes

Front: suppose that the system Y has four possible states with the following probabilities:
Y1: 1/2 (50%)
Y2: 1/4 (25%)
Y3: 1/8 (12.5%)
Y4: 1/8 (12.5%)
what is the entropy of Y?
Back: 1.75 bits
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
What do you ask first?
Back: Is the first symbol 1?
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
How often is the first symbol 1?
Back: 50% of the time
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
If you ask if the first symbol is 1 and the answer is yes, how many yes-or-no questions did it take to find out Y's state?
Back: 1
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
If you ask if the first symbol is 1 and the answer is no, what do you ask?
Back: Is the second symbol 1?
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
How often will you ask if the second symbol is 1 and get the answer yes?
Back: 25% of the time
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
How often does it take 2 yes-or-no questions to discover Y's state?
Back: 25% of the time
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
If the answer is no twice in a row, what do you ask?
Back: Is the third symbol 1?
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
What happens once you ask if the third symbol is 1?
Back: you're done--either Y is in state 3 or state 4
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
How often does it take 1 yes-or-no question to discover Y's state?
Back: 50% of the time
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
How often does it take 3 yes-or-no questions to discover Y's state?
Back: 25% of the time
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
how do we calculate the entropy of Y?
Back: we multiply the probablity of each state by the number of yes-or-no questions it takes to figure out if Y is in that state and add them up
(1/2 * 1) + (1/4 * 2) + (1/8 * 3) + (1/8 * 3)
Entropy and Short Codes

Front: The [...] for the entropy of a system S is the sum, over all Si, of -p(Si)*log2(p(Si)).
Back: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)*log2(p(Si)).
Entropy and Short Codes

Front: The general formula for the [...] is the sum, over all Si, of -p(Si)*log2(p(Si)).
Back: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)*log2(p(Si)).
Entropy and Short Codes

Front: The general formula for the entropy of a system S is the [...], of -p(Si)*log2(p(Si)).
Back: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)*log2(p(Si)).
Entropy and Short Codes

Front: The general formula for the entropy of a system S is the sum, over all Si, of [...](Si)*log2(p(Si)).
Back: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)*log2(p(Si)).
Entropy and Short Codes

Front: The general formula for the entropy of a system S is the sum, over all Si, of -p([...])*log2(p(Si)).
Back: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)*log2(p(Si)).
Entropy and Short Codes

Front: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)[...]log2(p(Si)).
Back: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)*log2(p(Si)).
Entropy and Short Codes

Front: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)*[...](p(Si)).
Back: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)*log2(p(Si)).
Entropy and Short Codes

Front: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)*log2([...](Si)).
Back: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)*log2(p(Si)).
Entropy and Short Codes

Front: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)*log2(p([...])).
Back: The general formula for the entropy of a system S is the sum, over all Si, of -p(Si)*log2(p(Si)).
Entropy and Short Codes

Front: You [...] devise a perfect code for a system, but if you have to tell someone the state of arbitrarily many copies of S in a single message, you can get arbitrarily close to a perfect code.
Back: You can't always devise a perfect code for a system, but if you have to tell someone the state of arbitrarily many copies of S in a single message, you can get arbitrarily close to a perfect code.
Entropy and Short Codes

Front: You can't always devise a [...] for a system, but if you have to tell someone the state of arbitrarily many copies of S in a single message, you can get arbitrarily close to a [...].
Back: You can't always devise a perfect code for a system, but if you have to tell someone the state of arbitrarily many copies of S in a single message, you can get arbitrarily close to a perfect code.
Entropy and Short Codes

Front: You can't always devise a perfect code for a system, but if you have to tell someone the state of [...] of S in a single message, you can get arbitrarily close to a perfect code.
Back: You can't always devise a perfect code for a system, but if you have to tell someone the state of arbitrarily many copies of S in a single message, you can get arbitrarily close to a perfect code.
Entropy and Short Codes

Front: You can't always devise a perfect code for a system, but if you have to tell someone the state of arbitrarily many copies of S in a [...], you can get arbitrarily close to a perfect code.
Back: You can't always devise a perfect code for a system, but if you have to tell someone the state of arbitrarily many copies of S in a single message, you can get arbitrarily close to a perfect code.
Entropy and Short Codes

Front: You can't always devise a perfect code for a system, but if you have to tell someone the state of arbitrarily many copies of S in a single message, you can get [...] to a perfect code.
Back: You can't always devise a perfect code for a system, but if you have to tell someone the state of arbitrarily many copies of S in a single message, you can get arbitrarily close to a perfect code.
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
Now, you might ask: "Why not use the code [...] for Y4, instead of 000? Wouldn't that let us transmit messages more quickly?"
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
Now, you might ask: "Why not use the code 10 for Y4, instead of 000? Wouldn't that let us transmit messages more quickly?"
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
Now, you might ask: "Why not use the code 10 for Y4, instead of [...]? Wouldn't that let us transmit messages more quickly?"
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
Now, you might ask: "Why not use the code 10 for Y4, instead of 000? Wouldn't that let us transmit messages more quickly?"
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
Now, you might ask: "Why not use the code 10 for Y4, instead of 000? Wouldn't that let us transmit messages [...]?"
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
Now, you might ask: "Why not use the code 10 for Y4, instead of 000? Wouldn't that let us transmit messages more quickly?"
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
But if you use the code [...] for Y4 , then when someone answers "Yes" to the question "Is the first symbol 1?", you won't know yet whether the system state is Y1 (1) or Y4 (10).
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
But if you use the code 10 for Y4 , then when someone answers "Yes" to the question "Is the first symbol 1?", you won't know yet whether the system state is Y1 (1) or Y4 (10).
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
But if you use the code 10 for Y4 , then when someone [...] to the question "Is the first symbol 1?", you won't know yet whether the system state is Y1 (1) or Y4 (10).
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
But if you use the code 10 for Y4 , then when someone answers "Yes" to the question "Is the first symbol 1?", you won't know yet whether the system state is Y1 (1) or Y4 (10).
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
But if you use the code 10 for Y4 , then when someone answers "Yes" to the question "[...]?", you won't know yet whether the system state is Y1 (1) or Y4 (10).
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
But if you use the code 10 for Y4 , then when someone answers "Yes" to the question "Is the first symbol 1?", you won't know yet whether the system state is Y1 (1) or Y4 (10).
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
But if you use the code 10 for Y4 , then when someone answers "Yes" to the question "Is the first symbol 1?", you [...] whether the system state is Y1 (1) or Y4 (10).
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
But if you use the code 10 for Y4 , then when someone answers "Yes" to the question "Is the first symbol 1?", you won't know yet whether the system state is Y1 (1) or Y4 (10).
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
But if you use the code 10 for Y4 , then when someone answers "Yes" to the question "Is the first symbol 1?", you won't know yet whether the system state is [...].
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
But if you use the code 10 for Y4 , then when someone answers "Yes" to the question "Is the first symbol 1?", you won't know yet whether the system state is Y1 (1) or Y4 (10).
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
In fact, if you [...], the whole system falls apart - because if you hear "1001", you don't know if it means "Y4, followed by Y2" or "Y1, followed by Y3."
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
In fact, if you use 10 for Y4, the whole system falls apart - because if you hear "1001", you don't know if it means "Y4, followed by Y2" or "Y1, followed by Y3."
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
In fact, if you use 10 for Y4, the [...] - because if you hear "1001", you don't know if it means "Y4, followed by Y2" or "Y1, followed by Y3."
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
In fact, if you use 10 for Y4, the whole system falls apart - because if you hear "1001", you don't know if it means "Y4, followed by Y2" or "Y1, followed by Y3."
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
In fact, if you use 10 for Y4, the whole system falls apart - because if you hear "[...]", you don't know if it means "Y4, followed by Y2" or "Y1, followed by Y3."
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
In fact, if you use 10 for Y4, the whole system falls apart - because if you hear "1001", you don't know if it means "Y4, followed by Y2" or "Y1, followed by Y3."
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
In fact, if you use 10 for Y4, the whole system falls apart - because if you hear "1001", you don't know if it means "[...]" or "Y1, followed by Y3."
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
In fact, if you use 10 for Y4, the whole system falls apart - because if you hear "1001", you don't know if it means "Y4, followed by Y2" or "Y1, followed by Y3."
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
In fact, if you use 10 for Y4, the whole system falls apart - because if you hear "1001", you don't know if it means "Y4, followed by Y2" or "[...]."
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
In fact, if you use 10 for Y4, the whole system falls apart - because if you hear "1001", you don't know if it means "Y4, followed by Y2" or "Y1, followed by Y3."
Entropy and Short Codes

Front: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
In fact, if you use 10 for Y4, the whole system falls apart - because if you hear "1001", you [...] "Y4, followed by Y2" or "Y1, followed by Y3."
Back: Imagine that we designate the states of Y using the following code:
Y1: 1 (50% probability)
Y2: 01 (25% probability)
Y3: 001 (12.5% probability)
Y4: 000 (12.5% probability)
In fact, if you use 10 for Y4, the whole system falls apart - because if you hear "1001", you don't know if it means "Y4, followed by Y2" or "Y1, followed by Y3."
Entropy and Short Codes

Front: what is the moral of the story about why you can't change Y4 to be 10?
Back: short words are a conserved resource
Entropy and Short Codes

Front: The [...] to creating a good code - a code that transmits messages as compactly as possible - is to reserve short words for things that you'll need to say frequently, and use longer words for things that you won't need to say as often.
Back: The key to creating a good code - a code that transmits messages as compactly as possible - is to reserve short words for things that you'll need to say frequently, and use longer words for things that you won't need to say as often.
Entropy and Short Codes

Front: The key to creating a [...] - a code that transmits messages as compactly as possible - is to reserve short words for things that you'll need to say frequently, and use longer words for things that you won't need to say as often.
Back: The key to creating a good code - a code that transmits messages as compactly as possible - is to reserve short words for things that you'll need to say frequently, and use longer words for things that you won't need to say as often.
Entropy and Short Codes

Front: The key to creating a good code - a code that [...] - is to reserve short words for things that you'll need to say frequently, and use longer words for things that you won't need to say as often.
Back: The key to creating a good code - a code that transmits messages as compactly as possible - is to reserve short words for things that you'll need to say frequently, and use longer words for things that you won't need to say as often.
Entropy and Short Codes

Front: The key to creating a good code - a code that transmits messages as compactly as possible - is to [...] for things that you'll need to say frequently, and use longer words for things that you won't need to say as often.
Back: The key to creating a good code - a code that transmits messages as compactly as possible - is to reserve short words for things that you'll need to say frequently, and use longer words for things that you won't need to say as often.
Entropy and Short Codes

Front: The key to creating a good code - a code that transmits messages as compactly as possible - is to reserve short words for things that you'll need to [...], and use longer words for things that you won't need to say as often.
Back: The key to creating a good code - a code that transmits messages as compactly as possible - is to reserve short words for things that you'll need to say frequently, and use longer words for things that you won't need to say as often.
Entropy and Short Codes

Front: The key to creating a good code - a code that transmits messages as compactly as possible - is to reserve short words for things that you'll need to say frequently, and [...] for things that you won't need to say as often.
Back: The key to creating a good code - a code that transmits messages as compactly as possible - is to reserve short words for things that you'll need to say frequently, and use longer words for things that you won't need to say as often.
Entropy and Short Codes

Front: The key to creating a good code - a code that transmits messages as compactly as possible - is to reserve short words for things that you'll need to say frequently, and use longer words for [...].
Back: The key to creating a good code - a code that transmits messages as compactly as possible - is to reserve short words for things that you'll need to say frequently, and use longer words for things that you won't need to say as often.
Entropy and Short Codes

Front: When you take [...] to its limit, the length of the message you need to describe something, corresponds exactly or almost exactly to its probability.
Back: When you take the art of creating a good code to its limit, the length of the message you need to describe something, corresponds exactly or almost exactly to its probability.
Entropy and Short Codes

Front: When you take the art of creating a good code to [...], the length of the message you need to describe something, corresponds exactly or almost exactly to its probability.
Back: When you take the art of creating a good code to its limit, the length of the message you need to describe something, corresponds exactly or almost exactly to its probability.
Entropy and Short Codes

Front: When you take the art of creating a good code to its limit, the [...] you need to describe something, corresponds exactly or almost exactly to its probability.
Back: When you take the art of creating a good code to its limit, the length of the message you need to describe something, corresponds exactly or almost exactly to its probability.
Entropy and Short Codes

Front: When you take the art of creating a good code to its limit, the length of the message you need to [...], corresponds exactly or almost exactly to its probability.
Back: When you take the art of creating a good code to its limit, the length of the message you need to describe something, corresponds exactly or almost exactly to its probability.
Entropy and Short Codes

Front: When you take the art of creating a good code to its limit, the length of the message you need to describe something, corresponds [...] to its probability.
Back: When you take the art of creating a good code to its limit, the length of the message you need to describe something, corresponds exactly or almost exactly to its probability.
Entropy and Short Codes

Front: When you take the art of creating a good code to its limit, the length of the message you need to describe something, corresponds exactly or almost exactly to [...].
Back: When you take the art of creating a good code to its limit, the length of the message you need to describe something, corresponds exactly or almost exactly to its probability.
Entropy and Short Codes

Front: what is the Minimum Description Length or Minimum Message Length formalization of Occam's Razor?
Back: the length of the message you need to describe something, corresponds exactly or almost exactly to its probability
Entropy and Short Codes

Front: what do we call it that the length of the message you need to describe something, corresponds exactly or almost exactly to its probability?
Back: the Minimum Description Length or Minimum Message Length formalization of Occam's Razor
Entropy and Short Codes

Front: "[...]; I have a right to my own opinion" obstructs the art of finding truth.
Back: "It's a free country; I have a right to my own opinion" obstructs the art of finding truth.
Entropy and Short Codes

Front: "It's a free country; [...]" obstructs the art of finding truth.
Back: "It's a free country; I have a right to my own opinion" obstructs the art of finding truth.
Entropy and Short Codes

Front: "It's a free country; I have a right to my own opinion" [...] the art of finding truth.
Back: "It's a free country; I have a right to my own opinion" obstructs the art of finding truth.
Entropy and Short Codes

Front: "It's a free country; I have a right to my own opinion" obstructs the [...].
Back: "It's a free country; I have a right to my own opinion" obstructs the art of finding truth.
Entropy and Short Codes

Front: "[...]" obstructs the art of carving reality at its joints.
Back: "I can define a word any way I like" obstructs the art of carving reality at its joints.
Entropy and Short Codes

Front: "I can define a word any way I like" [...] the art of carving reality at its joints.
Back: "I can define a word any way I like" obstructs the art of carving reality at its joints.
Entropy and Short Codes

Front: "I can define a word any way I like" obstructs the [...].
Back: "I can define a word any way I like" obstructs the art of carving reality at its joints.
Entropy and Short Codes

Front: And even the [...] "The labels we attach to words are arbitrary" obstructs awareness of compactness.
Back: And even the sensible-sounding "The labels we attach to words are arbitrary" obstructs awareness of compactness.
Entropy and Short Codes

Front: And even the sensible-sounding "[...]" obstructs awareness of compactness.
Back: And even the sensible-sounding "The labels we attach to words are arbitrary" obstructs awareness of compactness.
Entropy and Short Codes

Front: And even the sensible-sounding "The labels we attach to words are arbitrary" [...] awareness of compactness.
Back: And even the sensible-sounding "The labels we attach to words are arbitrary" obstructs awareness of compactness.
Entropy and Short Codes

Front: And even the sensible-sounding "The labels we attach to words are arbitrary" obstructs [...].
Back: And even the sensible-sounding "The labels we attach to words are arbitrary" obstructs awareness of compactness.
Entropy and Short Codes

Front: The [...] of words also plays a nontrivial role in the cognitive science of language.
Back: The length of words also plays a nontrivial role in the cognitive science of language.
Entropy and Short Codes

Front: The length of words also plays a [...] role in the cognitive science of language.
Back: The length of words also plays a nontrivial role in the cognitive science of language.
Entropy and Short Codes

Front: The length of words also plays a nontrivial role in the [...] of language.
Back: The length of words also plays a nontrivial role in the cognitive science of language.
Entropy and Short Codes

Front: The length of words also plays a nontrivial role in the cognitive science of [...].
Back: The length of words also plays a nontrivial role in the cognitive science of language.
Entropy and Short Codes

Front: In the terminology of [...], "chair" is a basic-level category.
Back: In the terminology of cognitive psychology, "chair" is a basic-level category.
Entropy and Short Codes

Front: In the terminology of cognitive psychology, "[...]" is a basic-level category.
Back: In the terminology of cognitive psychology, "chair" is a basic-level category.
Entropy and Short Codes

Front: In the terminology of cognitive psychology, "chair" is a [...] category.
Back: In the terminology of cognitive psychology, "chair" is a basic-level category.
Entropy and Short Codes

Front: People have a [...] to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Back: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Entropy and Short Codes

Front: People have a tendency to [...], and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Back: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Entropy and Short Codes

Front: People have a tendency to talk, and presumably [...], at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Back: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Entropy and Short Codes

Front: People have a tendency to talk, and presumably think, at the [...] - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Back: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Entropy and Short Codes

Front: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "[...]", rather than around the more specific category "recliner", or the more general category "furniture".
Back: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Entropy and Short Codes

Front: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the [...] "recliner", or the more general category "furniture".
Back: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Entropy and Short Codes

Front: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "[...]", or the more general category "furniture".
Back: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Entropy and Short Codes

Front: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more [...] "furniture".
Back: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Entropy and Short Codes

Front: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "[...]".
Back: People have a tendency to talk, and presumably think, at the basic level of categorization - to draw the boundary around "chairs", rather than around the more specific category "recliner", or the more general category "furniture".
Entropy and Short Codes

Front: People are [...] "You can sit in that chair" than "You can sit in that recliner" or "You can sit in that furniture".
Back: People are more likely to say "You can sit in that chair" than "You can sit in that recliner" or "You can sit in that furniture".
Entropy and Short Codes

Front: People are more likely to say "You can sit in that [...]" than "You can sit in that recliner" or "You can sit in that furniture".
Back: People are more likely to say "You can sit in that chair" than "You can sit in that recliner" or "You can sit in that furniture".
Entropy and Short Codes

Front: People are more likely to say "You can sit in that chair" than "You can sit in that [...]" or "You can sit in that furniture".
Back: People are more likely to say "You can sit in that chair" than "You can sit in that recliner" or "You can sit in that furniture".
Entropy and Short Codes

Front: People are more likely to say "You can sit in that chair" than "You can sit in that recliner" or "You can sit in that [...]".
Back: People are more likely to say "You can sit in that chair" than "You can sit in that recliner" or "You can sit in that furniture".
Entropy and Short Codes

Front: what sort of names do basic-level categories tend to have?
Back: short names
Entropy and Short Codes

Front: what do nouns with short names tend to refer to?
Back: basic-level categories
Entropy and Short Codes

Front: why is the correlation between basic-level category nouns and short names a perfect rule?
Back: it isn't
Entropy and Short Codes

Front: what is the correlation between basic-level category nouns and short names?
Back: a definite tendency
Entropy and Short Codes

Front: what does frequent use tend to go along with?
Back: short words
Entropy and Short Codes

Front: what do short words tend to go along with? (not just nouns)
Back: frequent use
Entropy and Short Codes

Front: 31. what's wrong with: Which sounds more plausible, "God did a miracle" or "A supernatural universe-creating entity temporarily suspended the laws of physics"?
Back: 31. You use a short word for something that you won't need to describe often, or a long word for something you'll need to describe often.
37 Ways That Words Can Be Wrong

Front: 31. followup point: You use a short word for something that you won't need to describe often, or a long word for something you'll need to describe often.
Back: 31. This can result in inefficient thinking, or even misapplications of Occam's Razor, if your mind thinks that short sentences sound "simpler".
37 Ways That Words Can Be Wrong

Front: The [...] of two variables is defined as the difference between the entropy of the joint system and the entropy of the independent systems: I(X;Y) = H(X) + H(Y) - H(X,Y).
Back: The mutual information of two variables is defined as the difference between the entropy of the joint system and the entropy of the independent systems: I(X;Y) = H(X) + H(Y) - H(X,Y).
Mutual Information and Density in Thingspace

Front: The mutual information of [...] is defined as the difference between the entropy of the joint system and the entropy of the independent systems: I(X;Y) = H(X) + H(Y) - H(X,Y).
Back: The mutual information of two variables is defined as the difference between the entropy of the joint system and the entropy of the independent systems: I(X;Y) = H(X) + H(Y) - H(X,Y).
Mutual Information and Density in Thingspace

Front: The mutual information of two variables is defined as [...] the entropy of the joint system and the entropy of the independent systems: I(X;Y) = H(X) + H(Y) - H(X,Y).
Back: The mutual information of two variables is defined as the difference between the entropy of the joint system and the entropy of the independent systems: I(X;Y) = H(X) + H(Y) - H(X,Y).
Mutual Information and Density in Thingspace

Front: The mutual information of two variables is defined as the difference between [...] and the entropy of the independent systems: I(X;Y) = H(X) + H(Y) - H(X,Y).
Back: The mutual information of two variables is defined as the difference between the entropy of the joint system and the entropy of the independent systems: I(X;Y) = H(X) + H(Y) - H(X,Y).
Mutual Information and Density in Thingspace

Front: The mutual information of two variables is defined as the difference between the entropy of the joint system and [...]: I(X;Y) = H(X) + H(Y) - H(X,Y).
Back: The mutual information of two variables is defined as the difference between the entropy of the joint system and the entropy of the independent systems: I(X;Y) = H(X) + H(Y) - H(X,Y).
Mutual Information and Density in Thingspace

Front: The mutual information of two variables is defined as the difference between the entropy of the joint system and the entropy of the independent systems: [...] = H(X) + H(Y) - H(X,Y).
Back: The mutual information of two variables is defined as the difference between the entropy of the joint system and the entropy of the independent systems: I(X;Y) = H(X) + H(Y) - H(X,Y).
Mutual Information and Density in Thingspace

Front: The mutual information of two variables is defined as the difference between the entropy of the joint system and the entropy of the independent systems: I(X;Y) = [...].
Back: The mutual information of two variables is defined as the difference between the entropy of the joint system and the entropy of the independent systems: I(X;Y) = H(X) + H(Y) - H(X,Y).
Mutual Information and Density in Thingspace

Front: So, just by [...], we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution factors into the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Back: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution factors into the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Mutual Information and Density in Thingspace

Front: So, just by inspecting the joint distribution, we can determine whether the [...] Y and Z are independent; that is, whether the joint distribution factors into the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Back: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution factors into the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Mutual Information and Density in Thingspace

Front: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are [...]; that is, whether the joint distribution factors into the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Back: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution factors into the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Mutual Information and Density in Thingspace

Front: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the [...] factors into the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Back: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution factors into the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Mutual Information and Density in Thingspace

Front: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution [...] the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Back: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution factors into the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Mutual Information and Density in Thingspace

Front: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution factors into [...]; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Back: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution factors into the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Mutual Information and Density in Thingspace

Front: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution factors into the product of the marginal distributions; whether, [...], P(Y,Z) = P(Y)P(Z).
Back: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution factors into the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Mutual Information and Density in Thingspace

Front: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution factors into the product of the marginal distributions; whether, for all Y and Z, [...].
Back: So, just by inspecting the joint distribution, we can determine whether the marginal variables Y and Z are independent; that is, whether the joint distribution factors into the product of the marginal distributions; whether, for all Y and Z, P(Y,Z) = P(Y)P(Z).
Mutual Information and Density in Thingspace

Front: So when [...] (description of the math) - when P(Y,Z) = P(Y)P(Z) - this is equivalent to "Learning about Y never tells us anything about Z or vice versa."
Back: So when the distribution factorizes - when P(Y,Z) = P(Y)P(Z) - this is equivalent to "Learning about Y never tells us anything about Z or vice versa."
Mutual Information and Density in Thingspace

Front: So when the distribution factorizes - when [...] - this is equivalent to "Learning about Y never tells us anything about Z or vice versa."
Back: So when the distribution factorizes - when P(Y,Z) = P(Y)P(Z) - this is equivalent to "Learning about Y never tells us anything about Z or vice versa."
Mutual Information and Density in Thingspace

Front: So when the distribution factorizes - when P(Y,Z) = P(Y)P(Z) - this is equivalent to "[...]"
Back: So when the distribution factorizes - when P(Y,Z) = P(Y)P(Z) - this is equivalent to "Learning about Y never tells us anything about Z or vice versa."
Mutual Information and Density in Thingspace

Front: Where there is no [...], there is no Bayesian evidence, and vice versa.
Back: Where there is no mutual information, there is no Bayesian evidence, and vice versa.
Mutual Information and Density in Thingspace

Front: Where there is no mutual information, there is no [...], and vice versa.
Back: Where there is no mutual information, there is no Bayesian evidence, and vice versa.
Mutual Information and Density in Thingspace

Front: Where there is no mutual information, there is no Bayesian evidence, and [...].
Back: Where there is no mutual information, there is no Bayesian evidence, and vice versa.
Mutual Information and Density in Thingspace

Front: The [...] do not always equal the product of the marginal probabilities.
Back: The joint probabilities do not always equal the product of the marginal probabilities.
Mutual Information and Density in Thingspace

Front: But the joint probabilities [...] the product of the marginal probabilities.
Back: But the joint probabilities do not always equal the product of the marginal probabilities.
Mutual Information and Density in Thingspace

Front: But the joint probabilities do not always equal the [...].
Back: But the joint probabilities do not always equal the product of the marginal probabilities.
Mutual Information and Density in Thingspace

Front: Since there's an "[...]" probability for P(Z1Y2) - defined as a probability higher than the marginal probabilities would indicate by default - it follows that observing Y2 is evidence which increases the probability of Z1.
Back: Since there's an "unusually high" probability for P(Z1Y2) - defined as a probability higher than the marginal probabilities would indicate by default - it follows that observing Y2 is evidence which increases the probability of Z1.
Mutual Information and Density in Thingspace

Front: Since there's an "unusually high" probability for P(Z1Y2) - defined as [...] - it follows that observing Y2 is evidence which increases the probability of Z1. (math in words not an equation)
Back: Since there's an "unusually high" probability for P(Z1Y2) - defined as a probability higher than the marginal probabilities would indicate by default - it follows that observing Y2 is evidence which increases the probability of Z1.
Mutual Information and Density in Thingspace

Front: Since there's an "unusually high" probability for P(Z1Y2) - defined as a probability higher than the marginal probabilities would indicate by default - it follows that [...] is evidence which increases the probability of Z1.
Back: Since there's an "unusually high" probability for P(Z1Y2) - defined as a probability higher than the marginal probabilities would indicate by default - it follows that observing Y2 is evidence which increases the probability of Z1.
Mutual Information and Density in Thingspace

Front: Since there's an "unusually high" probability for P(Z1Y2) - defined as a probability higher than the marginal probabilities would indicate by default - it follows that observing Y2 is [...] which increases the probability of Z1.
Back: Since there's an "unusually high" probability for P(Z1Y2) - defined as a probability higher than the marginal probabilities would indicate by default - it follows that observing Y2 is evidence which increases the probability of Z1.
Mutual Information and Density in Thingspace

Front: Since there's an "unusually high" probability for P(Z1Y2) - defined as a probability higher than the marginal probabilities would indicate by default - it follows that observing Y2 is evidence which [...].
Back: Since there's an "unusually high" probability for P(Z1Y2) - defined as a probability higher than the marginal probabilities would indicate by default - it follows that observing Y2 is evidence which increases the probability of Z1.
Mutual Information and Density in Thingspace

Front: As there are [...] of Y that tell us about Z (and vice versa) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields less total uncertainty than the sum of the independent entropies of Y and Z.
Back: As there are at least some values of Y that tell us about Z (and vice versa) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields less total uncertainty than the sum of the independent entropies of Y and Z.
Mutual Information and Density in Thingspace

Front: As there are at least some values of Y that [...] Z (and vice versa) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields less total uncertainty than the sum of the independent entropies of Y and Z.
Back: As there are at least some values of Y that tell us about Z (and vice versa) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields less total uncertainty than the sum of the independent entropies of Y and Z.
Mutual Information and Density in Thingspace

Front: As there are at least some values of Y that tell us about Z ([...]) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields less total uncertainty than the sum of the independent entropies of Y and Z.
Back: As there are at least some values of Y that tell us about Z (and vice versa) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields less total uncertainty than the sum of the independent entropies of Y and Z.
Mutual Information and Density in Thingspace

Front: As there are at least some values of Y that tell us about Z (and vice versa) there must be [...] between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields less total uncertainty than the sum of the independent entropies of Y and Z.
Back: As there are at least some values of Y that tell us about Z (and vice versa) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields less total uncertainty than the sum of the independent entropies of Y and Z.
Mutual Information and Density in Thingspace

Front: As there are at least some values of Y that tell us about Z (and vice versa) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating [...] yields less total uncertainty than the sum of the independent entropies of Y and Z.
Back: As there are at least some values of Y that tell us about Z (and vice versa) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields less total uncertainty than the sum of the independent entropies of Y and Z.
Mutual Information and Density in Thingspace

Front: As there are at least some values of Y that tell us about Z (and vice versa) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields [...] than the sum of the independent entropies of Y and Z.
Back: As there are at least some values of Y that tell us about Z (and vice versa) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields less total uncertainty than the sum of the independent entropies of Y and Z.
Mutual Information and Density in Thingspace

Front: As there are at least some values of Y that tell us about Z (and vice versa) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields less total uncertainty than the [...].
Back: As there are at least some values of Y that tell us about Z (and vice versa) there must be mutual information between the two variables; and so you will find - I am confident, though I haven't actually checked - that calculating the entropy of YZ yields less total uncertainty than the sum of the independent entropies of Y and Z.
Mutual Information and Density in Thingspace

Front: Why would you even want to have a word for "human"?  Why not just say "Socrates is a mortal featherless biped"?
Back: Because it's helpful to have shorter words for things that you encounter often.
Mutual Information and Density in Thingspace

Front: when is it efficient to have a special word for a conjunction--like "human" for "mortal featherless biped"?
Back: when things that are mortal and featherless and bipedal, are found more often than the marginal probabilities would lead you to expect
Mutual Information and Density in Thingspace

Front: In [...], word length corresponds to probability - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Back: In efficient codes, word length corresponds to probability - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Mutual Information and Density in Thingspace

Front: In efficient codes, [...] corresponds to probability - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Back: In efficient codes, word length corresponds to probability - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Mutual Information and Density in Thingspace

Front: In efficient codes, word length corresponds to [...] - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Back: In efficient codes, word length corresponds to probability - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Mutual Information and Density in Thingspace

Front: In efficient codes, word length corresponds to probability - so [...] will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Back: In efficient codes, word length corresponds to probability - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Mutual Information and Density in Thingspace

Front: In efficient codes, word length corresponds to probability - so the code for Z1Y2 will be [...] the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Back: In efficient codes, word length corresponds to probability - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Mutual Information and Density in Thingspace

Front: In efficient codes, word length corresponds to probability - so the code for Z1Y2 will be just as long as [...], unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Back: In efficient codes, word length corresponds to probability - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Mutual Information and Density in Thingspace

Front: In efficient codes, word length corresponds to probability - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless [...], in which case the code for the word can be shorter than the codes for its parts.
Back: In efficient codes, word length corresponds to probability - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Mutual Information and Density in Thingspace

Front: In efficient codes, word length corresponds to probability - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case [...].
Back: In efficient codes, word length corresponds to probability - so the code for Z1Y2 will be just as long as the code for Z1 plus the code for Y2, unless P(Z1Y2) > P(Z1)P(Y2), in which case the code for the word can be shorter than the codes for its parts.
Mutual Information and Density in Thingspace

Front: It must be [...] that featherless bipedal things will also be mortal.
Back: It must be more likely than the default that featherless bipedal things will also be mortal.
Mutual Information and Density in Thingspace

Front: It must be more likely than the default that [...] things will also be mortal.
Back: It must be more likely than the default that featherless bipedal things will also be mortal.
Mutual Information and Density in Thingspace

Front: It must be more likely than the default that featherless bipedal things will also be [...].
Back: It must be more likely than the default that featherless bipedal things will also be mortal.
Mutual Information and Density in Thingspace

Front: To replace the word "human" with [...] would require us to spend an inordinate amount of time talking.
Back: To replace the word "human" with a description of everything we know about humans would require us to spend an inordinate amount of time talking.
Mutual Information and Density in Thingspace

Front: To replace the word "human" with a description of everything we know about humans would require us to spend [...] talking.
Back: To replace the word "human" with a description of everything we know about humans would require us to spend an inordinate amount of time talking.
Mutual Information and Density in Thingspace

Front: when is having a word for thing a more compact code than just listing its properties?
Back: precisely in the case where we can infer some of those properties from the other properties
Mutual Information and Density in Thingspace

Front: So [...], is more useful than just saying "green-eyed black-haired person", precisely when:
1. Green-eyed people are more likely than average to be black-haired (and vice versa);
or
2. Wiggins share other properties that can be inferred at greater-than-default probability.
Back: So having a word "wiggin" for green-eyed black-haired people, is more useful than just saying "green-eyed black-haired person", precisely when:
1. Green-eyed people are more likely than average to be black-haired (and vice versa);
or
2. Wiggins share other properties that can be inferred at greater-than-default probability.
Mutual Information and Density in Thingspace

Front: So having a word "wiggin" for green-eyed black-haired people, is more useful than just saying "green-eyed black-haired person", precisely when:
1. [...];
or
2. Wiggins share other properties that can be inferred at greater-than-default probability.
Back: So having a word "wiggin" for green-eyed black-haired people, is more useful than just saying "green-eyed black-haired person", precisely when:
1. Green-eyed people are more likely than average to be black-haired (and vice versa);
or
2. Wiggins share other properties that can be inferred at greater-than-default probability.
Mutual Information and Density in Thingspace

Front: So having a word "wiggin" for green-eyed black-haired people, is more useful than just saying "green-eyed black-haired person", precisely when:
1. Green-eyed people are more likely than average to be black-haired (and vice versa);
or
2. [...].
Back: So having a word "wiggin" for green-eyed black-haired people, is more useful than just saying "green-eyed black-haired person", precisely when:
1. Green-eyed people are more likely than average to be black-haired (and vice versa);
or
2. Wiggins share other properties that can be inferred at greater-than-default probability.
Mutual Information and Density in Thingspace

Front: what can we do if green-eyed people are more likely than average to be black-haired (and vice versa)?
Back: probabilistically infer green eyes from black hair or vice versa
Mutual Information and Density in Thingspace

Front: what's going on with the case where wiggins share other properties that can be inferred at greater-than-default probability?
Back: In this case we have to separately observe the green eyes and black hair; but then, after observing both these properties independently, we can probabilistically infer other properties (like a taste for ketchup).
Mutual Information and Density in Thingspace

Front: how can we consider the act of defining a word?
Back: as a promise that it will describe reality more compactly
Mutual Information and Density in Thingspace

Front: Telling someone, "I define the word 'wiggin' to mean a person with green eyes and black hair", by [...], asserts that the word "wiggin" will somehow help you make inferences / shorten your messages.
Back: Telling someone, "I define the word 'wiggin' to mean a person with green eyes and black hair", by Gricean implication, asserts that the word "wiggin" will somehow help you make inferences / shorten your messages.
Mutual Information and Density in Thingspace

Front: Telling someone, "I define the word 'wiggin' to mean a person with green eyes and black hair", by Gricean implication, asserts that the word "wiggin" will somehow help you [...] / shorten your messages.
Back: Telling someone, "I define the word 'wiggin' to mean a person with green eyes and black hair", by Gricean implication, asserts that the word "wiggin" will somehow help you make inferences / shorten your messages.
Mutual Information and Density in Thingspace

Front: Telling someone, "I define the word 'wiggin' to mean a person with green eyes and black hair", by Gricean implication, asserts that the word "wiggin" will somehow help you make inferences / [...].
Back: Telling someone, "I define the word 'wiggin' to mean a person with green eyes and black hair", by Gricean implication, asserts that the word "wiggin" will somehow help you make inferences / shorten your messages.
Mutual Information and Density in Thingspace

Front: If green-eyes and black hair have [...], nor does any other property occur at greater than default probability along with them, then the word "wiggin" is a lie: The word claims that certain people are worth distinguishing as a group, but they're not.
Back: If green-eyes and black hair have no greater than default probability to be found together, nor does any other property occur at greater than default probability along with them, then the word "wiggin" is a lie: The word claims that certain people are worth distinguishing as a group, but they're not.
Mutual Information and Density in Thingspace

Front: If green-eyes and black hair have no greater than default probability to be found together, nor does [...], then the word "wiggin" is a lie: The word claims that certain people are worth distinguishing as a group, but they're not.
Back: If green-eyes and black hair have no greater than default probability to be found together, nor does any other property occur at greater than default probability along with them, then the word "wiggin" is a lie: The word claims that certain people are worth distinguishing as a group, but they're not.
Mutual Information and Density in Thingspace

Front: If green-eyes and black hair have no greater than default probability to be found together, nor does any other property occur at greater than default probability along with them, then the word "wiggin" is [...]: The word claims that certain people are worth distinguishing as a group, but they're not.
Back: If green-eyes and black hair have no greater than default probability to be found together, nor does any other property occur at greater than default probability along with them, then the word "wiggin" is a lie: The word claims that certain people are worth distinguishing as a group, but they're not.
Mutual Information and Density in Thingspace

Front: If green-eyes and black hair have no greater than default probability to be found together, nor does any other property occur at greater than default probability along with them, then the word "wiggin" is a lie: The word [...], but they're not.
Back: If green-eyes and black hair have no greater than default probability to be found together, nor does any other property occur at greater than default probability along with them, then the word "wiggin" is a lie: The word claims that certain people are worth distinguishing as a group, but they're not.
Mutual Information and Density in Thingspace

Front: If green-eyes and black hair have no greater than default probability to be found together, nor does any other property occur at greater than default probability along with them, then the word "wiggin" is a lie: The word claims that certain people are worth distinguishing as a group, but [...].
Back: If green-eyes and black hair have no greater than default probability to be found together, nor does any other property occur at greater than default probability along with them, then the word "wiggin" is a lie: The word claims that certain people are worth distinguishing as a group, but they're not.
Mutual Information and Density in Thingspace

Front: In this case the word "wiggin" does not help [...] - it is not defined by someone sending the shortest message - it has no role in the simplest explanation.
Back: In this case the word "wiggin" does not help describe reality more compactly - it is not defined by someone sending the shortest message - it has no role in the simplest explanation.
Mutual Information and Density in Thingspace

Front: In this case the word "wiggin" does not help describe reality more compactly - it is [...] by someone sending the shortest message - it has no role in the simplest explanation.
Back: In this case the word "wiggin" does not help describe reality more compactly - it is not defined by someone sending the shortest message - it has no role in the simplest explanation.
Mutual Information and Density in Thingspace

Front: In this case the word "wiggin" does not help describe reality more compactly - it is not defined by someone [...] - it has no role in the simplest explanation.
Back: In this case the word "wiggin" does not help describe reality more compactly - it is not defined by someone sending the shortest message - it has no role in the simplest explanation.
Mutual Information and Density in Thingspace

Front: In this case the word "wiggin" does not help describe reality more compactly - it is not defined by someone sending the shortest message - it has [...] in the simplest explanation.
Back: In this case the word "wiggin" does not help describe reality more compactly - it is not defined by someone sending the shortest message - it has no role in the simplest explanation.
Mutual Information and Density in Thingspace

Front: In this case the word "wiggin" does not help describe reality more compactly - it is not defined by someone sending the shortest message - it has no role in the [...].
Back: In this case the word "wiggin" does not help describe reality more compactly - it is not defined by someone sending the shortest message - it has no role in the simplest explanation.
Mutual Information and Density in Thingspace

Front: Equivalently, the word "wiggin" will be of no help to you in doing any [...].
Back: Equivalently, the word "wiggin" will be of no help to you in doing any Bayesian inference.
Mutual Information and Density in Thingspace

Front: And the way to [...], is to draw your boundaries around concentrations of unusually high probability density in Thingspace.
Back: And the way to carve reality at its joints, is to draw your boundaries around concentrations of unusually high probability density in Thingspace.
Mutual Information and Density in Thingspace

Front: And the way to carve reality at its joints, is to [...] concentrations of unusually high probability density in Thingspace.
Back: And the way to carve reality at its joints, is to draw your boundaries around concentrations of unusually high probability density in Thingspace.
Mutual Information and Density in Thingspace

Front: And the way to carve reality at its joints, is to draw your boundaries around [...] in Thingspace.
Back: And the way to carve reality at its joints, is to draw your boundaries around concentrations of unusually high probability density in Thingspace.
Mutual Information and Density in Thingspace

Front: And the way to carve reality at its joints, is to draw your boundaries around concentrations of unusually high probability density in [...].
Back: And the way to carve reality at its joints, is to draw your boundaries around concentrations of unusually high probability density in Thingspace.
Mutual Information and Density in Thingspace

Front: 32. what's wrong with: wiggin
Back: 32. You draw your boundary around a volume of space where there is no greater-than-usual density, meaning that the associated word does not correspond to any performable Bayesian inferences.
37 Ways That Words Can Be Wrong

Front: 32. followup point: wiggin
Back: 32. Since green-eyed people are not more likely to have black hair, or vice versa, and they don't share any other characteristics in common, why have a word for "wiggin"?
37 Ways That Words Can Be Wrong

Front: But as [...] as Thingspace may be, it doesn't hold a candle to the size of Conceptspace.
Back: But as vast as Thingspace may be, it doesn't hold a candle to the size of Conceptspace.
Superexponential Conceptspace and Simple Words

Front: But as vast as [...] may be, it doesn't hold a candle to the size of Conceptspace.
Back: But as vast as Thingspace may be, it doesn't hold a candle to the size of Conceptspace.
Superexponential Conceptspace and Simple Words

Front: But as vast as Thingspace may be, it [...] to the size of Conceptspace.
Back: But as vast as Thingspace may be, it doesn't hold a candle to the size of Conceptspace.
Superexponential Conceptspace and Simple Words

Front: But as vast as Thingspace may be, it doesn't hold a candle to the size of [...].
Back: But as vast as Thingspace may be, it doesn't hold a candle to the size of Conceptspace.
Superexponential Conceptspace and Simple Words

Front: "[...]", in machine learning, means a rule that includes or excludes examples.
Back: "Concept", in machine learning, means a rule that includes or excludes examples.
Superexponential Conceptspace and Simple Words

Front: "Concept", in [...], means a rule that includes or excludes examples.
Back: "Concept", in machine learning, means a rule that includes or excludes examples.
Superexponential Conceptspace and Simple Words

Front: "Concept", in machine learning, means [...].
Back: "Concept", in machine learning, means a rule that includes or excludes examples.
Superexponential Conceptspace and Simple Words

Front: If you see the data 2:+, 3:-, 14:+, 23:-, 8:+, 9:- then you might guess that the [...] was "even numbers".
Back: If you see the data 2:+, 3:-, 14:+, 23:-, 8:+, 9:- then you might guess that the concept was "even numbers".
Superexponential Conceptspace and Simple Words

Front: If you see the data 2:+, 3:-, 14:+, 23:-, 8:+, 9:- then you might guess that the concept was "[...]".
Back: If you see the data 2:+, 3:-, 14:+, 23:-, 8:+, 9:- then you might guess that the concept was "even numbers".
Superexponential Conceptspace and Simple Words

Front: [...]:
1. Maintain the set of the most general hypotheses that fit the data.
2. Maintain another set of the most specific hypotheses that fit the data.
3. Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible.
4. Each time we see a new positive example, we relax all the most specific hypotheses as little as possible.
5. We continue until we have only a single hypothesis left.
Back: the classic algorithm for generating a concept:
1. Maintain the set of the most general hypotheses that fit the data.
2. Maintain another set of the most specific hypotheses that fit the data.
3. Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible.
4. Each time we see a new positive example, we relax all the most specific hypotheses as little as possible.
5. We continue until we have only a single hypothesis left.
Superexponential Conceptspace and Simple Words

Front: the classic algorithm for generating a concept:
1. [...]
Back: the classic algorithm for generating a concept:
1. Maintain the set of the most general hypotheses that fit the data.
Superexponential Conceptspace and Simple Words

Front: the classic algorithm for generating a concept:
1. Maintain the set of the most general hypotheses that fit the data.
2. [...]
Back: the classic algorithm for generating a concept:
1. Maintain the set of the most general hypotheses that fit the data.
2. Maintain another set of the most specific hypotheses that fit the data.
Superexponential Conceptspace and Simple Words

Front: the classic algorithm for generating a concept:
1. Maintain the set of the most general hypotheses that fit the data.
2. Maintain another set of the most specific hypotheses that fit the data.
3. [...]
Back: the classic algorithm for generating a concept:
1. Maintain the set of the most general hypotheses that fit the data.
2. Maintain another set of the most specific hypotheses that fit the data.
3. Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible.
Superexponential Conceptspace and Simple Words

Front: the classic algorithm for generating a concept:
1. Maintain the set of the most general hypotheses that fit the data.
2. Maintain another set of the most specific hypotheses that fit the data.
3. Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible.
4. [...]
Back: the classic algorithm for generating a concept:
1. Maintain the set of the most general hypotheses that fit the data.
2. Maintain another set of the most specific hypotheses that fit the data.
3. Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible.
4. Each time we see a new positive example, we relax all the most specific hypotheses as little as possible.
Superexponential Conceptspace and Simple Words

Front: the classic algorithm for generating a concept:
1. Maintain the set of the most general hypotheses that fit the data.
2. Maintain another set of the most specific hypotheses that fit the data.
3. Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible.
4. Each time we see a new positive example, we relax all the most specific hypotheses as little as possible.
5. [...]
Back: the classic algorithm for generating a concept:
1. Maintain the set of the most general hypotheses that fit the data.
2. Maintain another set of the most specific hypotheses that fit the data.
3. Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible.
4. Each time we see a new positive example, we relax all the most specific hypotheses as little as possible.
5. We continue until we have only a single hypothesis left.
Superexponential Conceptspace and Simple Words

Front: Maintain the set of the [...] hypotheses that fit the data - those that positively classify as many examples as possible, while still fitting the facts.
Back: Maintain the set of the most general hypotheses that fit the data - those that positively classify as many examples as possible, while still fitting the facts.
Superexponential Conceptspace and Simple Words

Front: Maintain the set of the most general hypotheses that fit the data - those that [...], while still fitting the facts.
Back: Maintain the set of the most general hypotheses that fit the data - those that positively classify as many examples as possible, while still fitting the facts.
Superexponential Conceptspace and Simple Words

Front: Maintain the set of the most general hypotheses that fit the data - those that positively classify as many examples as possible, while [...].
Back: Maintain the set of the most general hypotheses that fit the data - those that positively classify as many examples as possible, while still fitting the facts.
Superexponential Conceptspace and Simple Words

Front: Maintain another set of the [...] that fit the data - those that negatively classify as many examples as possible, while still fitting the facts.
Back: Maintain another set of the most specific hypotheses that fit the data - those that negatively classify as many examples as possible, while still fitting the facts.
Superexponential Conceptspace and Simple Words

Front: Maintain another set of the most specific hypotheses that fit the data - those that [...], while still fitting the facts.
Back: Maintain another set of the most specific hypotheses that fit the data - those that negatively classify as many examples as possible, while still fitting the facts.
Superexponential Conceptspace and Simple Words

Front: Maintain another set of the most specific hypotheses that fit the data - those that negatively classify as many examples as possible, while [...].
Back: Maintain another set of the most specific hypotheses that fit the data - those that negatively classify as many examples as possible, while still fitting the facts.
Superexponential Conceptspace and Simple Words

Front: Each time we see a new [...] example, we strengthen all the most general hypotheses as little as possible, so that the new set is again as general as possible while fitting the facts.
Back: Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible, so that the new set is again as general as possible while fitting the facts.
Superexponential Conceptspace and Simple Words

Front: Each time we see a new negative example, we [...], so that the new set is again as general as possible while fitting the facts.
Back: Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible, so that the new set is again as general as possible while fitting the facts.
Superexponential Conceptspace and Simple Words

Front: Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible, so that the new set is [...] while fitting the facts.
Back: Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible, so that the new set is again as general as possible while fitting the facts.
Superexponential Conceptspace and Simple Words

Front: Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible, so that the new set is again as general as possible while [...].
Back: Each time we see a new negative example, we strengthen all the most general hypotheses as little as possible, so that the new set is again as general as possible while fitting the facts.
Superexponential Conceptspace and Simple Words

Front: Each time we see a new positive example, we [...], so that the new set is again as specific as possible while fitting the facts.
Back: Each time we see a new positive example, we relax all the most specific hypotheses as little as possible, so that the new set is again as specific as possible while fitting the facts.
Superexponential Conceptspace and Simple Words

Front: Each time we see a new positive example, we relax all the most specific hypotheses as little as possible, so that the new set is again [...] while fitting the facts.
Back: Each time we see a new positive example, we relax all the most specific hypotheses as little as possible, so that the new set is again as specific as possible while fitting the facts.
Superexponential Conceptspace and Simple Words

Front: Each time we see a new positive example, we relax all the most specific hypotheses as little as possible, so that the new set is again as specific as possible while [...].
Back: Each time we see a new positive example, we relax all the most specific hypotheses as little as possible, so that the new set is again as specific as possible while fitting the facts.
Superexponential Conceptspace and Simple Words

Front: We continue until we have only a single hypothesis left. This will be the [...] if the target concept was in our hypothesis space at all.
Back: We continue until we have only a single hypothesis left. This will be the answer if the target concept was in our hypothesis space at all.
Superexponential Conceptspace and Simple Words

Front: We continue until we have only a single hypothesis left. This will be the answer if the [...] was in our hypothesis space at all.
Back: We continue until we have only a single hypothesis left. This will be the answer if the target concept was in our hypothesis space at all.
Superexponential Conceptspace and Simple Words

Front: We continue until we have only a single hypothesis left. This will be the answer if the target concept was [...].
Back: We continue until we have only a single hypothesis left. This will be the answer if the target concept was in our hypothesis space at all.
Superexponential Conceptspace and Simple Words

Front: +   Sky: Sunny;  AirTemp: Warm;  Humidity: High;  Wind: Strong.
-   Sky: Rainy;  AirTemp: Cold;  Humidity: High;  Wind: Strong.
+   Sky: Sunny;  AirTemp: Warm;  Humidity: High;  Wind: Weak.
what is the set of the most general hypotheses?
Back: {?, Warm, ?, ?} and {Sunny, ?, ?, ?}
Superexponential Conceptspace and Simple Words

Front: +   Sky: Sunny;  AirTemp: Warm;  Humidity: High;  Wind: Strong.
-   Sky: Rainy;  AirTemp: Cold;  Humidity: High;  Wind: Strong.
+   Sky: Sunny;  AirTemp: Warm;  Humidity: High;  Wind: Weak.
what is the set of the most specific hypotheses?
Back: the single member {Sunny, Warm, High, ?}
Superexponential Conceptspace and Simple Words

Front: Any [...] you can find that fits the data will be strictly more specific than one of the most general hypotheses, and strictly more general than the most specific hypothesis.
Back: Any other concept you can find that fits the data will be strictly more specific than one of the most general hypotheses, and strictly more general than the most specific hypothesis.
Superexponential Conceptspace and Simple Words

Front: Any other concept you can find that [...] will be strictly more specific than one of the most general hypotheses, and strictly more general than the most specific hypothesis.
Back: Any other concept you can find that fits the data will be strictly more specific than one of the most general hypotheses, and strictly more general than the most specific hypothesis.
Superexponential Conceptspace and Simple Words

Front: Any other concept you can find that fits the data will be [...] than one of the most general hypotheses, and strictly more general than the most specific hypothesis.
Back: Any other concept you can find that fits the data will be strictly more specific than one of the most general hypotheses, and strictly more general than the most specific hypothesis.
Superexponential Conceptspace and Simple Words

Front: Any other concept you can find that fits the data will be strictly more specific than one of the [...] hypotheses, and strictly more general than the most specific hypothesis.
Back: Any other concept you can find that fits the data will be strictly more specific than one of the most general hypotheses, and strictly more general than the most specific hypothesis.
Superexponential Conceptspace and Simple Words

Front: Any other concept you can find that fits the data will be strictly more specific than one of the most general hypotheses, and [...] than the most specific hypothesis.
Back: Any other concept you can find that fits the data will be strictly more specific than one of the most general hypotheses, and strictly more general than the most specific hypothesis.
Superexponential Conceptspace and Simple Words

Front: Any other concept you can find that fits the data will be strictly more specific than one of the most general hypotheses, and strictly more general than the [...] hypothesis.
Back: Any other concept you can find that fits the data will be strictly more specific than one of the most general hypotheses, and strictly more general than the most specific hypothesis.
Superexponential Conceptspace and Simple Words

Front: Is it suspicious that there are more possible concepts than possible Days?
Back: no
Superexponential Conceptspace and Simple Words

Front: why is it surely not suspicious that there are more possible concepts that possible Days?
Back: a concept can be viewed as a collection of Days
Superexponential Conceptspace and Simple Words

Front: A [...] can be viewed as the set of days that it classifies positively, or isomorphically, the set of days that it classifies negatively.
Back: A concept can be viewed as the set of days that it classifies positively, or isomorphically, the set of days that it classifies negatively.
Superexponential Conceptspace and Simple Words

Front: A concept can be viewed as [...], or isomorphically, the set of days that it classifies negatively.
Back: A concept can be viewed as the set of days that it classifies positively, or isomorphically, the set of days that it classifies negatively.
Superexponential Conceptspace and Simple Words

Front: A concept can be viewed as the set of days that it classifies positively, or [...], the set of days that it classifies negatively.
Back: A concept can be viewed as the set of days that it classifies positively, or isomorphically, the set of days that it classifies negatively.
Superexponential Conceptspace and Simple Words

Front: A concept can be viewed as the set of days that it classifies positively, or isomorphically, [...].
Back: A concept can be viewed as the set of days that it classifies positively, or isomorphically, the set of days that it classifies negatively.
Superexponential Conceptspace and Simple Words

Front: So the space of [...] that classify Days is the set of all possible sets of Days, whose size is 2^24 = 16,777,216.
Back: So the space of all possible concepts that classify Days is the set of all possible sets of Days, whose size is 2^24 = 16,777,216.
Superexponential Conceptspace and Simple Words

Front: So the space of all possible concepts that classify Days is the set of [...] of Days, whose size is 2^24 = 16,777,216.
Back: So the space of all possible concepts that classify Days is the set of all possible sets of Days, whose size is 2^24 = 16,777,216.
Superexponential Conceptspace and Simple Words

Front: So the space of all possible concepts that classify Days is the set of all possible sets of Days, whose [...] is 2^24 = 16,777,216.
Back: So the space of all possible concepts that classify Days is the set of all possible sets of Days, whose size is 2^24 = 16,777,216.
Superexponential Conceptspace and Simple Words

Front: So the space of all possible concepts that classify Days is the set of all possible sets of Days, whose size is [...].
Back: So the space of all possible concepts that classify Days is the set of all possible sets of Days, whose size is 2^24 = 16,777,216.
Superexponential Conceptspace and Simple Words

Front: The [...] includes concepts with no compact representation, just a flat list of what is and isn't allowed.
Back: The complete space of concepts includes concepts with no compact representation, just a flat list of what is and isn't allowed.
Superexponential Conceptspace and Simple Words

Front: The complete space of concepts includes [...], just a flat list of what is and isn't allowed.
Back: The complete space of concepts includes concepts with no compact representation, just a flat list of what is and isn't allowed.
Superexponential Conceptspace and Simple Words

Front: The complete space of concepts includes concepts with no compact representation, just a [...].
Back: The complete space of concepts includes concepts with no compact representation, just a flat list of what is and isn't allowed.
Superexponential Conceptspace and Simple Words

Front: what is the problem with trying to build a "fully general" inductive learner?
Back: They can't learn concepts until they've seen every possible example in the instance space.
Superexponential Conceptspace and Simple Words

Front: with our restricted concept space, why isn't it a problem to add more attributes to Days, so the number of possible days grows exponentially in the number of attributes?
Back: because you can narrow down a large space using a logarithmic number of examples
Superexponential Conceptspace and Simple Words

Front: Even if Days had [...] binary attributes, it should still only take a manageable amount of data to narrow down the possible concepts to one.
Back: Even if Days had forty binary attributes, it should still only take a manageable amount of data to narrow down the possible concepts to one.
Superexponential Conceptspace and Simple Words

Front: Even if Days had forty [...], it should still only take a manageable amount of data to narrow down the possible concepts to one.
Back: Even if Days had forty binary attributes, it should still only take a manageable amount of data to narrow down the possible concepts to one.
Superexponential Conceptspace and Simple Words

Front: Even if Days had forty binary attributes, it should still only take [...] of data to narrow down the possible concepts to one.
Back: Even if Days had forty binary attributes, it should still only take a manageable amount of data to narrow down the possible concepts to one.
Superexponential Conceptspace and Simple Words

Front: Even if Days had forty binary attributes, it should still only take a manageable amount of data to [...].
Back: Even if Days had forty binary attributes, it should still only take a manageable amount of data to narrow down the possible concepts to one.
Superexponential Conceptspace and Simple Words

Front: The space of [...] grows superexponentially in the number of attributes.
Back: The space of all possible concepts grows superexponentially in the number of attributes.
Superexponential Conceptspace and Simple Words

Front: The space of all possible concepts grows [...] in the number of attributes.
Back: The space of all possible concepts grows superexponentially in the number of attributes.
Superexponential Conceptspace and Simple Words

Front: The space of all possible concepts grows superexponentially in the [...].
Back: The space of all possible concepts grows superexponentially in the number of attributes.
Superexponential Conceptspace and Simple Words

Front: So, here in the [...], where objects take more than 5 bytes to describe and a trillion examples are not available and there is noise in the training data, we only even think about highly regular concepts.
Back: So, here in the real world, where objects take more than 5 bytes to describe and a trillion examples are not available and there is noise in the training data, we only even think about highly regular concepts.
Superexponential Conceptspace and Simple Words

Front: So, here in the real world, where [...] and a trillion examples are not available and there is noise in the training data, we only even think about highly regular concepts.
Back: So, here in the real world, where objects take more than 5 bytes to describe and a trillion examples are not available and there is noise in the training data, we only even think about highly regular concepts.
Superexponential Conceptspace and Simple Words

Front: So, here in the real world, where objects take more than 5 bytes to describe and [...] and there is noise in the training data, we only even think about highly regular concepts.
Back: So, here in the real world, where objects take more than 5 bytes to describe and a trillion examples are not available and there is noise in the training data, we only even think about highly regular concepts.
Superexponential Conceptspace and Simple Words

Front: So, here in the real world, where objects take more than 5 bytes to describe and a trillion examples are not available and [...], we only even think about highly regular concepts.
Back: So, here in the real world, where objects take more than 5 bytes to describe and a trillion examples are not available and there is noise in the training data, we only even think about highly regular concepts.
Superexponential Conceptspace and Simple Words

Front: So, here in the real world, where objects take more than 5 bytes to describe and a trillion examples are not available and there is noise in the training data, we only even think about [...].
Back: So, here in the real world, where objects take more than 5 bytes to describe and a trillion examples are not available and there is noise in the training data, we only even think about highly regular concepts.
Superexponential Conceptspace and Simple Words

Front: From this perspective, learning doesn't just [...], it is nearly all inductive bias - when you compare the number of concepts ruled out a priori, to those ruled out by mere evidence.
Back: From this perspective, learning doesn't just rely on inductive bias, it is nearly all inductive bias - when you compare the number of concepts ruled out a priori, to those ruled out by mere evidence.
Superexponential Conceptspace and Simple Words

Front: From this perspective, learning doesn't just rely on inductive bias, it [...] - when you compare the number of concepts ruled out a priori, to those ruled out by mere evidence.
Back: From this perspective, learning doesn't just rely on inductive bias, it is nearly all inductive bias - when you compare the number of concepts ruled out a priori, to those ruled out by mere evidence.
Superexponential Conceptspace and Simple Words

Front: From this perspective, learning doesn't just rely on inductive bias, it is nearly all inductive bias - when you compare [...], to those ruled out by mere evidence.
Back: From this perspective, learning doesn't just rely on inductive bias, it is nearly all inductive bias - when you compare the number of concepts ruled out a priori, to those ruled out by mere evidence.
Superexponential Conceptspace and Simple Words

Front: From this perspective, learning doesn't just rely on inductive bias, it is nearly all inductive bias - when you compare the number of concepts ruled out a priori, to those ruled out by [...].
Back: From this perspective, learning doesn't just rely on inductive bias, it is nearly all inductive bias - when you compare the number of concepts ruled out a priori, to those ruled out by mere evidence.
Superexponential Conceptspace and Simple Words

Front: what is the whole reason that words have intensions as well as extensions?
Back: learning is nearly all inductive bias
Superexponential Conceptspace and Simple Words

Front: what is the key qualification to "The way to carve reality at its joints, is to draw boundaries around concentrations of unusually high probability density."
Back: The way to carve reality at its joints, is to draw simple boundaries around concentrations of unusually high probability density in Thingspace.
Superexponential Conceptspace and Simple Words

Front: what's the problem with leaving out the qualification about drawing simple boundaries in Thingspace?
Back: Otherwise you would just gerrymander Thingspace.
Superexponential Conceptspace and Simple Words

Front: In the real world, nothing [...] repeats itself exactly.
Back: In the real world, nothing above the level of molecules repeats itself exactly.
Superexponential Conceptspace and Simple Words

Front: In the real world, nothing above the level of molecules [...] exactly.
Back: In the real world, nothing above the level of molecules repeats itself exactly.
Superexponential Conceptspace and Simple Words

Front: In the real world, nothing above the level of molecules repeats itself [...].
Back: In the real world, nothing above the level of molecules repeats itself exactly.
Superexponential Conceptspace and Simple Words

Front: If you don't draw simple boundaries around your experiences, you can't [...].
Back: If you don't draw simple boundaries around your experiences, you can't do inference with them.
Superexponential Conceptspace and Simple Words

Front: why do you try to describe "art" with intensional definitions like "that which is intended to inspire any complex emotion for the sake of inspiring it", rather than just pointing at a long list of things that are, or aren't art?
Back: because otherwise you can't do inference
Superexponential Conceptspace and Simple Words

Front: In fact, the above statement about "[...]" is a bit chicken-and-eggish: You can't assess the density of actual observations, until you've already done at least a little carving.
Back: In fact, the above statement about "how to carve reality at its joints" is a bit chicken-and-eggish: You can't assess the density of actual observations, until you've already done at least a little carving.
Superexponential Conceptspace and Simple Words

Front: In fact, the above statement about "how to carve reality at its joints" is a bit [...]: You can't assess the density of actual observations, until you've already done at least a little carving.
Back: In fact, the above statement about "how to carve reality at its joints" is a bit chicken-and-eggish: You can't assess the density of actual observations, until you've already done at least a little carving.
Superexponential Conceptspace and Simple Words

Front: In fact, the above statement about "how to carve reality at its joints" is a bit chicken-and-eggish: You can't [...], until you've already done at least a little carving.
Back: In fact, the above statement about "how to carve reality at its joints" is a bit chicken-and-eggish: You can't assess the density of actual observations, until you've already done at least a little carving.
Superexponential Conceptspace and Simple Words

Front: In fact, the above statement about "how to carve reality at its joints" is a bit chicken-and-eggish: You can't assess the density of actual observations, until you've [...].
Back: In fact, the above statement about "how to carve reality at its joints" is a bit chicken-and-eggish: You can't assess the density of actual observations, until you've already done at least a little carving.
Superexponential Conceptspace and Simple Words

Front: And the [...] comes from drawing the boundaries, not the other way around - if you already had the [...], you'd have everything necessary for inference, so why would you bother drawing boundaries?
Back: And the probability distribution comes from drawing the boundaries, not the other way around - if you already had the probability distribution, you'd have everything necessary for inference, so why would you bother drawing boundaries?
Superexponential Conceptspace and Simple Words

Front: And the probability distribution comes from [...], not the other way around - if you already had the probability distribution, you'd have everything necessary for inference, so why would you bother [...]?
Back: And the probability distribution comes from drawing the boundaries, not the other way around - if you already had the probability distribution, you'd have everything necessary for inference, so why would you bother drawing boundaries?
Superexponential Conceptspace and Simple Words

Front: And the probability distribution comes from drawing the boundaries, not the other way around - if you [...], you'd have everything necessary for inference, so why would you bother drawing boundaries?
Back: And the probability distribution comes from drawing the boundaries, not the other way around - if you already had the probability distribution, you'd have everything necessary for inference, so why would you bother drawing boundaries?
Superexponential Conceptspace and Simple Words

Front: And the probability distribution comes from drawing the boundaries, not the other way around - if you already had the probability distribution, you'd [...], so why would you bother drawing boundaries?
Back: And the probability distribution comes from drawing the boundaries, not the other way around - if you already had the probability distribution, you'd have everything necessary for inference, so why would you bother drawing boundaries?
Superexponential Conceptspace and Simple Words

Front: And the probability distribution comes from drawing the boundaries, not the other way around - if you already had the probability distribution, you'd have everything necessary for inference, so why would you bother [...]?
Back: And the probability distribution comes from drawing the boundaries, not the other way around - if you already had the probability distribution, you'd have everything necessary for inference, so why would you bother drawing boundaries?
Superexponential Conceptspace and Simple Words

Front: When you consider the [...], it becomes clear that singling out one particular concept for consideration is an act of no small audacity - not just for us, but for any mind of bounded computing power.
Back: When you consider the superexponential size of Conceptspace, it becomes clear that singling out one particular concept for consideration is an act of no small audacity - not just for us, but for any mind of bounded computing power.
Superexponential Conceptspace and Simple Words

Front: When you consider the superexponential size of Conceptspace, it becomes clear that [...] is an act of no small audacity - not just for us, but for any mind of bounded computing power.
Back: When you consider the superexponential size of Conceptspace, it becomes clear that singling out one particular concept for consideration is an act of no small audacity - not just for us, but for any mind of bounded computing power.
Superexponential Conceptspace and Simple Words

Front: When you consider the superexponential size of Conceptspace, it becomes clear that singling out one particular concept for consideration is an act of [...] - not just for us, but for any mind of bounded computing power.
Back: When you consider the superexponential size of Conceptspace, it becomes clear that singling out one particular concept for consideration is an act of no small audacity - not just for us, but for any mind of bounded computing power.
Superexponential Conceptspace and Simple Words

Front: When you consider the superexponential size of Conceptspace, it becomes clear that singling out one particular concept for consideration is an act of no small audacity - not just for us, but for [...].
Back: When you consider the superexponential size of Conceptspace, it becomes clear that singling out one particular concept for consideration is an act of no small audacity - not just for us, but for any mind of bounded computing power.
Superexponential Conceptspace and Simple Words

Front: Presenting us with [...], without some reason for raising this particular concept to the level of our deliberate attention, is rather like a detective saying: "Well, I haven't the slightest shred of support one way or the other for who could've murdered those orphans... not even an intuition, mind you... but have we considered John Q. Wiffleheim of 1234 Norkle Rd as a suspect?"
Back: Presenting us with the word "wiggin", defined as "a black-haired green-eyed person", without some reason for raising this particular concept to the level of our deliberate attention, is rather like a detective saying: "Well, I haven't the slightest shred of support one way or the other for who could've murdered those orphans... not even an intuition, mind you... but have we considered John Q. Wiffleheim of 1234 Norkle Rd as a suspect?"
Superexponential Conceptspace and Simple Words

Front: Presenting us with the word "wiggin", defined as "a black-haired green-eyed person", without [...], is rather like a detective saying: "Well, I haven't the slightest shred of support one way or the other for who could've murdered those orphans... not even an intuition, mind you... but have we considered John Q. Wiffleheim of 1234 Norkle Rd as a suspect?"
Back: Presenting us with the word "wiggin", defined as "a black-haired green-eyed person", without some reason for raising this particular concept to the level of our deliberate attention, is rather like a detective saying: "Well, I haven't the slightest shred of support one way or the other for who could've murdered those orphans... not even an intuition, mind you... but have we considered John Q. Wiffleheim of 1234 Norkle Rd as a suspect?"
Superexponential Conceptspace and Simple Words

Front: Presenting us with the word "wiggin", defined as "a black-haired green-eyed person", without some reason for raising this particular concept to the level of our deliberate attention, is rather like a detective saying: "[...]"
Back: Presenting us with the word "wiggin", defined as "a black-haired green-eyed person", without some reason for raising this particular concept to the level of our deliberate attention, is rather like a detective saying: "Well, I haven't the slightest shred of support one way or the other for who could've murdered those orphans... not even an intuition, mind you... but have we considered John Q. Wiffleheim of 1234 Norkle Rd as a suspect?"
Superexponential Conceptspace and Simple Words

Front: 33. what's wrong with: The act of defining a word to refer to all humans, except black people, seems kind of suspicious.
Back: 33. You draw an unsimple boundary without any reason to do so.
37 Ways That Words Can Be Wrong

Front: 33. followup point: The act of defining a word to refer to all humans, except black people, seems kind of suspicious.
Back: 33. If you don't present reasons to draw that particular boundary, trying to create an "arbitrary" word in that location is like a detective saying:  "Well, I haven't the slightest shred of support one way or the other for who could've murdered those orphans... but have we considered John Q. Wiffleheim as a suspect?"
37 Ways That Words Can Be Wrong

Front: what's wrong with using this formula:
H(X,Y,Z) = H(X) + H(Y) + H(Z) - I(X;Z) - I(Z;Y) - I(X;Y)
for the entropy of X, Y, and Z?
Back: Because if you have the mutual information between X and Z, and the mutual information between Z and Y, that may include some of the same mutual information that we'll calculate exists between X and Y.
Conditional Independence, and Naive Bayes

Front: what's the mistake here?
H(X,Y,Z) = H(X) + H(Y) + H(Z) - I(X;Z) - I(Z;Y) - I(X;Y)
Back: We double-counted some of our knowledge, and so came up with too little entropy.
Conditional Independence, and Naive Bayes

Front: what's the correct version of:
H(X,Y,Z) = H(X) + H(Y) + H(Z) - I(X;Z) - I(Z;Y) - I(X;Y)
Back: H(X,Y,Z) = H(X) + H(Y) + H(Z) - I(X;Z) - I(Z;Y) - I(X;Y | Z)
Conditional Independence, and Naive Bayes

Front: what does the term I(X;Y | Z) mean?
Back: "the information that X tells us about Y, given that we already know Z"
Conditional Independence, and Naive Bayes

Front: [...] = H(X|Z) + H(Y|Z) - H(X,Y|Z)
Back: I(X;Y|Z) = H(X|Z) + H(Y|Z) - H(X,Y|Z)
Conditional Independence, and Naive Bayes

Front: I(X;Y|Z) = [...] + H(Y|Z) - H(X,Y|Z)
Back: I(X;Y|Z) = H(X|Z) + H(Y|Z) - H(X,Y|Z)
Conditional Independence, and Naive Bayes

Front: I(X;Y|Z) = H(X|Z) + [...] - H(X,Y|Z)
Back: I(X;Y|Z) = H(X|Z) + H(Y|Z) - H(X,Y|Z)
Conditional Independence, and Naive Bayes

Front: I(X;Y|Z) = H(X|Z) + H(Y|Z) - [...]
Back: I(X;Y|Z) = H(X|Z) + H(Y|Z) - H(X,Y|Z)
Conditional Independence, and Naive Bayes

Front: I(X;Y|Z) = H(X|Z) [...] H(Y|Z) - H(X,Y|Z)
Back: I(X;Y|Z) = H(X|Z) + H(Y|Z) - H(X,Y|Z)
Conditional Independence, and Naive Bayes

Front: I(X;Y|Z) = H(X|Z) + H(Y|Z) [...] H(X,Y|Z)
Back: I(X;Y|Z) = H(X|Z) + H(Y|Z) - H(X,Y|Z)
Conditional Independence, and Naive Bayes

Front: If we then [...] Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Back: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Conditional Independence, and Naive Bayes

Front: If we then learned a new fact [...], our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Back: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Conditional Independence, and Naive Bayes

Front: If we then learned a new fact Z0, our [...] about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Back: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Conditional Independence, and Naive Bayes

Front: If we then learned a new fact Z0, our remaining uncertainty [...] would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Back: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Conditional Independence, and Naive Bayes

Front: If we then learned a new fact Z0, our remaining uncertainty about S would be:
[...] = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Back: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Conditional Independence, and Naive Bayes

Front: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = [...] p(Si|Z0)*-log2(p(Si|Z0))
Back: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Conditional Independence, and Naive Bayes

Front: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: [...]*-log2(p(Si|Z0))
Back: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Conditional Independence, and Naive Bayes

Front: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)[...]-log2(p(Si|Z0))
Back: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Conditional Independence, and Naive Bayes

Front: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*[...](p(Si|Z0))
Back: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Conditional Independence, and Naive Bayes

Front: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2([...])
Back: If we then learned a new fact Z0, our remaining uncertainty about S would be:
H(S|Z0) = Sum i: p(Si|Z0)*-log2(p(Si|Z0))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact [...], but we don't know which [...] yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to [...] Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we [...], then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, [...], we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we [...] to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be [...] of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of [...] afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S [...]:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
[...] = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = [...] (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: ([...] * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) [...] Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * [...] p(Si|Zj)*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: [...]*-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)[...]-log2(p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*[...](p(Si|Zj)))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2([...]))
A: So if we're going to learn a new fact Z, but we don't know which Z yet, then, on average, we expect to be around this uncertain of S afterward:
H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
Conditional Independence, and Naive Bayes

Q: H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
And that's how one [...]; from which, in turn, we can get the conditional mutual information.
A: H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
And that's how one calculates conditional entropies; from which, in turn, we can get the conditional mutual information.
Conditional Independence, and Naive Bayes

Q: H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
And that's how one calculates conditional entropies; from which, in turn, we can get the [...].
A: H(S|Z) = Sum j: (p(Zj) * Sum i: p(Si|Zj)*-log2(p(Si|Zj)))
And that's how one calculates conditional entropies; from which, in turn, we can get the conditional mutual information.
Conditional Independence, and Naive Bayes

Q: There is a [...] between mutual information and Bayesian evidence.
A: There is a duality between mutual information and Bayesian evidence.
Conditional Independence, and Naive Bayes

Q: There is a duality between [...] and Bayesian evidence.
A: There is a duality between mutual information and Bayesian evidence.
Conditional Independence, and Naive Bayes

Q: There is a duality between mutual information and [...].
A: There is a duality between mutual information and Bayesian evidence.
Conditional Independence, and Naive Bayes

Q: [...] is positive if and only if the probability of at least some joint events P(x, y) does not equal the product of the probabilities of the separate events P(x)*P(y).
A: Mutual information is positive if and only if the probability of at least some joint events P(x, y) does not equal the product of the probabilities of the separate events P(x)*P(y).
Conditional Independence, and Naive Bayes

Q: Mutual information is [...] if and only if the probability of at least some joint events P(x, y) does not equal the product of the probabilities of the separate events P(x)*P(y).
A: Mutual information is positive if and only if the probability of at least some joint events P(x, y) does not equal the product of the probabilities of the separate events P(x)*P(y).
Conditional Independence, and Naive Bayes

Q: Mutual information is positive [...] the probability of at least some joint events P(x, y) does not equal the product of the probabilities of the separate events P(x)*P(y).
A: Mutual information is positive if and only if the probability of at least some joint events P(x, y) does not equal the product of the probabilities of the separate events P(x)*P(y).
Conditional Independence, and Naive Bayes

Q: Mutual information is positive if and only if the [...] does not equal the product of the probabilities of the separate events P(x)*P(y).
A: Mutual information is positive if and only if the probability of at least some joint events P(x, y) does not equal the product of the probabilities of the separate events P(x)*P(y).
Conditional Independence, and Naive Bayes

Q: Mutual information is positive if and only if the probability of at least some joint events P(x, y) [...] the product of the probabilities of the separate events P(x)*P(y).
A: Mutual information is positive if and only if the probability of at least some joint events P(x, y) does not equal the product of the probabilities of the separate events P(x)*P(y).
Conditional Independence, and Naive Bayes

Q: Mutual information is positive if and only if the probability of at least some joint events P(x, y) does not equal the [...].
A: Mutual information is positive if and only if the probability of at least some joint events P(x, y) does not equal the product of the probabilities of the separate events P(x)*P(y).
Conditional Independence, and Naive Bayes

Q: [...] =>
P(x,y) != P(x)*P(y)
P(x,y) / P(y) != P(x)
P(x|y) != P(x)
A: I(X;Y) > 0 =>
P(x,y) != P(x)*P(y)
P(x,y) / P(y) != P(x)
P(x|y) != P(x)
Conditional Independence, and Naive Bayes

Q: I(X;Y) > 0 =>
[...]
P(x,y) / P(y) != P(x)
P(x|y) != P(x)
A: I(X;Y) > 0 =>
P(x,y) != P(x)*P(y)
P(x,y) / P(y) != P(x)
P(x|y) != P(x)
Conditional Independence, and Naive Bayes

Q: I(X;Y) > 0 =>
P(x,y) != P(x)*P(y)
[...]
P(x|y) != P(x)
A: I(X;Y) > 0 =>
P(x,y) != P(x)*P(y)
P(x,y) / P(y) != P(x)
P(x|y) != P(x)
Conditional Independence, and Naive Bayes

Q: I(X;Y) > 0 =>
P(x,y) != P(x)*P(y)
P(x,y) / P(y) != P(x)
[...]
A: I(X;Y) > 0 =>
P(x,y) != P(x)*P(y)
P(x,y) / P(y) != P(x)
P(x|y) != P(x)
Conditional Independence, and Naive Bayes

Q: [...] =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
A: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
Conditional Independence, and Naive Bayes

Q: I(X;Y | Z) > 0 =>
[...]
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
A: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
Conditional Independence, and Naive Bayes

Q: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
[...]
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
A: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
Conditional Independence, and Naive Bayes

Q: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
([...]) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
A: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
Conditional Independence, and Naive Bayes

Q: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / ([...]) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
A: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
Conditional Independence, and Naive Bayes

Q: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) [...] (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
A: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
Conditional Independence, and Naive Bayes

Q: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
[...]
P(x|y,z) != P(x|z)
A: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
Conditional Independence, and Naive Bayes

Q: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
[...]
A: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
Conditional Independence, and Naive Bayes

Q: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
Which last line reads "[...]"
A: I(X;Y | Z) > 0 =>
P(x,y|z) != P(x|z)*P(y|z)
P(x,y|z) / P(y|z) != P(x|z)
(P(x,y,z) / P(z)) / (P(y, z) / P(z)) != P(x|z)
P(x,y,z) / P(y,z) != P(x|z)
P(x|y,z) != P(x|z)
Which last line reads "Even knowing Z, learning Y still changes our beliefs about X."
Conditional Independence, and Naive Bayes

Q: [...] is a hugely important concept in probability theory - to cite just one example, without [...], the universe would have no structure.
A: Conditional independence is a hugely important concept in probability theory - to cite just one example, without conditional independence, the universe would have no structure.
Conditional Independence, and Naive Bayes

Q: Conditional independence is a hugely important concept in [...] - to cite just one example, without conditional independence, the universe would have no structure.
A: Conditional independence is a hugely important concept in probability theory - to cite just one example, without conditional independence, the universe would have no structure.
Conditional Independence, and Naive Bayes

Q: Conditional independence is a hugely important concept in probability theory - to cite just one example, without conditional independence, [...].
A: Conditional independence is a hugely important concept in probability theory - to cite just one example, without conditional independence, the universe would have no structure.
Conditional Independence, and Naive Bayes

Q: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every [...] of these variables, one variable is evidence about the other. If you select U and W, for example, then learning U=U1 will tell you something you didn't know before about the probability W=W1.
An unmanageable inferential mess? Evidence gone wild? Not necessarily.
A: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, one variable is evidence about the other. If you select U and W, for example, then learning U=U1 will tell you something you didn't know before about the probability W=W1.
An unmanageable inferential mess? Evidence gone wild? Not necessarily.
Conditional Independence, and Naive Bayes

Q: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, [...]. If you select U and W, for example, then learning U=U1 will tell you something you didn't know before about the probability W=W1.
An unmanageable inferential mess? Evidence gone wild? Not necessarily.
A: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, one variable is evidence about the other. If you select U and W, for example, then learning U=U1 will tell you something you didn't know before about the probability W=W1.
An unmanageable inferential mess? Evidence gone wild? Not necessarily.
Conditional Independence, and Naive Bayes

Q: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, one variable is evidence about the other. If you select U and W, for example, then [...] will tell you something you didn't know before about the probability W=W1.
An unmanageable inferential mess? Evidence gone wild? Not necessarily.
A: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, one variable is evidence about the other. If you select U and W, for example, then learning U=U1 will tell you something you didn't know before about the probability W=W1.
An unmanageable inferential mess? Evidence gone wild? Not necessarily.
Conditional Independence, and Naive Bayes

Q: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, one variable is evidence about the other. If you select U and W, for example, then learning U=U1 will [...] about the probability W=W1.
An unmanageable inferential mess? Evidence gone wild? Not necessarily.
A: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, one variable is evidence about the other. If you select U and W, for example, then learning U=U1 will tell you something you didn't know before about the probability W=W1.
An unmanageable inferential mess? Evidence gone wild? Not necessarily.
Conditional Independence, and Naive Bayes

Q: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, one variable is evidence about the other. If you select U and W, for example, then learning U=U1 will tell you something you didn't know before about [...].
An unmanageable inferential mess? Evidence gone wild? Not necessarily.
A: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, one variable is evidence about the other. If you select U and W, for example, then learning U=U1 will tell you something you didn't know before about the probability W=W1.
An unmanageable inferential mess? Evidence gone wild? Not necessarily.
Conditional Independence, and Naive Bayes

Q: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, one variable is evidence about the other. If you select U and W, for example, then learning U=U1 will tell you something you didn't know before about the probability W=W1.
An [...]? Evidence gone wild? Not necessarily.
A: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, one variable is evidence about the other. If you select U and W, for example, then learning U=U1 will tell you something you didn't know before about the probability W=W1.
An unmanageable inferential mess? Evidence gone wild? Not necessarily.
Conditional Independence, and Naive Bayes

Q: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, one variable is evidence about the other. If you select U and W, for example, then learning U=U1 will tell you something you didn't know before about the probability W=W1.
An unmanageable inferential mess? Evidence gone wild? [...].
A: Let there be five variables U, V, W, X, Y; and moreover, suppose that for every pair of these variables, one variable is evidence about the other. If you select U and W, for example, then learning U=U1 will tell you something you didn't know before about the probability W=W1.
An unmanageable inferential mess? Evidence gone wild? Not necessarily.
Conditional Independence, and Naive Bayes

Q: why is the situation with U, V, W, X, and Y all being evidence about each other not an unmanageable inferential inconvenience?
A: because there may be some sixth variable Z, which, if we knew it, really would screen off every pair of variables from each other
Conditional Independence, and Naive Bayes

Q: what are the two possibilities for getting the sixth variable Z, which, if we knew it, really would screen off every variable from each other?
A: we may have to construct Z rather than observing it directly
Conditional Independence, and Naive Bayes

Q: Perhaps, [...] a thing is "human", then the probabilities of it speaking, wearing clothes, and having the standard number of fingers, are all independent.
A: Perhaps, given that a thing is "human", then the probabilities of it speaking, wearing clothes, and having the standard number of fingers, are all independent.
Conditional Independence, and Naive Bayes

Q: Perhaps, given that a thing is [...], then the probabilities of it speaking, wearing clothes, and having the standard number of fingers, are all independent.
A: Perhaps, given that a thing is "human", then the probabilities of it speaking, wearing clothes, and having the standard number of fingers, are all independent.
Conditional Independence, and Naive Bayes

Q: Perhaps, given that a thing is "human", then [...] of it speaking, wearing clothes, and having the standard number of fingers, are all independent.
A: Perhaps, given that a thing is "human", then the probabilities of it speaking, wearing clothes, and having the standard number of fingers, are all independent.
Conditional Independence, and Naive Bayes

Q: Perhaps, given that a thing is "human", then the probabilities of it speaking, wearing clothes, and having the standard number of fingers, are all [...].
A: Perhaps, given that a thing is "human", then the probabilities of it speaking, wearing clothes, and having the standard number of fingers, are all independent.
Conditional Independence, and Naive Bayes

Q: This is called the [...], because it usually isn't quite true, but pretending that it's true can simplify the living daylights out of your calculations.
A: This is called the "Naive Bayes" method, because it usually isn't quite true, but pretending that it's true can simplify the living daylights out of your calculations.
Conditional Independence, and Naive Bayes

Q: This is called the "Naive Bayes" method, because it [...], but pretending that it's true can simplify the living daylights out of your calculations.
A: This is called the "Naive Bayes" method, because it usually isn't quite true, but pretending that it's true can simplify the living daylights out of your calculations.
Conditional Independence, and Naive Bayes

Q: This is called the "Naive Bayes" method, because it usually isn't quite true, but [...] can simplify the living daylights out of your calculations.
A: This is called the "Naive Bayes" method, because it usually isn't quite true, but pretending that it's true can simplify the living daylights out of your calculations.
Conditional Independence, and Naive Bayes

Q: This is called the "Naive Bayes" method, because it usually isn't quite true, but pretending that it's true can [...].
A: This is called the "Naive Bayes" method, because it usually isn't quite true, but pretending that it's true can simplify the living daylights out of your calculations.
Conditional Independence, and Naive Bayes

Q: why do we keep separate track of the influence of clothed-ness on speech capability given finger number?
A: we don't
Conditional Independence, and Naive Bayes

Q: what do we do instead of keeping separate track of the influence of clothed-ness on speech capability given finger number?
A: use all the information we've observed to keep track of the probability that this thingy is a human
Conditional Independence, and Naive Bayes

Q: We just use all the information we've observed to keep track of the probability that this thingy is a human (or alternatively, something else, like a chimpanzee or robot) and then [...] to predict anything we haven't seen yet, like vulnerability to hemlock.
A: We just use all the information we've observed to keep track of the probability that this thingy is a human (or alternatively, something else, like a chimpanzee or robot) and then use our beliefs about the central class to predict anything we haven't seen yet, like vulnerability to hemlock.
Conditional Independence, and Naive Bayes

Q: We just use all the information we've observed to keep track of the probability that this thingy is a human (or alternatively, something else, like a chimpanzee or robot) and then use our beliefs about the central class to [...], like vulnerability to hemlock.
A: We just use all the information we've observed to keep track of the probability that this thingy is a human (or alternatively, something else, like a chimpanzee or robot) and then use our beliefs about the central class to predict anything we haven't seen yet, like vulnerability to hemlock.
Conditional Independence, and Naive Bayes

Q: Any [...] U, V, W, X, and Y just act as evidence for the central class variable Z, and then we use the posterior distribution on Z to make any predictions that need making about unobserved variables in U, V, W, X, and Y.
A: Any observations of U, V, W, X, and Y just act as evidence for the central class variable Z, and then we use the posterior distribution on Z to make any predictions that need making about unobserved variables in U, V, W, X, and Y.
Conditional Independence, and Naive Bayes

Q: Any observations of U, V, W, X, and Y just act as [...], and then we use the posterior distribution on Z to make any predictions that need making about unobserved variables in U, V, W, X, and Y.
A: Any observations of U, V, W, X, and Y just act as evidence for the central class variable Z, and then we use the posterior distribution on Z to make any predictions that need making about unobserved variables in U, V, W, X, and Y.
Conditional Independence, and Naive Bayes

Q: Any observations of U, V, W, X, and Y just act as evidence for the central class variable Z, and then we use the [...] to make any predictions that need making about unobserved variables in U, V, W, X, and Y.
A: Any observations of U, V, W, X, and Y just act as evidence for the central class variable Z, and then we use the posterior distribution on Z to make any predictions that need making about unobserved variables in U, V, W, X, and Y.
Conditional Independence, and Naive Bayes

Q: Any observations of U, V, W, X, and Y just act as evidence for the central class variable Z, and then we use the posterior distribution on Z to [...] about unobserved variables in U, V, W, X, and Y.
A: Any observations of U, V, W, X, and Y just act as evidence for the central class variable Z, and then we use the posterior distribution on Z to make any predictions that need making about unobserved variables in U, V, W, X, and Y.
Conditional Independence, and Naive Bayes

Q: Any observations of U, V, W, X, and Y just act as evidence for the central class variable Z, and then we use the posterior distribution on Z to make any predictions that need making about [...] in U, V, W, X, and Y.
A: Any observations of U, V, W, X, and Y just act as evidence for the central class variable Z, and then we use the posterior distribution on Z to make any predictions that need making about unobserved variables in U, V, W, X, and Y.
Conditional Independence, and Naive Bayes

Q: The [...] just needs a logistic threshold - an S-curve response - and the weights of the inputs just need to match the logarithms of the likelihood ratios, etcetera.
A: The central unit just needs a logistic threshold - an S-curve response - and the weights of the inputs just need to match the logarithms of the likelihood ratios, etcetera.
Conditional Independence, and Naive Bayes

Q: The central unit just needs a [...] - an S-curve response - and the weights of the inputs just need to match the logarithms of the likelihood ratios, etcetera.
A: The central unit just needs a logistic threshold - an S-curve response - and the weights of the inputs just need to match the logarithms of the likelihood ratios, etcetera.
Conditional Independence, and Naive Bayes

Q: The central unit just needs a logistic threshold - an [...] - and the weights of the inputs just need to match the logarithms of the likelihood ratios, etcetera.
A: The central unit just needs a logistic threshold - an S-curve response - and the weights of the inputs just need to match the logarithms of the likelihood ratios, etcetera.
Conditional Independence, and Naive Bayes

Q: The central unit just needs a logistic threshold - an S-curve response - and the [...] just need to match the logarithms of the likelihood ratios, etcetera.
A: The central unit just needs a logistic threshold - an S-curve response - and the weights of the inputs just need to match the logarithms of the likelihood ratios, etcetera.
Conditional Independence, and Naive Bayes

Q: The central unit just needs a logistic threshold - an S-curve response - and the weights of the inputs just need to [...] the logarithms of the likelihood ratios, etcetera.
A: The central unit just needs a logistic threshold - an S-curve response - and the weights of the inputs just need to match the logarithms of the likelihood ratios, etcetera.
Conditional Independence, and Naive Bayes

Q: The central unit just needs a logistic threshold - an S-curve response - and the weights of the inputs just need to match the [...], etcetera.
A: The central unit just needs a logistic threshold - an S-curve response - and the weights of the inputs just need to match the logarithms of the likelihood ratios, etcetera.
Conditional Independence, and Naive Bayes

Q: what's Eliezer's good guess about one of the reasons why logistic response often works so well in neural networks?
A: it lets the algorithm sneak in a little Bayesian reasoning while the designers aren't looking
Conditional Independence, and Naive Bayes

Q: 34. what's wrong with: No way am I trying to summarize this one.  Just read the blog post.
A: 34. You use categorization to make inferences about properties that don't have the appropriate empirical structure, namely, conditional independence given knowledge of the class, to be well-approximated by Naive Bayes.
37 Ways That Words Can Be Wrong

Q: Believe it or not, for some decades, there was [...] about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
A: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
Words as Mental Paintbrush Handles

Q: Believe it or not, for some decades, there was a serious debate about whether people [...] - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
A: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
Words as Mental Paintbrush Handles

Q: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - [...] - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
A: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
Words as Mental Paintbrush Handles

Q: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people [...] (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
A: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
Words as Mental Paintbrush Handles

Q: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images ([...], a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
A: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
Words as Mental Paintbrush Handles

Q: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having [...], like a LISP token, active in their brain.
A: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
Words as Mental Paintbrush Handles

Q: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, [...], active in their brain.
A: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
Words as Mental Paintbrush Handles

Q: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, [...].
A: Believe it or not, for some decades, there was a serious debate about whether people really had mental images in their mind - an actual picture of a chair somewhere - or if people just naively thought they had mental images (having been misled by "introspection", a very bad forbidden activity), while actually just having a little "chair" label, like a LISP token, active in their brain.
Words as Mental Paintbrush Handles

Q: what was the academic paradigm that we didn't have real images in our brain a deranged legacy of?
A: behaviorism
Words as Mental Paintbrush Handles

Q: what did behaviorism say about human thought?
A: it denied its existence
Words as Mental Paintbrush Handles

Q: how did behaviorism seek to explain all human phenomena?
A: as "reflex"
Words as Mental Paintbrush Handles

Q: what behavior did the behaviorists include as "reflex"?
A: speech
Words as Mental Paintbrush Handles

Q: back in the day of the contraversy about the brain representing visual images, what experiment was done to infer the existence of mental imagery?
A: Show subjects two objects and ask them if one can be rotated into correspondence with the other.
Words as Mental Paintbrush Handles

Q: what was the result (not conclusion) of showing subjects two objects and ask them if one can be rotated into correspondence with the other?
A: The response time is linearly proportional to the angle of rotation required.
Words as Mental Paintbrush Handles

Q: what made the result that the response time is linearly proportional to the angle of rotation required easy to explain?
A: if you are actually visualizing the image and continuously rotating it at a constant speed
Words as Mental Paintbrush Handles

Q: what made the result that the response time is linearly proportional to the angle of rotation required hard to explain?
A: if you are just checking propositional features of the image
Words as Mental Paintbrush Handles

Q: today, how are we sure that our brain represents visual images?
A: we can actually neuroimage the little pictures in the visual cortex
Words as Mental Paintbrush Handles

Q: does your brain really represent a detailed image of what it sees or imagines?
A: yes
Words as Mental Paintbrush Handles

Q: Part of the reason people [...] with words, is that they do not realize how much complexity lurks behind words.
A: Part of the reason people get in trouble with words, is that they do not realize how much complexity lurks behind words.
Words as Mental Paintbrush Handles

Q: Part of the reason people get in trouble with words, is that they [...] how much complexity lurks behind words.
A: Part of the reason people get in trouble with words, is that they do not realize how much complexity lurks behind words.
Words as Mental Paintbrush Handles

Q: Part of the reason people get in trouble with words, is that they do not realize [...] words.
A: Part of the reason people get in trouble with words, is that they do not realize how much complexity lurks behind words.
Words as Mental Paintbrush Handles

Q: "Apple" isn't just [...]. That's a shadow. That's the tip of the tiger's tail.
A: "Apple" isn't just a sequence of two syllables or five letters. That's a shadow. That's the tip of the tiger's tail.
Words as Mental Paintbrush Handles

Q: "Apple" isn't just a sequence of two syllables or five letters. [...]. That's the tip of the tiger's tail.
A: "Apple" isn't just a sequence of two syllables or five letters. That's a shadow. That's the tip of the tiger's tail.
Words as Mental Paintbrush Handles

Q: "Apple" isn't just a sequence of two syllables or five letters. That's a shadow. [...].
A: "Apple" isn't just a sequence of two syllables or five letters. That's a shadow. That's the tip of the tiger's tail.
Words as Mental Paintbrush Handles

Q: [...], or rather the concepts behind them, are paintbrushes - you can use them to draw images in your own mind.
A: Words, or rather the concepts behind them, are paintbrushes - you can use them to draw images in your own mind.
Words as Mental Paintbrush Handles

Q: Words, or rather [...], are paintbrushes - you can use them to draw images in your own mind.
A: Words, or rather the concepts behind them, are paintbrushes - you can use them to draw images in your own mind.
Words as Mental Paintbrush Handles

Q: Words, or rather the concepts behind them, are [...] - you can use them to draw images in your own mind.
A: Words, or rather the concepts behind them, are paintbrushes - you can use them to draw images in your own mind.
Words as Mental Paintbrush Handles

Q: Words, or rather the concepts behind them, are paintbrushes - you can [...] to draw images in your own mind.
A: Words, or rather the concepts behind them, are paintbrushes - you can use them to draw images in your own mind.
Words as Mental Paintbrush Handles

Q: Words, or rather the concepts behind them, are paintbrushes - you can use them to [...].
A: Words, or rather the concepts behind them, are paintbrushes - you can use them to draw images in your own mind.
Words as Mental Paintbrush Handles

Q: [...], if you employ concepts to make a picture in your visual cortex.
A: Literally draw, if you employ concepts to make a picture in your visual cortex.
Words as Mental Paintbrush Handles

Q: Literally draw, if you [...] to make a picture in your visual cortex.
A: Literally draw, if you employ concepts to make a picture in your visual cortex.
Words as Mental Paintbrush Handles

Q: Literally draw, if you employ concepts to [...] in your visual cortex.
A: Literally draw, if you employ concepts to make a picture in your visual cortex.
Words as Mental Paintbrush Handles

Q: Literally draw, if you employ concepts to make a picture in your [...].
A: Literally draw, if you employ concepts to make a picture in your visual cortex.
Words as Mental Paintbrush Handles

Q: And by the use of [...], you can reach into someone else's mind, and grasp their paintbrushes to draw pictures in their minds - sketch a little green dog in their visual cortex.
A: And by the use of shared labels, you can reach into someone else's mind, and grasp their paintbrushes to draw pictures in their minds - sketch a little green dog in their visual cortex.
Words as Mental Paintbrush Handles

Q: And by the use of shared labels, you can [...], and grasp their paintbrushes to draw pictures in their minds - sketch a little green dog in their visual cortex.
A: And by the use of shared labels, you can reach into someone else's mind, and grasp their paintbrushes to draw pictures in their minds - sketch a little green dog in their visual cortex.
Words as Mental Paintbrush Handles

Q: And by the use of shared labels, you can reach into someone else's mind, and [...] to draw pictures in their minds - sketch a little green dog in their visual cortex.
A: And by the use of shared labels, you can reach into someone else's mind, and grasp their paintbrushes to draw pictures in their minds - sketch a little green dog in their visual cortex.
Words as Mental Paintbrush Handles

Q: And by the use of shared labels, you can reach into someone else's mind, and grasp their paintbrushes to [...] - sketch a little green dog in their visual cortex.
A: And by the use of shared labels, you can reach into someone else's mind, and grasp their paintbrushes to draw pictures in their minds - sketch a little green dog in their visual cortex.
Words as Mental Paintbrush Handles

Q: what should you not think, because you send syllables through the air, or letters through the Internet?
A: that it is the syllables or the letters that draw pictures in the visual cortex
Words as Mental Paintbrush Handles

Q: how do we know that it isn't the syllables or the letters that draw pictures in the visual cortex?
A: That takes some complex instructions that wouldn't fit in the sequence of letters.
Words as Mental Paintbrush Handles

Q: "Apple" is [...], and drawing a picture of an apple from scratch would take more data than that.
A: "Apple" is 5 bytes, and drawing a picture of an apple from scratch would take more data than that.
Words as Mental Paintbrush Handles

Q: "Apple" is 5 bytes, and [...] would take more data than that.
A: "Apple" is 5 bytes, and drawing a picture of an apple from scratch would take more data than that.
Words as Mental Paintbrush Handles

Q: "Apple" is 5 bytes, and drawing a picture of an apple from scratch would take [...].
A: "Apple" is 5 bytes, and drawing a picture of an apple from scratch would take more data than that.
Words as Mental Paintbrush Handles

Q: "[...]" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
A: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
Words as Mental Paintbrush Handles

Q: "Apple" is merely the [...] attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
A: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
Words as Mental Paintbrush Handles

Q: "Apple" is merely the tag [...] the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
A: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
Words as Mental Paintbrush Handles

Q: "Apple" is merely the tag attached to the [...] apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
A: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
Words as Mental Paintbrush Handles

Q: "Apple" is merely the tag attached to the true and wordless [...], which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
A: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
Words as Mental Paintbrush Handles

Q: "Apple" is merely the tag attached to the true and wordless apple concept, which can [...], or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
A: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
Words as Mental Paintbrush Handles

Q: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or [...], or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
A: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
Words as Mental Paintbrush Handles

Q: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or [...], or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
A: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
Words as Mental Paintbrush Handles

Q: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or [...], maybe even send out the motor behavior for eating an apple...
A: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
Words as Mental Paintbrush Handles

Q: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even [...]...
A: "Apple" is merely the tag attached to the true and wordless apple concept, which can paint a picture in your visual cortex, or collide with "cheese", or recognize an apple when you see one, or taste its archetype in apple pie, maybe even send out the motor behavior for eating an apple...
Words as Mental Paintbrush Handles

Q: is the way our labels work as simple as just calling up a picture from memory?
A: no
Words as Mental Paintbrush Handles

Q: how do we know that it's not just as simple as calling up a picture from memory?
A: Or how would you be able to visualize combinations like a "triangular lightbulb" - imposing triangleness on lightbulbs, keeping the essence of both, even if you've never seen such a thing in your life?
Words as Mental Paintbrush Handles

Q: The [...] are just pointers - "look in memory area 1387540".
A: The labels are just pointers - "look in memory area 1387540".
Words as Mental Paintbrush Handles

Q: The labels are just [...] - "look in memory area 1387540".
A: The labels are just pointers - "look in memory area 1387540".
Words as Mental Paintbrush Handles

Q: The labels are just pointers - "[...]".
A: The labels are just pointers - "look in memory area 1387540".
Words as Mental Paintbrush Handles

Q: Sooner or later, when [...], it comes time to dereference it, and actually look in memory area 1387540.
A: Sooner or later, when you're handed a pointer, it comes time to dereference it, and actually look in memory area 1387540.
Words as Mental Paintbrush Handles

Q: Sooner or later, when you're handed a pointer, it comes time to [...] it, and actually look in memory area 1387540.
A: Sooner or later, when you're handed a pointer, it comes time to dereference it, and actually look in memory area 1387540.
Words as Mental Paintbrush Handles

Q: Sooner or later, when you're handed a pointer, it comes time to dereference it, and [...] in memory area 1387540.
A: Sooner or later, when you're handed a pointer, it comes time to dereference it, and actually look in memory area 1387540.
Words as Mental Paintbrush Handles

Q: 35. what's wrong with: Visualize a "triangular lightbulb".  What did you see?
A: 35. You think that words are like tiny little LISP symbols in your mind, rather than words being labels that act as handles to direct complex mental paintbrushes that can paint detailed pictures in your sensory workspace.
37 Ways That Words Can Be Wrong

Q: what did Eliezer say to empathize with Albert and Barry arguing over the tree in the forest?
A: "Either the falling tree makes a sound, or it does not!"
Variable Question Fallacies

Q: [...] is not always a reliable heuristic, if you substitute arbitrary English sentences for P.
A: (P or ~P) is not always a reliable heuristic, if you substitute arbitrary English sentences for P.
Variable Question Fallacies

Q: (P or ~P) is not always a [...], if you substitute arbitrary English sentences for P.
A: (P or ~P) is not always a reliable heuristic, if you substitute arbitrary English sentences for P.
Variable Question Fallacies

Q: (P or ~P) is not always a reliable heuristic, if you substitute [...] for P.
A: (P or ~P) is not always a reliable heuristic, if you substitute arbitrary English sentences for P.
Variable Question Fallacies

Q: "This sentence is false" cannot be consistently viewed as [...].
A: "This sentence is false" cannot be consistently viewed as true or false.
Variable Question Fallacies

Q: why can you just bull ahead on every occasion with "Either it does or it doesn't!"
A: you can't
Variable Question Fallacies

Q: Surely, 2 + 2 = X [...]? Well, maybe, if it's really the same X, the same 2, and the same + and =. If X evaluates to 5 on some occasions and 4 on another, your indignation may be misplaced.
A: Surely, 2 + 2 = X or it does not? Well, maybe, if it's really the same X, the same 2, and the same + and =. If X evaluates to 5 on some occasions and 4 on another, your indignation may be misplaced.
Variable Question Fallacies

Q: Surely, 2 + 2 = X or it does not? Well, maybe, if it's [...], the same 2, and the same + and =. If X evaluates to 5 on some occasions and 4 on another, your indignation may be misplaced.
A: Surely, 2 + 2 = X or it does not? Well, maybe, if it's really the same X, the same 2, and the same + and =. If X evaluates to 5 on some occasions and 4 on another, your indignation may be misplaced.
Variable Question Fallacies

Q: Surely, 2 + 2 = X or it does not? Well, maybe, if it's really the same X, [...], and the same + and =. If X evaluates to 5 on some occasions and 4 on another, your indignation may be misplaced.
A: Surely, 2 + 2 = X or it does not? Well, maybe, if it's really the same X, the same 2, and the same + and =. If X evaluates to 5 on some occasions and 4 on another, your indignation may be misplaced.
Variable Question Fallacies

Q: Surely, 2 + 2 = X or it does not? Well, maybe, if it's really the same X, the same 2, and [...]. If X evaluates to 5 on some occasions and 4 on another, your indignation may be misplaced.
A: Surely, 2 + 2 = X or it does not? Well, maybe, if it's really the same X, the same 2, and the same + and =. If X evaluates to 5 on some occasions and 4 on another, your indignation may be misplaced.
Variable Question Fallacies

Q: Surely, 2 + 2 = X or it does not? Well, maybe, if it's really the same X, the same 2, and the same + and =. If X evaluates to [...], your indignation may be misplaced.
A: Surely, 2 + 2 = X or it does not? Well, maybe, if it's really the same X, the same 2, and the same + and =. If X evaluates to 5 on some occasions and 4 on another, your indignation may be misplaced.
Variable Question Fallacies

Q: Surely, 2 + 2 = X or it does not? Well, maybe, if it's really the same X, the same 2, and the same + and =. If X evaluates to 5 on some occasions and 4 on another, your [...].
A: Surely, 2 + 2 = X or it does not? Well, maybe, if it's really the same X, the same 2, and the same + and =. If X evaluates to 5 on some occasions and 4 on another, your indignation may be misplaced.
Variable Question Fallacies

Q: To even begin claiming that ([...]) ought to be a necessary truth, the symbol P must stand for exactly the same thing in both halves of the dilemma.
A: To even begin claiming that (P or ~P) ought to be a necessary truth, the symbol P must stand for exactly the same thing in both halves of the dilemma.
Variable Question Fallacies

Q: To even begin claiming that (P or ~P) ought to be a necessary truth, the symbol P must [...] in both halves of the dilemma.
A: To even begin claiming that (P or ~P) ought to be a necessary truth, the symbol P must stand for exactly the same thing in both halves of the dilemma.
Variable Question Fallacies

Q: To even begin claiming that (P or ~P) ought to be a necessary truth, the symbol P must stand for exactly the same thing in [...].
A: To even begin claiming that (P or ~P) ought to be a necessary truth, the symbol P must stand for exactly the same thing in both halves of the dilemma.
Variable Question Fallacies

Q: why is there nothing paradoxical about "Either the fall makes a sound or not!"
A: because Albert::sound is not the same as Barry::sound
Variable Question Fallacies

Q: what might Albert and Barry forget, even if they carefully verify that it is the same tree, in the same forest, and the same occasion of falling?
A: to check that they are matching this event against exactly the same concept
Variable Question Fallacies

Q: Many of the words we use are really [...] implicit variables supplied by context.
A: Many of the words we use are really functions of implicit variables supplied by context.
Variable Question Fallacies

Q: Many of the words we use are really functions of [...] supplied by context.
A: Many of the words we use are really functions of implicit variables supplied by context.
Variable Question Fallacies

Q: Many of the words we use are really functions of implicit variables supplied by [...].
A: Many of the words we use are really functions of implicit variables supplied by context.
Variable Question Fallacies

Q: what is the name for the phenomenon of trying to handle words being functions of implicit variables supplied by context?
A: "speaker deixis"
Variable Question Fallacies

Q: what is "speaker deixis"?
A: the phenomenon of trying to handle words being functions of implicit variables supplied by context
Variable Question Fallacies

Q: "Martin told Bob the building was on his left." But "[...]" is a function-word that evaluates with a speaker-dependent variable invisibly grabbed from the surrounding context.
A: "Martin told Bob the building was on his left." But "left" is a function-word that evaluates with a speaker-dependent variable invisibly grabbed from the surrounding context.
Variable Question Fallacies

Q: "Martin told Bob the building was on his left." But "left" is a [...] that evaluates with a speaker-dependent variable invisibly grabbed from the surrounding context.
A: "Martin told Bob the building was on his left." But "left" is a function-word that evaluates with a speaker-dependent variable invisibly grabbed from the surrounding context.
Variable Question Fallacies

Q: "Martin told Bob the building was on his left." But "left" is a function-word that [...] with a speaker-dependent variable invisibly grabbed from the surrounding context.
A: "Martin told Bob the building was on his left." But "left" is a function-word that evaluates with a speaker-dependent variable invisibly grabbed from the surrounding context.
Variable Question Fallacies

Q: "Martin told Bob the building was on his left." But "left" is a function-word that evaluates with a [...] invisibly grabbed from the surrounding context.
A: "Martin told Bob the building was on his left." But "left" is a function-word that evaluates with a speaker-dependent variable invisibly grabbed from the surrounding context.
Variable Question Fallacies

Q: "Martin told Bob the building was on his left." But "left" is a function-word that evaluates with a speaker-dependent variable [...] from the surrounding context.
A: "Martin told Bob the building was on his left." But "left" is a function-word that evaluates with a speaker-dependent variable invisibly grabbed from the surrounding context.
Variable Question Fallacies

Q: "Martin told Bob the building was on his left." But "left" is a function-word that evaluates with a speaker-dependent variable invisibly grabbed from the [...].
A: "Martin told Bob the building was on his left." But "left" is a function-word that evaluates with a speaker-dependent variable invisibly grabbed from the surrounding context.
Variable Question Fallacies

Q: Your [...] sees the map, not the territory directly.
A: Your mind's eye sees the map, not the territory directly.
Variable Question Fallacies

Q: Your mind's eye sees the [...], not the territory directly.
A: Your mind's eye sees the map, not the territory directly.
Variable Question Fallacies

Q: Your mind's eye sees the map, not the [...] directly.
A: Your mind's eye sees the map, not the territory directly.
Variable Question Fallacies

Q: Your mind's eye sees the map, not the territory [...].
A: Your mind's eye sees the map, not the territory directly.
Variable Question Fallacies

Q: If you have a [...] with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
A: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
Variable Question Fallacies

Q: If you have a question with a [...], that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
A: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
Variable Question Fallacies

Q: If you have a question with a hidden variable, that [...] different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
A: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
Variable Question Fallacies

Q: If you have a question with a hidden variable, that evaluates to [...], it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
A: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
Variable Question Fallacies

Q: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it [...] reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
A: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
Variable Question Fallacies

Q: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like [...] - what your mind's eye sees, shifts around depending on where it looks.
A: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
Variable Question Fallacies

Q: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - [...], shifts around depending on where it looks.
A: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
Variable Question Fallacies

Q: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, [...] depending on where it looks.
A: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
Variable Question Fallacies

Q: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around [...] where it looks.
A: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
Variable Question Fallacies

Q: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on [...].
A: If you have a question with a hidden variable, that evaluates to different expressions in different contexts, it feels like reality itself is unstable - what your mind's eye sees, shifts around depending on where it looks.
Variable Question Fallacies

Q: what do people sometimes think when they discover a sentence with more than one interpretation?
A: that they have discovered an unstable portion of reality
Variable Question Fallacies

Q: what's wrong with saying "If X is 5 on some occasions and 4 on another, the sentence '2 + 2 = X' may have no fixed truth-value"?
A: There is not one sentence with a variable truth-value.
Variable Question Fallacies

Q: what is the truth value of  "2 + 2 = X"?
A: it has no truth-value
Variable Question Fallacies

Q: why does  "2 + 2 = X" have no truth-value?
A: it is not a proposition, as mathematicians define proposition-ness
Variable Question Fallacies

Q: why does the variable question fallacy sneak in, even when you allegedly know better?
A: because, well, that's how the algorithm feels from inside
Variable Question Fallacies

Q: 36. what's wrong with: "Martin told Bob the building was on his left."
A: 36. You use a word that has different meanings in different places as though it meant the same thing on each occasion, possibly creating the illusion of something protean and shifting.
37 Ways That Words Can Be Wrong

Q: 36. followup point: "Martin told Bob the building was on his left."
A: 36. But "left" is a function-word that evaluates with a speaker-dependent variable grabbed from the surrounding context.  Whose "left" is meant, Bob's or Martin's?
37 Ways That Words Can Be Wrong

Q: 37. what's wrong with: 37 Ways That Suboptimal Use Of Categories Can Have Negative Side Effects On Your Cognition
A: 37. You think that definitions can't be "wrong", or that "I can define a word any way I like!"
37 Ways That Words Can Be Wrong

Q: 37. followup point: 37 Ways That Suboptimal Use Of Categories Can Have Negative Side Effects On Your Cognition
A: 37. This kind of attitude teaches you to indignantly defend your past actions, instead of paying attention to their consequences, or fessing up to your mistakes.
37 Ways That Words Can Be Wrong

Q: what is one of the primary lessons of Eliezer's gigantic list of ways words can be wrong?
A: "There's no way my choice of X can be 'wrong'" is nearly always an error in practice, whatever the theory.
37 Ways That Words Can Be Wrong

Q: You can always be [...]. Even when it's theoretically impossible to be [...], you can still be [...]. There is never a Get-Out-Of-Jail-Free card for anything you do. That's life.
A: You can always be wrong. Even when it's theoretically impossible to be wrong, you can still be wrong. There is never a Get-Out-Of-Jail-Free card for anything you do. That's life.
37 Ways That Words Can Be Wrong

Q: You can always be wrong. Even when [...], you can still be wrong. There is never a Get-Out-Of-Jail-Free card for anything you do. That's life.
A: You can always be wrong. Even when it's theoretically impossible to be wrong, you can still be wrong. There is never a Get-Out-Of-Jail-Free card for anything you do. That's life.
37 Ways That Words Can Be Wrong

Q: You can always be wrong. Even when it's theoretically impossible to be wrong, you can still be wrong. There is never a [...] for anything you do. That's life.
A: You can always be wrong. Even when it's theoretically impossible to be wrong, you can still be wrong. There is never a Get-Out-Of-Jail-Free card for anything you do. That's life.
37 Ways That Words Can Be Wrong

Q: You can always be wrong. Even when it's theoretically impossible to be wrong, you can still be wrong. There is never a Get-Out-Of-Jail-Free card for [...]. That's life.
A: You can always be wrong. Even when it's theoretically impossible to be wrong, you can still be wrong. There is never a Get-Out-Of-Jail-Free card for anything you do. That's life.
37 Ways That Words Can Be Wrong

Q: You can always be wrong. Even when it's theoretically impossible to be wrong, you can still be wrong. There is never a Get-Out-Of-Jail-Free card for anything you do. [...].
A: You can always be wrong. Even when it's theoretically impossible to be wrong, you can still be wrong. There is never a Get-Out-Of-Jail-Free card for anything you do. That's life.
37 Ways That Words Can Be Wrong

Q: Saying "[...]" makes around as much sense as driving a car over thin ice with the accelerator floored and saying, "Looking at this steering wheel, I can't see why one radial angle is special - so I can turn the steering wheel any way I like."
A: Saying "Words are arbitrary; I can define a word any way I like" makes around as much sense as driving a car over thin ice with the accelerator floored and saying, "Looking at this steering wheel, I can't see why one radial angle is special - so I can turn the steering wheel any way I like."
37 Ways That Words Can Be Wrong

Q: Saying "Words are arbitrary; I can define a word any way I like" makes around as much sense as [...] and saying, "Looking at this steering wheel, I can't see why one radial angle is special - so I can turn the steering wheel any way I like."
A: Saying "Words are arbitrary; I can define a word any way I like" makes around as much sense as driving a car over thin ice with the accelerator floored and saying, "Looking at this steering wheel, I can't see why one radial angle is special - so I can turn the steering wheel any way I like."
37 Ways That Words Can Be Wrong

Q: Saying "Words are arbitrary; I can define a word any way I like" makes around as much sense as driving a car over thin ice with the accelerator floored and saying, "Looking at this steering wheel, [...] - so I can turn the steering wheel any way I like."
A: Saying "Words are arbitrary; I can define a word any way I like" makes around as much sense as driving a car over thin ice with the accelerator floored and saying, "Looking at this steering wheel, I can't see why one radial angle is special - so I can turn the steering wheel any way I like."
37 Ways That Words Can Be Wrong

Q: Saying "Words are arbitrary; I can define a word any way I like" makes around as much sense as driving a car over thin ice with the accelerator floored and saying, "Looking at this steering wheel, I can't see why one radial angle is special - so [...]."
A: Saying "Words are arbitrary; I can define a word any way I like" makes around as much sense as driving a car over thin ice with the accelerator floored and saying, "Looking at this steering wheel, I can't see why one radial angle is special - so I can turn the steering wheel any way I like."
37 Ways That Words Can Be Wrong

Q: If you're trying to [...], or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
A: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
37 Ways That Words Can Be Wrong

Q: If you're trying to go anywhere, or even just trying to [...], you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
A: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
37 Ways That Words Can Be Wrong

Q: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen [...] that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
A: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
37 Ways That Words Can Be Wrong

Q: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control [...] words, definitions, categories, classes, boundaries, labels, and concepts.
A: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
37 Ways That Words Can Be Wrong

Q: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use [...], definitions, categories, classes, boundaries, labels, and concepts.
A: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
37 Ways That Words Can Be Wrong

Q: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, [...], categories, classes, boundaries, labels, and concepts.
A: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
37 Ways That Words Can Be Wrong

Q: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, [...], classes, boundaries, labels, and concepts.
A: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
37 Ways That Words Can Be Wrong

Q: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, [...], boundaries, labels, and concepts.
A: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
37 Ways That Words Can Be Wrong

Q: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, [...], labels, and concepts.
A: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
37 Ways That Words Can Be Wrong

Q: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, [...], and concepts.
A: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
37 Ways That Words Can Be Wrong

Q: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and [...].
A: If you're trying to go anywhere, or even just trying to survive, you had better start paying attention to the three or six dozen optimality criteria that control how you use words, definitions, categories, classes, boundaries, labels, and concepts.
37 Ways That Words Can Be Wrong