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> All of the information of our world is contained in text Even if this were a true statement, it's still the case that it might not be enough. There is a clas

by dmreedy 10y ago

> All of the information of our world is contained in text Even if this were a true statement, it's still the case that it might not be enough. There is a class of functions that are simply not learnable without some prerequisite knowledge. This is directly analogous to a one-time pad in crypto. It is entirely possible that the function 'language' is in this class of unlearnable functions. While it may be the case that certain varieties of intelligence are learnable tabula rasa from a powerful neural net, the surface form of human natural language (the part your recommending measuring) may simply not have enough information in it to decode the whole picture. It is entirely possible that you need to supply some of your own information as well to the picture, in a specific manner so as to act as a kind of decryption key. A record needs a record player, even if you can make similar sounds with cassettes and CDs. And so, I'm willing to bet that you simply cannot, using raw, uninformed statistical techniques, predict what word a human would say next. You need to understand more of the underlying structure of humans first. I will agree, however, that the success towards the Hutter Prize is a valuable demonstration of AI progress. Simply because I believe that maximal compression and the kind of intelligence I'm talking about are one and the same thing. You need to offload as much of the semantic weight of the corpus into the encryption algorithm as you can. That means building a very complex model of natural language. And if you accept the premise that this model is not simply learnable by observing the surface form, then that means building Strong AI

Houshalter 10y ago
>There is a class of functions that are simply not learnable without some prerequisite knowledge. This is directly analogous to a one-time pad in crypto. It is entirely possible that the function 'language' is in this class of unlearnable functions. I don't understand how this could possibly be the case. We can already make great progress on language understanding with simple methods like word2vec, or perhaps even markov chains. There are tons of statistical patterns in text that can be learned by computers.
- dmreedy 10y ago
  It can be the case if Chomsky was right, and Universal Grammar and other similar structures are a thing. That would mean that part of our ability to understand language comes from the particular structure of our brain (which everyone seems to by and large share). That would mean that some of our ability to understand language is genetic in nature, by whatever means genes direct the structure of brain development.
  - Houshalter 10y ago
    But I don't see any reason a "universal grammar" couldn't be learned. It may take something more complicated than ANNs, of course. But it would be really weird if there was a pattern in language that was so obfuscated it couldn't be detected at all.
    - dmreedy 10y ago
      it comes down to the limits of available Information with a capital 'I'. If you're working within the encoding system (as you're recommending here with the "all the text in the world" approach), then in order to learn the function that's generating this information, the messages that you're examining have a minimum amount of information they can convey. There needs to be enough visible structure purely within the context of the messages themselves to make the underlying signal clear. I don't think it's so weird to imagine that natural language really doesn't convey a ton of explicit information on its own. Sure, there's some there, enough that our current AI attempts can solve little corners of the bigger problem. But is it so strange to imagine that the machinery of the human brain takes lossy, low-information language and expands, extrapolates, and interprets it so heavily so as to make it orders of magnitude more complex than the lossy, narrow channel through which it was conveyed? That the only reason we're capable of learning language and understanding eachother (the times we _do_ understand eachother) is because we all come pre-equipped with the same decryption hardware?
  - bweitzman 10y ago
    So if language comes from the structure of the brain, what would stop us from simulating that structure to give a machine mastery of language? And specifically what would imply that a machine which had some of that structure would need to learn by interaction as the top level comment suggests?
- DonaldFisk 10y ago
  Wouldn't you still need to attach meanings to the words though? How could an AI system ever understand, for example, the Voynich Manuscript? There's plenty of text in it, and encryption methods when it was written weren't particularly strong. Or how would a person do if they were locked in a room with lots of books written in a language unknown to them?
  - Houshalter 10y ago
    Infact someone has run word2vec on the Voynich manuscript: http://blog.christianperone.com/2016/01/voynich-manuscript-word-vectors-and-t-sne-visualization-of-some-patterns/ http://blog.christianperone.com/2016/01/voynich-manuscript-w... (web archive while it's down: http://web.archive.org/web/20160205003817/http://blog.christianperone.com/2016/01/voynich-manuscript-word-vectors-and-t-sne-visualization-of-some-patterns/ http://web.archive.org/web/20160205003817/http://blog.christ...) Such methods could someday completely decode the thing, but for now they just show the relationships between words and different clusters of words, not their meaning. Of course we have no idea how the Voynich manuscript is encrypted (which would make the assumptions of word2vec wrong), or if it even has any meaning at all. And it's an incredibly small dataset compared to modern text corpuses, so there is probably significant uncertainty and overfitting. And other problems like inconsistent spellings, many errors in transcriptions, etc. But in principle this is a good strategy. >how would a person do if they were locked in a room with lots of books written in a language unknown to them? If you spent all day reading them, for years, and you somehow didn't get bored and kept at it, eventually you would start to see the patterns. You would learn how "slithy toves" are related to "brillig", even if you have no idea how that would translate to English. Study it long enough, and you may even be able to produce text in that language, indistinguishable from the real text. You may be able to predict the next word in a sentence, and identify mistakes, etc. Perhaps carry out a conversation in that language. And I think eventually you would understand what the words mean, by comparing the patterns to those found in English. Once you have guesses for translations of just a few words, you can translate the rest. Because you know the relationships between words, and so knowing one word constrains the possibilities of what the other words can be. If the translation it produces is nonsense, the words you guessed must have been wrong, and you can try again with other words. Eventually you will find a translation that isn't nonsense, and there you go. This would be very difficult for humans, because the number of hypotheses to test is so large, and analyzing text takes forever. Computers can do it at lightspeed though.
- jdmichal 10y ago
  Connotation. Connotation is a huge part of human language, and is completely orthogonal to the denotation, which is what a vector is going to find. For instance, an AI should accurately be able to distinguish the fact that calling someone "cheap" is different from calling them "frugal", even though both objectively mean that the person doesn't spend much money. There's also the related phenomenon of "subtext" -- the idea that some language has a different meaning than what's said. For instance, when I ask about whether a signature line on a form is required, and the other person says, "Yes, it's required. However you think best to get the signature." There's a subtext there of, "This signature won't actually be checked, so don't worry about it."

by dmreedy 10y ago

Houshalter 10y ago

>There is a class of functions that are simply not learnable without some prerequisite knowledge. This is directly analogous to a one-time pad in crypto. It is entirely possible that the function 'language' is in this class of unlearnable functions. I don't understand how this could possibly be the case. We can already make great progress on language understanding with simple methods like word2vec, or perhaps even markov chains. There are tons of statistical patterns in text that can be learned by computers.

dmreedy 10y ago
It can be the case if Chomsky was right, and Universal Grammar and other similar structures are a thing. That would mean that part of our ability to understand language comes from the particular structure of our brain (which everyone seems to by and large share). That would mean that some of our ability to understand language is genetic in nature, by whatever means genes direct the structure of brain development.
- Houshalter 10y ago
  But I don't see any reason a "universal grammar" couldn't be learned. It may take something more complicated than ANNs, of course. But it would be really weird if there was a pattern in language that was so obfuscated it couldn't be detected at all.
  - dmreedy 10y ago
    it comes down to the limits of available Information with a capital 'I'. If you're working within the encoding system (as you're recommending here with the "all the text in the world" approach), then in order to learn the function that's generating this information, the messages that you're examining have a minimum amount of information they can convey. There needs to be enough visible structure purely within the context of the messages themselves to make the underlying signal clear. I don't think it's so weird to imagine that natural language really doesn't convey a ton of explicit information on its own. Sure, there's some there, enough that our current AI attempts can solve little corners of the bigger problem. But is it so strange to imagine that the machinery of the human brain takes lossy, low-information language and expands, extrapolates, and interprets it so heavily so as to make it orders of magnitude more complex than the lossy, narrow channel through which it was conveyed? That the only reason we're capable of learning language and understanding eachother (the times we _do_ understand eachother) is because we all come pre-equipped with the same decryption hardware?
- bweitzman 10y ago
  So if language comes from the structure of the brain, what would stop us from simulating that structure to give a machine mastery of language? And specifically what would imply that a machine which had some of that structure would need to learn by interaction as the top level comment suggests?
DonaldFisk 10y ago
Wouldn't you still need to attach meanings to the words though? How could an AI system ever understand, for example, the Voynich Manuscript? There's plenty of text in it, and encryption methods when it was written weren't particularly strong. Or how would a person do if they were locked in a room with lots of books written in a language unknown to them?
- Houshalter 10y ago
  Infact someone has run word2vec on the Voynich manuscript: http://blog.christianperone.com/2016/01/voynich-manuscript-word-vectors-and-t-sne-visualization-of-some-patterns/ http://blog.christianperone.com/2016/01/voynich-manuscript-w... (web archive while it's down: http://web.archive.org/web/20160205003817/http://blog.christianperone.com/2016/01/voynich-manuscript-word-vectors-and-t-sne-visualization-of-some-patterns/ http://web.archive.org/web/20160205003817/http://blog.christ...) Such methods could someday completely decode the thing, but for now they just show the relationships between words and different clusters of words, not their meaning. Of course we have no idea how the Voynich manuscript is encrypted (which would make the assumptions of word2vec wrong), or if it even has any meaning at all. And it's an incredibly small dataset compared to modern text corpuses, so there is probably significant uncertainty and overfitting. And other problems like inconsistent spellings, many errors in transcriptions, etc. But in principle this is a good strategy. >how would a person do if they were locked in a room with lots of books written in a language unknown to them? If you spent all day reading them, for years, and you somehow didn't get bored and kept at it, eventually you would start to see the patterns. You would learn how "slithy toves" are related to "brillig", even if you have no idea how that would translate to English. Study it long enough, and you may even be able to produce text in that language, indistinguishable from the real text. You may be able to predict the next word in a sentence, and identify mistakes, etc. Perhaps carry out a conversation in that language. And I think eventually you would understand what the words mean, by comparing the patterns to those found in English. Once you have guesses for translations of just a few words, you can translate the rest. Because you know the relationships between words, and so knowing one word constrains the possibilities of what the other words can be. If the translation it produces is nonsense, the words you guessed must have been wrong, and you can try again with other words. Eventually you will find a translation that isn't nonsense, and there you go. This would be very difficult for humans, because the number of hypotheses to test is so large, and analyzing text takes forever. Computers can do it at lightspeed though.
jdmichal 10y ago
Connotation. Connotation is a huge part of human language, and is completely orthogonal to the denotation, which is what a vector is going to find. For instance, an AI should accurately be able to distinguish the fact that calling someone "cheap" is different from calling them "frugal", even though both objectively mean that the person doesn't spend much money. There's also the related phenomenon of "subtext" -- the idea that some language has a different meaning than what's said. For instance, when I ask about whether a signature line on a form is required, and the other person says, "Yes, it's required. However you think best to get the signature." There's a subtext there of, "This signature won't actually be checked, so don't worry about it."