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Comment by Houshalter | Hacker News Reader

>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.

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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.

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.

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?

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?

Nothing would stop us from simulating human brain-like (or analogously powerful) structures to build a machine that genuinely understands natural language. I'm arguing that we can't just learn those structures by statistical optimization techniques though.

If it turns out that the easiest, or even only means of doing this is by emulating the human brain, then it is entirely possible that we inherit a whole new set of constraints and dependencies such that world-simulation and an emobdied mind are required to make such a system learn. If this turns out not to be the case, that there's some underlying principle of language we can emulate (the classic "airplanes don't fly like birds" argument) then it may be the case that text is enough. But that's in the presence of a new assumption, that our system came pre-equipped to learn language, and didn't manufacture an understanding from whole cloth. That the model weights were pre-initialized to specific values.

If there is an innate language structure in the brain then we know that it's possible to develop such a structure by statistical optimization, since this is exactly what evolution did, no?

Nah, it's mostly just hierarchical probability models.

http://www.ncbi.nlm.nih.gov/pubmed/24977647

This is a very neat paper, but:

1) They appear to have crafted the skeleton of a grammar as it is with their nodes, super nodes, and slot collocations. This is directly analogous to something like an Xbar grammar, and is not learned by the system; therefore, if anything, it's strengthening a Chomskian position; the system is learning how a certain set of signals satisfy its extant constraints.

2) The don't appear to go beyond generative grammar, which already seems largely solvable by other ML methods, and is a subset of the problem "language". Correct me if I'm wrong here, it's a very long paper and I may have missed something.

Something like https://news.ycombinator.com/item?id=10016052 possibly?

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."

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?

Infact someone has run word2vec on the Voynich manuscript: http://blog.christianperone.com/2016/01/voynich-manuscript-w... (web archive while it's down: 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.

I'm familiar with this particular attack, as it was discussed here previously. It's a worthwhile attempt but the identification as star names, if real, hasn't been confirmed. But your reservations are justified.

More generally, has any attempt been made to identify the meanings of words in any sufficiently large corpus of text in a known foreign language (for example, Finnish), without being provided with a translation into English, and then compare the identified meanings to the actual meanings, as a first step towards translation?

There was a paper where they trained word vectors for English and Chinese at the same time. But they forced a few Chinese words to have the same vectors as their translated English words. This gave accurate translations for many Chinese words that didn't have translations.

Doing this without any translated words at all, would be more difficult. But I believe possible. It's actually a project I want to try in the near future.

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