AI’s Language Problem

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.

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."

Reading your post, you actually seem quite confused.

>Imagine a hypothetical language that is so compressed, so non-redundant, so little correlated, that it's indistinguishable from random noise. Learning this language may seem an impossible task.

Well yes, learning a class of strings in which each digit of every finite prefix is statistically independent from each other digit, is very hard, bordering on impossible (or at least, impossible to do better than uniform-random guessing).

>But in fact it's very easy to produce text in this language. Just produce random noise!

But that isn't the learning problem being posed! You are not being asked to learn `P(string | language)` (which is, in fact, the uniform distribution over arbitrary-length strings), but `P(language | string1, string2, ..., stringn)`, which by the way you've posed the problem factorizes into `P(language| character1) x P(language|character2) x ... x P(language|characterm)`. If the actual strings are sampled from a uniform distribution over arbitrary-length strings, then we have two possibilities:

1) The prior is over a class of languages some of which are not optimally compressed, and which thus do not render each character (or even each string) conditionally independent. In this case, the posterior will favor languages that do render each character conditionally independent, but we won't be able to tell apart one such hypothesis from another. We've learned very little.

2) The prior is over a class of languages all of which yield strings full of conditionally-independent noise: no hypothesis can compress the data. In this case, the evidence-probability and the likelihood cancel, and our posterior over languages equals our prior (we've learned nothing).

>Real language, of course, has tons of statistical patterns, and is definitely not random. But I don't see how it is harder to learn than, say more redundant audio recording of the same words, or a video recording of the person speaking them. That extra information is irrelevant and will just be discarded by any smart algorithm anyway.

Noooo. Compression does not work that way. Compression works by finding informative patterns in data, not by throwing them away. If your goal is to learn the structure in the data, you want the structure to be more redundant rather than less.

I'm telling you, once the paper is submitted, I can send you a copy and just show you the equations and inequalities demonstrating this fact.

>Most people do. As I said some people don't, and they function fine. See this: http://www.bbc.com/news/health-34039054

Differences in sensorimotor cortex function that leave the brain unable to perform top-down offline simulation with a high subjective-sensory precision don't invalidate the broad theory that cortical microcircuits are generative models (in particular, hierarchical ones, possibly just large hierarchies in which the individual nodes are very simple distributions).

http://www.fil.ion.ucl.ac.uk/~karl/The%20free-energy%20princ...

>Robots are never going to have exactly the same internal states and experience as humans. They could be very, very different, in structure, to the human brain.

Duh. However, if we want them to work, they probably have to run on free-energy minimization somehow. There is more necessity at work here than connectionism believes in, but that's a fault in connectionism.

>Being exactly like humans isn't the goal. Mimicking humans is an interesting diversion, but it's not necessary, or the goal in and of itself.

I didn't say that a working robot's representations had to exactly match those of humans. In fact, doing so would be downright inefficient, since robots would have completely different embodiments to work with, and thus be posed different inference problems in both perception and action. The fact that they would be, necessarily, inference problems is the shared fact.

>And you may be right that a robot without vision would be disadvantaged. I think that's mostly anthropomorphism, imagining how disadvantaged blind humans are (and in fact even blind humans can function better than most people expect.) But even if it's true, my point is that sight is not strictly necessary for intelligence.

Sight isn't. Some kind of high-dimensional sense-data is.

>In fact I think vision may even be a disadvantage. So much of the brain is devoted to visual processing. While text, and even language itself, are hacks that evolution created relatively recently. A brain built purely for language could be much more efficient at it than we can probably imagine. Ditching vision could save a huge amount of processing power and space.

That's putting the cart before the horse. Language is, again, an efficient but redundant (ie: robust against noise) code for the models (ie: knowledge, intuitive theories, as you like) the brain already wields. You can take the linguistic usage statistics of a word and construct a causal-role concept for them in the absence of a verbal definition or sensory grounding for the word, which is arguably what children do when we read a word before anyone has taught it to us, but doing so will only work well when the concepts' definitions are themselves mostly ungrounded and abstract.

So purely linguistic processing would work fairly well for, say, some of mathematics, but not so much for more empirical fields like social interaction, ballistic-missile targeting, and the proper phrasing of demands made to world leaders in exchange for not blowing up the human race.