Predicting where AI is going in 2020

I dont' know if it's a good idea to mix terminology from machine learning (or game theory?) in the subject of human learning, like you do. At some point the analogies become a bit too far-fetched. Parents and babies "are agents who interact to maximise rewards"? That just sounds like taking an analogy and running with it, and then putting it in a rocket and sending it to Mars. We have no idea why and how babies think or decide to behave how they behave.

This is one reason why I'm confused about the OP's use of "labelled information". Clearly that is a term borrowed from machine learning to describe something that happens in the real world- but, what?

>> Well surely that's a case of the range of the vocal chords? Parrots are another intelligent creature that has better range and they imitate all sorts of sounds.

Parrots (and birds like mainas etc) immitate human sounds and all sorts of sounds, but they don't discriminate between, e.g., the sound made by a train whistle and the sound made by a human carer. I mean that a parrot will not learn to speak a human language by immitating its sounds, any more than it'll learn to speak train by immitating a train whistle.

Human babies don't just immitate their parents' sounds, they figure out what those sounds do and how they come together to form language and express meaning. That is a small miracle that we don't understand at all well and Chomsky is 100% right to speak of scientific wonderment, in its context. It is really mind-blowing that kids can eventually learn to speak without, for the vast majority of children, anyone around them having any idea how to teach a kid to speak in any systematic way. Not to mention the trouble that adults have in learning another language even given formal training in it (which perhaps is further evidence that we really don't know how to teach language, because we don't understand how it works, so again, how can we teach small children to speak a language, but not adults?).

Chomsky's universal grammar is really the simplest answer: children don't learn how to speak a human language, they already know how, and they only have to learn the vocabulary and syntax of the language of their parents. This only presuposes that humans have human biology, and that our biology is responsible for our language ability. We can't learn to fly because we don't have wings and parrots can't learn to speak because they don't have human brains.

[Edit: that it's the simplest answer doesn't mean it's the right answer, only that it's got a damn good chance to be it.]

Maybe not fully "correctly"!

In

https://news.ycombinator.com/item?id=21949722

are some arguments about the term data structure. For that argument, what I have in mind is more general. So, with generality beyond data structures we can have relational database schema (apparently Microsoft's SQL Server documentation has a different meaning of schema) which can be new.

Or the intelligence needs to store and manipulate data. To store is to use a data structure of some kind, and to manipulate is to use some algorithms.

So, the challenge is to find the data structure and corresponding algorithms.

My prediction is that in the 10 years we will do that.

My reasoning:

(1) Mice, rats, kittens, puppies, ..., and much more including humans do a lot of it, that is, what humans do with intelligence.

(2) The babies of these species learn starting with relatively very little or nothing.

(3) The learning is fairly simple, e.g., does not require a lot of computing or data.

(4) Once the learning gets going, especially in humans, the amount of learning grows quickly from building on past learning, new data, and new thinking.

(5) We can guess that especially early on the learning is relatively simple -- that keeps the intelligence relatively stable.

Well I'm predicting that from essentially just (1)-(5) we can get the human level intelligence I mentioned, i.e., we will find the appropriate data structures and algorithms.

If there were a lot of value in my thinking about (1)-(5), NSF and/or DARPA would fund me to pursue what I outlined; since I'm sure they would not fund me, we, including me, can conclude that there is not a lot of value in my thinking. Maybe that does not make my thinking wrong; it might be right but just so far incomplete!

So, why not the short summary of GOFAI "someday"? Because I'm guessing that data structures, algorithms, (1)-(5), and 10 years will be at least a significant part of what will be sufficient for GOFAI!

In particular, what I explained seems to be more than just "someday" because:

(A) I believe that current work in deep learning neural networks, which obviously DO have some applications, might have some role in some relatively autonomous and non-conscious parts of GOFAI, maybe similar to some of what is crucial in some insects. So, I don't see the current work in neural networks as very relevant to the GOFAI I am predicting. So, I'm pruning off that stream of work for progress toward what I'm predicting. Again the neural network DOES have some applications.

(B) I worked for some years in AI in IBM's Watson lab. My view is that none of that work is at all relevant to what I'm predicting. So, I'm pruning off that stream of work for progress toward what I'm predicting.

So, with (A) and (B), I'm saying two paths to avoid. If I'm right, then avoiding (A) and (B) would have some evidence of being good contributions.

But no doubt like nearly everyone else, I want practical results faster than my prediction promises so don't want to work on that prediction. And I am not working on that path and, instead, am pursuing my startup which DOES have some pure and applied math at its core but is MUCH more likely to work and work much faster than my AI prediction!

But here HN asked for some predictions, so I made some!

Fair, I just looked for the first reference I could find. I haven't done any real linguistics in years, although I vividly remember the arguments. Especially that Evans & Levinson article 10 years or so back ("The Myth of language universals") which generated quite some heat. If I have time, I will check out your reference.

Not sure about the negative examples; but language acquisition was never my focus area anyway.

I would just generally be cautious about applying formal language theory too readily to linguistics, that's all I wanted to say.