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Comment by pingyong | Hacker News Reader

I think if you have a framework that can produce arbitrary single-task solvers (which AlphaZero can't yet), you would have something indistinguishable from AGI, since communication between single-task solvers is also kinda just a single-task solver.

It's certainly not the most efficient way to use our current hardware, and it's not clear to me how big some of these neural nets would have to be, but if we had computers with a trillion times the memory capacity and speed, IMO it'd certainly work on some level.

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How would a single-task solver, or hierarchy of them, go about constructing a conceptual model of a new problem domain? The problem with a solver is it only really goes in a single direction, but when modeling a system you spend a huge amount of time backtracking and eliminating elements that yielded progress at first but then proved to be obstacles to progress later. You also need to be able to rapidly adapt to changing requirements.

Imagine playing a game of Chess in which the pieces and rules gradually changed bit by bit until by the end of the game you were playing Go. That's much closer to what real life problems are like and a human child could absolutely do that. They might not be much good at it, but they could absolutely do it even without ever having played either game before just learning as they went. Note to AGI researchers, if your chatbot can't cope with that or a problem like it without any forewarning, don't bother applying for a Turning Test with me on the other side of the teletype.

they'd do it like we do: by comparing the new situation to previous ones we know about, and applying the model that fits best, and then adapting to results.

For humans, the more previous ones we know about, the better, because we have more chance of applying a model that works in the new environment. That's called "experience".

That’s a very broad, general description of behaviour that doesn’t actually describe an implementation. In fact it could apply to many completely different possible implementations. I suspect though that humans do more than this, that we have a way of either constructing entirely new models from scratch, or of dramatically adapting models to new situations without mere iterative fitting to feedback. Humans are actually capable of reasoning effectively about entirely new ideas, scenarios and problems. We have little to no idea how we do this.

I don't know. I'm not so sure that we can create new working models from scratch. We definitely learn by iterative feedback: babies wiggle stuff and watch what happens to learn how to move their bodies. Learning to ride a bicycle is mostly about falling off bicycles until you learn how not to.

I've seen people apply their normal behaviour to situations that have changed, and then get totally confused (and angry) as to why the result isn't the same. Observe anyone travelling in a new country for examples ("why don't they show the price with the sales tax included here? This is ridiculous!").

In a perfect world, sure, we'd construct a rational mental model of a new situation and test it carefully to ensure it matched reality before trusting it, and then apply it correctly to the new situation. But it's not a perfect world, and people don't actually do that. Usually we charge in and then cope with the results.

Of course, I'm not saying that AI should do that. It'll be interesting to see how a "good" general AI copes with a genuinely new situation.

I think we apply radically different cognitive machinery to physical skills like riding a bicycle, compared to playing a card game where the rules are on the cards, and you have no idea what rule will be on the next card or even what rules are possible. We can train Chimps to ride bicycles, so they have the cognitive machinery for that, but we can't teach them to play these kinds of card games.

Interesting. True. But is that because we lack the communication skills to explain the rules to chimps, or because they lack the cognitive modelling ability to understand those rules?

Seems to me you're just describing reinforcement learning. Youre just saying a human child can adapt to the new problem faster than the AI can adapt, which is true but not the argument you've been making in this thread.

It's not reinforcement learning, because the child can do it the first time, so there's no reinforcement. I have kids, so many times I have played games with them successfully, purely from a description of the rules and playing as we went. They even beat me once the first time we ever played a game, by employing a rule at the end which had never come up in previous play. Compared to that Alphago isn't even in the race, because we can't even tell it the rules.

> since communication between single-task solvers is also kinda just a single-task solver.

It would be nice if it worked like that but I think you're massively underestimating the problem set here. I'd suggest its more like the architectural glue one needs as an engineer writing a command line util and a fully fledged piece of Enterprise solution (i.e. orders of magnitude).

Of course because we don't actually know how intelligence exactly works we're both guessing here.

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