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Comment by Barrin92 | Hacker News Reader

>but it's more of a language that an intelligent agent may operate in, opposed to the self-sufficient structure

yes, this kind of functional intelligence seems distinct from an actual living entity, which is the thing that uses subordinate functions to pursue goals and has some interior state, motivations and some sort of architecture. To reduce intelligence to tokens predicting more tokens is kind of like saying f(x), just solve for intelligence. When prediction itself is only partially what intelligent systems are about.

Agent is a very important word because it's accurate ("a means or instrument by which a guiding intelligence achieves a result") And it's the latter I think we ought to be after when talking about 'general ai'.

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It’s possible that in serving the function of prediction, the model forms a complex internal representation akin even to goals, motivations, etc. It is true that DL architectures are not explicitly designed to do this, not yet anyway. But my point is that the task of prediction can give rise to such architectural patterns. According to Karl Friston’s Free Energy Principle, biological brains serve the purpose of predicting the value of different actions available.

this assumes there is a finite list of available actions. for example, a primate has to see that "sharpen a stick to use as a spear" is an option, and add it to the list

I agree with that, I think it's even necessarily true in natural intelligence which after all emerged by some means spontaneously. But scientifically I think it is a big problem because it does not supply us with a theory or systematized knowledge of the mind or intelligence. I think you could even imagine say, why not just make a primordial soup simulation, insert some DNA, and crank the speed up, intelligence is just a byproduct somewhere in there in a physics simulation. don't even bother with such details as neural nets.

Scientifically this is unsatisfying but also if for some reason this turns out to be an engineering dead-end we have a big hole where a concrete theory of intelligence should be, with its components, mechanisms and so forth. And sadly I think this is still the weakest link in AI.

To me it seems a little bit like if you trained architects instead of having a theoretical basis for architecture, you just showed them every building in existence and sent them to work. It may very well work, but if it didn't you have a problem. And even if it did, you'd still want to have an understanding of why it works.

Sometimes there is just no reductive theory for emergent phenomena. For example, in the case of ant routing we have a good understanding of the behavior of individual ants, and we can observe the intelligent behavior of the colony as a whole. One could ask for a concrete theory of how the micro scale behavior of each ant leads to the macro scale behavior of the colony, but it doesn't exist. If we had enough working memory to hold all the ants in our minds, we'd see that both micro and macro scale behaviors are different aspects of the same system, there's no "missing link" in the chain of explanations, and the dichotomy is only due to our own cognitive limitations.

There are a lot of problems that do not have analytical solutions, like the N-body problem. With these problems all you can do is numerical simulation, which is pretty much what modern machine learning is.

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