An understanding of AI’s limitations is starting to sink in

In their mental model, the AI is far from defined by these capabilities. They won’t be happy unless there’s an actual AGI whose capabilities happen to overlap with the spec.

So my point is historically we have cheated our way out of defining “intelligence” and instead given necessary but insufficient examples. I think this is the mechanism behind the goalpost-shifting in “AI”.

That’s the metaphor: it would clearly be absurd for engineers to define Jesus by some examples of capabilities. People who are waiting for the second coming won’t be satisfied.

In the lab we maybe should define AI by some set of capabilities. But clients, journalists etc. picture the Hollywood version, and narrowly fulfilling their spec won’t actually satisfy them.

> Your use of 'human-centric' is odd. I would have thought the traditional 'human-centric' theory of the mind is something monolithic and indivisible.

Sorry, my answer wasn't clear. It was not tied to the "small processes communicating..." approach but to your very list of "problems" (facial recognition/detection, facial synthesis (deepfakes), speech synthesis, speech recognition..) which seems to me expressed in a way tied to human activities, while at least part if the underlying "intelligence" underlying many of them may also exists in other forms of life (other mammals, birds, fishes...).

> Suggesting that it's many small processes communicating with each other is basically taken straight out of nature, from ants, schools of fish, birds flocking, etc.

Exactly. My point is that analyzing the ways "the smaller, specialized problems" are tackled by non-human living beings seems pertinent as self-analysis (as humans analyzing human intelligence) is difficult, and as various species may apply various solutions, some more easy to grok. Focusing on "problems" too specific to the human being may be a sort of "framing" detrimental to the quest. Moreover the famous Dijkstra quote ("The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.") may be pertinent.

> incremental progress in individual processes that can then be composed together is a productive way to traverse the energy landscape. This is why (imo) we see so many symbiotic relationships from cells on up to higher level animals.

I agree. My point is about _how_ we consider the system(s) (our "point of view"): framing it to human characteristics, globally or locally (dualism)... It seems to me that the very organization of a system may be neglected when we consider it a stack of "small processes communicating...". Pirsig's "Metaphysics of Quality" may be pertinent.

>> Aren't your criteria of the "black box" type, given that AFAIK no human can really completely explain how he recognizes faces/does NLP/walks/...?

> I'm not quite sure what your point is here. If you're critiquing me about neural networks being black boxes and not giving us real insight into the underlying system, that's fair and the reason why I said I didn't like the black box aspect of neural networks.

This was my point and I agree with you.

> if there is a black box model that can be easily manipulated, this will probably lead to deeper models much quicker.

I'm less optimistic, as it is only 'probable', and AFAIK won't give us more real insight into the underlying system.

> Having no human be able to describe the underlying computation (of face recognition, nlp, walking etc.) doesn't mean it's indescribable, it means it's not describable by anyone right now. > I have no doubt we'll figure out how to do complex human-level computation even if we don't have a deep model of the specifics of human thought.

I agree, we will enhance ways to "approximate" (tricks leading us to a solution to each "local" problem) up to the point of being able to solve real-world problems. However it may reach some hard limit (as far as I understand this is the point of the article), and using a powerful tool/method insufficiently understood may be dangerous.

How is this knowledge encoded in the heads of people? Perhaps through the choice of synapse strength and connectivity between neurons?

What is being encoded in GPT-3 weights as it reads through millions of pages of text? How is it different from tuning biological synapses?

Still don't know what I'm talking about? It's OK, neither do I :)

The Turing test hasn't been passed if I'm the judge. Supposing I were unable to tell the difference between any AI system and a human interlocutor defending the same argument, at any time before 2023, I'd admit my prediction was wholly incorrect.

I doubt very much I'll find a taker for this bet, however. The AI field has always been bigger on optimism than results, as we both know.