The "ever" comes from the Church-Turing thesis. Maybe in the future computers will not be Turing machines, but that we can't know yet.

This is my entire quote:

>> We basically have no idea how to create a test for intelligence that computers cannot beat by brute force or big data approaches so we have no effective way to test computers for (artificial) intelligence.

When I say "brute force" I mean an exhaustive search of some large search space, in real time, not in evolutionary time. For example, searching a very large database for an answer, rather than computing the answer. But, as usual, I don't understand the point you're trying to make and where the bit about evolution came from. Can you clarify?

Btw, three requests, so we can have a productive conversation with as little time wasted in misunderstandings as possible:

a) Don't Fisk me (https://www.urbandictionary.com/define.php?term=Fisking).

b) Don't quote my words out of context.

c) If you don't understand why I say something, just ask.

Thanks, I know about the core knowledge priors, and François Chollet's claims about them (I've read his white paper, although it was long, and long-winded and I don't remember most of it). The empirical observation however is that none of the systems that have positive performance on ARC, on Kaggle or the new leaderboard, have anything to do with core knowledge priors. Which means core knowledge priors are not needed to solve any of the so-far solved ARC tasks.

I think Chollet is making a syllogistic error:

  a) Humans have core knowledge priors and can solve ARC tasks
  b) Some machine X can solve ARC tasks
  c) Therefore machine X has core knowledge priors

That doesn't follow; and like I say it is refuted by empirical observations, to boot. This is particularly so for his claim that ARC "does not appear approachable" (what) by deep learning. Plenty of neural-net based systems on the ARC-AGI leaderboard.

There's also no reason to assume that core knowledge priors present any particular difficulty to computers (i.e. that they're "hard for AI"). The problem seems to be more with the ability of humans to formalise them precisely enough to be programmed into a computer. That's not a computer problem, it's a human problem. But that's common in AI. For example, we don't know how to hand-code an image classifier; but we can train very accurate ones with deep neural nets. That doesn't mean computers aren't good at image classification: they are; CNNs to the proof. It's humans who suck at coding it. Except nobody's insisting on image classification datasets with only three or four training examples for each class, so it was possible to develop those powerful deep neural net classifiers. Chollet's choice to only allow very few training examples is creating an artificial data bottleneck that does not restrict anyone in the real world so it tells us nothing about the true capabilities of deep neural nets.

Cthulhu. I never imagined I'd end up defending deep neural nets...

I have to say this: Chollet annoys me mightily. Every time I hear him speak, he makes gigantic statements about what intelligence is, and how to create it artificially, as if he knows what dozens of thousands of researchers in biology, cognitive science, psychology, neuroscience, AI, and who knows what other field, don't. That is despite the fact that he has created just as many intelligent machines as everyone else so far, which is to say: zero. Where that self-confidence comes from, I have no idea, but the results on his "AIQ test" indicate he, just like everyone else, has no clue what intelligence is, yet he persists with the absurd self-assurance. Insufferable arrogance.

Apologies for the rant.