[HarHarVeryFunny]

> And even if he did, I could name you a couple of CEOs of AI labs with better models that would disagree, or even Turing award laureates. This is by no means a consensus stance in the expert community.

I disagree - there is pretty widespread agreement that reasoning is a weakness, even among the best models, (and note Chollet's $1M ARC prize competition to spur improvements), but the big labs all seem to think that post-training can fix it. To me this is whack-a-mole wishful thinking (reminds me of CYC - just add more rules!). At least one of your "Turing award laureates" thinks Transformers are a complete dead end as far as AGI goes.

We'll see soon enough who's right.

[accountnum]

A weakness of the current models in some domains considered useful, yes - but not a fundamental limitation of the architecture. I see no consensus on the latter whatsoever.

The ARC challenge tests spatial reasoning, something we humans are obviously quite good at, given 4 billion years of evolutionary optimization. But as I said, there is no "general reasoning", it's all domain dependent. A child does better at the spatial problems in ARC given that it has that previously mentioned evolutionary advantage, but just as we don't worship calculators as superior intelligences because they can multiply 10^9 digit numbers in milliseconds, we shouldn't draw fundamental conclusions from humans doing well at a problem that they are in many ways built to solve. If the failures of previous predictions - those that considered Chess or Go as unmistakable signals of true general reasoning - have taught us anything, it's that general reasoning simply does not exist.

The bet of current labs is synthetic data in pre-training, or slight changes of natural data that induces more generalization pressure for multi-step transformations on state in various domains. The goal is to change the data so models learn these transformations more readily and develop good heuristics for them, so not the non-continuous patching that you suggest.

But yes, the next generation of models will probably reveal much more about where we're headed.

[HarHarVeryFunny]

> If the failures of previous predictions - those that considered Chess or Go as unmistakable signals of true general reasoning - have taught us anything, it's that general reasoning simply does not exist.

I don't think DeepBlue or AlphaGo/etc were meant to teach us anything - they were just showcases of technological prowess by the companies involved, demonstrations of (narrow) machine intelligence.

But...

Reasoning (differentiated from simpler shallow "reactive" intelligence) is basically multi-step chained what-if prediction, and may involve a branching exploration of alternatives ("ok, so that wouldn't work, so what if I did this instead ..."), so could be framed as a tree search of sorts, not entirely disimilar to the MCTS used by DeepBlue or AlphaGo.

Of course general reasoning is a lot more general than playing a game like Chess or Go since the type of moves/choices available/applicable will vary at each step (these aren't all "game move" steps), as will the "evaluation function" that predicts what'll happen if we took that step, but "tree search" isn't a bad way to conceptualize the process, and this is true regardless of the domain(s) of knowledge over which the reasoning is operating.

Which is to say, that reasoning is in fact a generalized process, and one who' nature has some corresponding requirements (e.g. keeping track of state) for any machine to be capable of performing it ...