Unless you have strong prior beliefs (like "computers can't be AGI") or something else that's problem specific ("these problems can be solved by these techniques which don't count as AGI"). So I guess that's my real question.
* How likely you think AGI is in general.
* How solvable you think the problem is, independently of what's solving it.
In the cases you've brought up that latter probability is very high, which means that they are extremely weak evidence that computers are AGI. So we agree!
In this case the latter probability seems to be quite low - attempts to solve it with computers have largely failed so far!
Edit: I think maybe the disagreement here is about the nature of evidence. I think there can be evidence that something is AGI even if it isn't, in fact, AGI. You seem to believe that if there's any evidence that something is AGI, it must be AGI, I think?
No.
Because there might undiscovered ways to solve these problems that no one claims is AGI.
The definition of AGI is notoriously fuzzy, but non-the-less if there was a 10 line python program (with no external dependencies or data) that could solve it then few would argue that was AGI.
So perhaps there is an algorithm that solves these puzzles 100% of the time and can be easily expressed.
So I agree that only being able to solve these problems doesn't define AGI.
1. Only humans are known to have solved problem X, and we've spent no time looking for alternative solutions.
2. Only humans are known to have solved problem X, and we've spent hundreds of thousands of hours looking for alternative solutions and failed.
Now suppose something solves the problem. I feel like in case 2 we are justified in saying there's evidence that something is a human-like AGI. In case 1 we probably aren't justified in saying that.
To me this seems evident regardless of what the problem actually is! Because if it's hard enough that thousands of human hours cannot find a simple/algorithmic solution it's probably something like an "AGI-complete" problem?
Testing whether an AI can play chess or solve Chollet's ARC problems, or some other set of narrow skills, doesn't prove generality. If you want to test for generality, then you either have to:
1) Have a huge and very broad test suite, covering as many diverse human-level skills as possible.
and/or,
2) Reductively understand what human intelligence is, and what combination of capabilities it provides, then test for all of those capabilities both individually and in combination.
As Chollet notes, a crucial part of any AGI test is solving novel problems that are not just templated versions (or shallow combinatins) of things the wanna-be AGI has been trained on, so for both of above tests this is key.
AGI can add 1+1 correctly, but an ability to do that is not a test for AGI.
"Absence of evidence is evidence of absence."
Presumably you would call this a simple logical fallacy for the same reason, but a little reflection would show that in many cases such a statement is true! It depends on context, in this case your estimate of how well your search covered the possible search space.
Evidence is a continuous variable - things can be weak evidence, strong evidence... There's a whole spectrum. I just take issue with statements like "X is zero evidence of Y" because often you can do a lot better than that with the information at hand.
