Comment by theLiminator
1 day ago
Lol basically we're saying AI isn't AI if we utilize the strength of computers (being able to compute). There's no reason why AGI should have to be as "sample efficient" as humans if it can achieve the same result in less time.
Let's say an agent needs to do 10 brain surgeries on a human to remove a tumor and a human doctor can do it in a single surgery. I would prefer the human.
"steps" are important to optimize if they have negative externalities.
It's kind of the point? To test AI where it's weak instead of where it's strong.
"Sample efficient rule inference where AI gets to control the sampling" seems like a good capability to have. Would be useful for science, for example. I'm more concerned by its overreliance on humanlike spatial priors, really.
ARC has always had that problem but for this round, the score is just too convoluted to be meaningful. I want to know how well the models can solve the problem. I may want to know how 'efficient' they are, but really I don't care if they're solving it in reasonable clock time and/or cost. I certainly do not want them jumbled into one messy convoluted score.
'Reasoning steps' here is just arbitrary and meaningless. Not only is there no utility to it unlike the above 2 but it's just incredibly silly to me to think we should be directly comparing something like that with entities operating in wildly different substrates.
If I can't look at the score and immediately get a good idea of where things stand, then throw it way. 5% here could mean anything from 'solving only a tiny fraction of problems' to "solving everything correctly but with more 'reasoning steps' than the best human scores." Literally wildly different implications. What use is a score like that ?
The measurement metric is in-game steps. Unlimited reasoning between steps is fine.
This makes sense to me. Most actions have some cost associated, and as another poster stated it's not interesting to let models brute-force a solution with millions of steps.
1 reply →
The metric is very similar to cost. It seems odd to justify one and not the other.
1 reply →
It's an interesting point but I too find it questionable. Humans operate differently than machines. We don't design CPU benchmarks around how humans would approach a given computation. It's not entirely obvious why we would do it here (but it might still be a good idea, I am curious).
I think your logic isn't sound: Wouldn't we want a "intelligence" to solve problems efficiently rather than brute force a million monkies? There's defnitely a limit to compute, the same ways there's a limit to how much oil we can use, etc.
In theory, sure, if I can throw a million monkies and ramble into a problem solution, it doesnt matter how I got there. In practice though, every attempt has a direct and indirect impact on the externalities. You can argue those externalities are minor, but the largesse of money going to data centers suggests otherwise.
Lastly, humans use way less energy to solve these in fewer steps, so of course it matter when you throw Killowatts at something that takes milliwatts to solve.
> Lastly, humans use way less energy to solve these in fewer steps,
Not if you count all the energy that was necessary to feed, shelter and keep the the human at his preferred temperature so that he can sit in front of a computer and solve the problem.
ok, but thats the same for bulding a data center.
Try again.
2 replies →