If all science/math problems become solvable by AI, what differences would remain between humans and AI?
Building AI that can set its own goals seems to be the next obvious step. But how to do this? How do humans do this?
In general, I don't think there are any big secrets in AI research today.
The only one I'm absolutely clueless about is how OpenAI trained the IMO model if they didn't use any formal verification at all.
Any guesses?
I'm tired of seeing Hierarchical Reasoning Model on every X thread. I don't think its great on ARC
Nobody else seems to have actually gone into the weeds so I'll do that over the next few days
looking for a student researcher September-December.
working w/ me+@mayalen_etc, and alongside @zzznah+@RandazzoEttore+others in Pi, on either NCAs or Computational Life.
must be enrolled in a degree granting program. fill out the interest form by EOW: docs.google.com/forms/d/e/1FAI…
🎯🎯🎯 It would be great to see AI papers adopt such rigor in benchmarking (and clearly explain the experiment setup!).
Details are conveniently omitted for hype poasting, but **nuances really matter** for a clear understanding + honest discussion of capabilities.
🎯🎯🎯 It would be great to see AI papers adopt such rigor in benchmarking (and clearly explain the experiment setup!).
Details are conveniently omitted for hype poasting, but **nuances really matter** for a clear understanding + honest discussion of capabilities.
Damn I did not expect this this year.
@polynoamial is an ablation study possible that 100% ensures these problems aren't in training distribution? Would help color expectations for scientific discovery
I understand its hard with the vast training data but extraordinary claims…
Damn I did not expect this this year.
@polynoamial is an ablation study possible that 100% ensures these problems aren't in training distribution? Would help color expectations for scientific discovery
I understand its hard with the vast training data but extraordinary claims…
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