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AlphaGeometry: An Olympiad-level AI system for geometry
31–40 of 177 posts
Re: AlphaGeometry: An Olympiad-level AI system for geometry
#32Earlier quoted context omitted.
Not a subject matter expert, so forgive me if the question is unintentionally obtuse, but It seems like a reasonable statement. They seem to be inferring that problems with existing public solutions wouldn't be a good indicator of performance in solving novel problems-- likely a more important evaluative measure than how fast it can replicate an answer it's already seen. Since you couldn't know if that solution was i…
They're referring to "performance of performance". Not that it's a big deal. I notice problems like this slip into my writing more and more without detection as I get older :/ I don't see the sentence in the Nature paper, though.
Ah-- I've got super bad ADHD so I usually don't even catch things like that. I'm a terrible editor. I wouldn't be surprised if the paper authors were paying attention to this thread, and gave a call to Nature after seeing the parent comment.
Re: AlphaGeometry: An Olympiad-level AI system for geometry
#33See also https://www.nytimes.com/2024/01/17/science/ai-computers-math... (via https://news.ycombinator.com/item?id=39030186 , but we'll merge that thread hither)
https://archive.is/https://www.nytimes.com/2024/01/17/scienc...
Re: AlphaGeometry: An Olympiad-level AI system for geometry
#34Re: AlphaGeometry: An Olympiad-level AI system for geometry
#35[deleted]
Re: AlphaGeometry: An Olympiad-level AI system for geometry
#36See also https://www.nytimes.com/2024/01/17/science/ai-computers-math...
(via https://news.ycombinator.com/item?id=39030186, but we'll merge that thread hither)
Re: AlphaGeometry: An Olympiad-level AI system for geometry
#37I'm a big fan of approaches like this, that combine deep learning / newer techniques w/ "GOFAI" approaches.
Re: AlphaGeometry: An Olympiad-level AI system for geometry
#38It looks like there's some interesting works to connect ML with symbolic reasoning (or searching). I'm closer to layman in this area but IIUC the latter is known to be rife with yet-to-be-understood heuristics to prune out the solution space and ML models are pretty good at this area.
I'm not in a position to suggest what needs to happen to further push the boundary, but in my impression it looks like the current significant blocker is that we don't really have a way to construct a self-feedback loop structure that consistently iterates and improves the model from its own output. If this can be done properly, we may see something incredible in a few years.
Re: AlphaGeometry: An Olympiad-level AI system for geometry
#39I suppose we can now replace high schoolers.
and their teachers
Re: AlphaGeometry: An Olympiad-level AI system for geometry
#40Earlier quoted context omitted.
It's not tiny, this is a quite normal size outside the field of LLMs, e.g. normal-sized language models, or also translation models, or acoustic models. Some people even would call this large.
It's tiny by the standards of transformers, pretty sure most transformers trained (across all domains) are larger than this
No. Where do you have this from?
Looking at NeurIPS 2023:
https://openreview.net/group?id=NeurIPS.cc/2023/Conference#t...
Some random spotlight papers:
- https://openreview.net/pdf?id=YkBDJWerKg: Transformer (VPT) with 248M parameters
- https://openreview.net/pdf?id=CAF4CnUblx: Vit-B/16 with 86M parameters
- https://openreview.net/pdf?id=3PjCt4kmRx: Transformer with 282M parameters
Also, in my field (speech recognition, machine translation, language modeling), all using Transformer variants, this is a pretty normal model size.