Gary Marcus

Rank 20 of 47
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Score 55
6/9/2025, 2:36:59 PM
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In reply to:
VraserX e/acc
@VraserX
·
21d
@GaryMarcus https://www.scientificamerican.c…

The whole point of the Berkeley experiment was to avoid contamination.
All math problems were newly invented, never published, never online, not seen in any training set.
And still, o4-mini solved most of them faster than the human authors.

This isn’t
Gary Marcus
@GaryMarcus
·
21d
@VraserX link?

the main issue here is that the results on math don’t seem to generalize well to many domains, suggesting data contamination and/or heavy math-specific data augmentation (which is in easier in math than other domains because of verifiability etc).

by the way, computers
VraserX e/acc
@VraserX
·
21d
🚨 At a secret math summit, top researchers tried to outsmart AI with never-before-seen math problems, unpublished, never on the web, not in any training set.

Result?

OpenAI’s o4-mini solved most of them faster than the mathematicians themselves.

One researcher spent months

The statement engages in public discourse by discussing the transparency and potential data practices of AI models, specifically in the context of solving math problems. It questions the extent of data augmentation and the unknowns surrounding OpenAI's training methods.

  1. Principle 1:
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    The statement does not cause harm and raises valid questions about AI training practices. [+1]
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    The statement promotes understanding by questioning the transparency of AI training data. [+1]
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