Over the last year, discussions around AI performance in the International Mathematical Olympiad (IMO) have surged, particularly as proponents of AI safety use DeepMind’s results to bolster claims of advancing LLM capabilities. However, these models are notably task-specific and only accept formal language, unlike more versatile multi-modal models. A recent paper, “Proof or Bluff,” indicated that state-of-the-art LLMs struggled significantly on comparable tasks, with none achieving scores above 5%. This finding contrasts with the optimism surrounding DeepMind’s achievements, leading to skepticism about whether such results can genuinely forecast general AI progress. The author questions the conflation between specialized and general AI models, suggesting that the acclaim for DeepMind may serve as convenient support for certain narratives within the AI safety community. They express appreciation for the authors of “Proof or Bluff,” emphasizing the need for rigorous data in assessing AI model performance and its implications for future developments.
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