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@math_rachel

"Long-term innovation depends not only on optimization for current objectives, but on the continued viability of ideas whose value is not yet legible" Thought-provoking ICML paper on limitations & risks of over-relying on AI benchmarks 1/ https://t.co/dNmvRXtsis

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        "ext_alt_text": "Position: There are futures that benchmark-driven AI cannot see\nSobhan Lotfi ⋅ Ava Iranmanesh ⋅ Lachin Naghashyar ⋅ Ali Shirali ⋅ Fateme Haredasht ⋅ Sanmi Koyejo ⋅ Phil Torr ⋅ Yong Suk Lee ⋅ Fazl Barez ⋅ Joel Lehman ⋅ Peter Norvig ⋅ Arvind Narayanan\n\nBreakthroughs often come from ideas we could not have predicted in advance. In biology, this is called exaptation: traits evolved for one function become decisive for another. Scientific progress works similarly, but only if ideas survive periods when they appear uncompetitive by current metrics. This position paper argues that AI's benchmark-centered selection environment, while successful at bypassing complex debates about the nature of intelligence, taxes exaptation. When one selection rule dominates, ideas that do not fit it have nowhere to persist. The cost grows acute as the field shifts from asking can machines exhibit intelligent behavior? to asking can machines exhibit intelligent behavior such that they are aligned, interpretable",
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