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The article critiques the prevailing "scale is all you need" paradigm in AI development by contrasting it with historical examples where intelligence alone was insufficient for major breakthroughs, such as the Manhattan Project. It argues that current assumptions about AI's exponential growth and autonomous problem-solving capabilities overlook critical resource constraints and the distinction between raw computational power and true scientific insight. This perspective challenges the foundational beliefs driving both the optimism around curing diseases and the fears of rogue AI systems.

Sources coveringGDELT, LessWrong

What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

The AI folks dont seem to understand intelligence

GDELTSeptember 24, 20265 min read

Just three years after OpenAI said its models were good at grade-school math, the company announced they had solved the Navier-Stokes Millennium Prize problem in 88 hours, something that had stumped humanity’s greatest mathematical minds since the 1930s. Project that trend forward and you see why OpenAI Chief Executive Officer Sam Altman thinks AI will cure all diseases - and why people are afraid that a rogue AI might harm humanity. Santa Fe Institute scientists Melanie Mitchell and David Krakauer described AI’s mantra as “Scale is all you need.” What that means is the bigger the training sets and the more computer you have, the…

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