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The article critiques the prevailing "scale is all you need" paradigm in AI development, arguing that raw computational power and data volume do not equate to general intelligence or the ability to solve complex physical problems like curing cancer. It contrasts this modern assumption with historical precedents, such as the Manhattan Project, where scientific genius was only one component of a massive logistical and industrial effort required for success. This perspective challenges the foundational beliefs driving current optimism about AI's exponential growth and its potential to autonomously control or create successors.
The AI Folks Dont Seem to Understand Intelligence
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 better AI becomes.…
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