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AI knows all the right answers. That's the problem, says Hugging Face co-founder Thomas Wolf. Wolf argues that today's AI models ace tests but can't think like scientists. They've memorized human knowledge but can't question it. This flaw could slow scientific progress.
He speaks from experience. Wolf went from top student to MIT researcher, where he learned that getting perfect grades didn't help him discover new ideas. "I was good at predicting exam questions but hit a wall with original research," he writes.
History backs this up. Einstein failed his entrance exam. Teachers called Edison "addled." Critics dismissed Nobel winner Barbara McClintock's "weird thinking." Breaking scientific ground often means breaking academic rules.
AI companies test their models on complex questions with clear answers. But science moves forward through questions that challenge accepted facts. Think of Copernicus arguing that Earth orbits the Sun when everyone believed otherwise.
Current AI models work like perfect students who never question the textbook. They connect existing facts but don't ask why those facts might be wrong. OpenAI's Sam Altman promises these systems will speed up scientific discovery. Wolf disagrees.
Real breakthroughs come from asking "What if everyone is wrong?" That's how Jennifer Doudna and Emmanuelle Charpentier turned bacterial defense systems into gene-editing tools, winning a Nobel Prize.
The tech industry has built helpful digital assistants that never challenge authority. But science needs rebels who question everything - including their own training.
Wolf suggests new ways to measure AI progress. Stop testing how well systems follow rules. Start testing how they:
"We need a B student who sees what everyone else missed," Wolf says. Not an A+ student who knows all the right answers.
The solution? Build systems that think differently, not perfectly.
Why this matters:
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