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Researchers test whether AI models can rediscover Einstein's relativity from pre-1911 data

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Google DeepMind's Demis Hassabis proposed training a large language model only on scientific knowledge available before 1911 to see if it could independently derive general relativity, calling it a meaningful test for artificial general intelligence. Several research teams, including one led by Ido Kaminer at Technion, have since built such 'vintage' AI models restricted to historical data, but early results have exposed shortcomings in current systems rather than demonstrating breakthrough reasoning.

Why It Matters

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The experiment reframes the debate over AGI away from benchmark scores and toward a harder question: can AI generate genuinely novel scientific theories rather than just recombine existing knowledge. Early failures suggest today's models excel at pattern matching but struggle with the conceptual leaps that produced revolutionary physics, tempering claims about imminent superintelligent discovery.

Key Takeaways
Worth a Look

Einstein's "Relativity: The Special and General Theory" (book) — If you want to judge whether an AI could ever rediscover general relativity, start with Einstein's own popular exposition, written to explain the theory to non-specialists. It walks through the leap from special to general relativity in his own words, making it the perfect companion to debates about machine-made breakthroughs.

See Einstein's "Relativity: The Special and General Theory" (book) on Amazon → Affiliate link — we may earn a commission on purchases, at no extra cost to you. Product picked by AI based on this article; it is not a tested recommendation.

Source: nature.com — Ball, 2026-09-09

Published there as: “The Einstein test: what happens when AI tries to rediscover relativity?”

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