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OpenAI said its AI models have produced mathematical proofs that refute a longstanding computer science hypothesis and establish results in mathematics and theoretical physics, as the company brings together independent academic experts to verify the findings and assess their implications.
The company announced three results covering harmonic analysis, computational complexity and quantum field theory, including a proof that refutes the Exponential Time Hypothesis (ETH), a widely used conjecture about the computational effort required to solve certain problems. Surprisingly, our system has recently proved that the ETH is false, a result that was subsequently verified in Lean, OpenAI said. The ETH has been used in hundreds of research papers to establish limits on how efficiently computational problems can be solved across fields such as graph theory, computational biology, databases and computational geometry. OpenAI said the finding reopens questions about how much faster these tasks could become, and may help guide the search for better algorithms. However, it cautioned that finding such algorithms, and turning theoretical advances into practical improvements, will require significant further research. The company said its models had also produced a proof of the local smoothing conjecture in harmonic analysis across all dimensions. According to OpenAI, this also resolves the Kakeya conjecture and Stein's restriction conjecture in all dimensions. In theoretical physics, OpenAI said its work provides a mathematically rigorous existence proof for certain interacting quantum field theories in four dimensions, including quantum chromodynamics (QCD), which describes the interactions of particles such as quarks and gluons through the strong force. The work also includes a proof concerning the existence of pure Yang-Mills theory, part of a longstanding mathematics problem. OpenAI is adopting different approaches to release the findings, depending on recommendations from external academic experts. For the computer science result, the group will make the natural-language proof and its Lean formalisation publicly available for independent examination. For the physics result, the group has decided to prepare its own account before releasing the AI-produced materials. The company said the collaborations would follow principles of transparency, recognition and independence, with participation voluntary and unpaid. Researchers will be free to discuss the work and publish their conclusions. We do not yet know which of these three approaches will be most useful, and we welcome feedback on what works and what we can improve, OpenAI said. The company also said it had committed to funding a conference to examine the implications of the computer science finding and was supporting research through a USD 5 million contribution to the Simons Foundation. OpenAI said it hoped the results would mark the beginning of a shift towards greater use of AI in scientific discovery, while emphasising the need for independent researchers to examine and explain the findings. (ANI)
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