The computer science collaboration will understand and explain a result concerning the Exponential Time Hypothesis (ETH), a longstanding conjecture about the computational complexity of Boolean formula satisfiability, a problem deeply related to the P vs. NP problem. Surprisingly, our system has recently proved that the ETH is false, a result that was subsequently verified in Lean. The refutation of the ETH reopens questions about how much faster these tasks could become, and may help guide the search for better algorithms. Finding such algorithms, and turning theoretical advances into practical improvements, will require significant further research. Our system found its natural language disproof for the ETH we reached out to to @rrwilliams. Ryan will form a collaboration with Russell Impagliazzo and Ramamohan Paturi to verify and shepherd this result. Impagliazzo and Paturi, along with Francis Zane, are the original proposers of the Exponential Time Hypothesis, and Williams has refuted strong versions of the ETH, used ETH for hardness results, and connected algorithms and lower bounds. This group asked us to make all raw data of the results public to facilitate independent verification by the community, which we have done by posting the natural language proof and Lean formalization to our GitHub repo. The group also invites the community to help organize an open examination of the ETH claim and to discuss the broader questions that AI-generated breakthroughs raise about verification, access, credit, and the future of research.

A computer-science degree is no longer a golden ticket. What’s the alternative? Get really good at the stuff machines are lousy at, writes columnist Callum Borchers. 🔗

A computer-science degree is no longer a golden ticket. What’s the alternative? Get really good at the stuff machines are lousy at, writes columnist Callum Borchers. 🔗

A computer-science degree is no longer a golden ticket. What’s the alternative? Get really good at the stuff machines are lousy at, writes columnist Callum Borchers. 🔗

🚨 A computer science hypothesis underpinning hundreds of research papers is being challenged by AI. OpenAI announced that its AI found an algorithm that refutes the Exponential Time Hypothesis (ETH), which concerns how the difficulty of solving certain computational problems grows with their size. The company says the result was obtained on October 16 and formally verified using Lean the following day. The paper and proof have been published on GitHub. The hypothesis’s originators, Russell Impagliazzo and Ramamohan Paturi, will lead the review alongside MIT’s Ryan Williams. If confirmed, the finding would require conclusions that rely on ETH to be reassessed. Whether it translates into practical algorithmic speedups remains an open research question. The company also reported proofs of the Kakeya conjecture in all dimensions and the “existence” component of the Yang–Mills Millennium Prize Problem. The latter has not yet been released publicly and will undergo verification led by Nobel laureate David Gross. These reported breakthroughs still await further independent scrutiny from the academic community. #AI# #OpenAI# #oncoin# #Mathematics#

where did computer science go wrong for us to accept “8 emojis” as a *headline feature* of an OS upgrade? Ternus, pls rescue Apple from decadence. maybe the next headline should be, you know, *actually useful* on-device AI?