17 connected korrents · 13 moments from 15 Jul 2021 to 31 Jul 2026.
At the centre
An outsider could have disproved the Erdős unit-distance conjecture with a public model, because nobody had tried spending $100,000 of compute on one question.
Last stated 26 Jun 2026 · 4 months ago
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Noam Brown
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Similar wording
The twin prime conjecture is certainly true, the random model gives overwhelming odds of it, and he just cannot prove it.
Last stated 14 Jun 2025 · a year ago
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Terence Tao
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Similar wording
AI learning to generate good conjectures will never show up as a benchmark being knocked down; it will show up as a shift in how mathematicians talk about the tools.
Last stated 30 Jun 2026 · 4 months ago
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Grant Sanderson
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If the Riemann hypothesis turned out to be false, prime-based cryptography would be abandoned very quickly, because one unknown pattern implies more.
Last stated 20 Mar 2026 · 7 months ago
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Terence Tao
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Similar wording
Models can prove monumental theorems and still have never written an essay worth reading.
Last stated 31 Jul 2026 · 3 months ago
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Patrick Collison
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The month in which AI one-shot solutions to open Erdős problems kept arriving has ended, and it was not for lack of people trying.
Last stated 20 Mar 2026 · 7 months ago
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Terence Tao
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Almost every Erdős problem AI has solved was one with no literature behind it, which is the median level of what these tools can do.
Last stated 20 Mar 2026 · 7 months ago
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Terence Tao
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Similar wording
If reasoning can be separated from facts, the frontier labs lose their advantage, because nobody needs a million-GPU cluster any more.
Last stated 19 Jun 2024 · 2 years ago
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Aravind Srinivas
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Similar wording
A model can make an existing algorithm a hundred times faster and still cannot invent a better one, however long you give it.
Last stated 26 Jun 2026 · 4 months ago
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Noam Brown
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Same subject: AI and science
A compound's AI origin story is not by itself a reason to expect it to do better in the clinic.
Last stated 13 May 2024 · 2 years ago
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Derek Lowe
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Same subject: AI and science
A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck.
Last stated 17 Sept 2024 · 2 years ago
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Alex Telford
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Same subject: AI and science
A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known.
Last stated 15 Jul 2021 · 5 years ago
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Demis Hassabis
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Same subject: protein structure prediction
Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics.
Last stated 15 Jul 2021 · 5 years ago
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Demis Hassabis
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Same subject: protein structure prediction
AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points.
Last stated 23 Nov 2025 · 11 months ago
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Elliot Hershberg
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Same subject: protein structure prediction
AI will not produce a big breakthrough in biological understanding any time soon, because biology has nothing like the clean, complete, high-quality data that made protein-structure prediction work.
Last stated 19 Sept 2024 · 2 years ago
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Derek Lowe
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Same subject: formal proof
A test checks a handful of examples; a proof settles an infinite state space, which is why formal methods can say things testing never can.
Last stated 22 Apr 2026 · 6 months ago
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Martin Kleppmann
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Same subject: formal proof
AI is making formal methods more popular without making them mainstream, moving them from about a tenth of a per cent of engineers to three tenths.
Last stated 29 Jul 2026 · 3 months ago
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Hillel Wayne
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Same subject: formal proof
Engineers are sceptical of formal methods because they were burned by CASE and UML, sold as miracle solutions and imposed on them regardless of fit.
Last stated 29 Jul 2026 · 3 months ago
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Hillel Wayne