This was clearly written by someone with too little exposure to history and (comparably) too much to academic economics. No one else could think Coase belongs on such a list and forget Orsted/Faraday/Maxwell (initially...). And if you think John Locke did something important beyond adding philosophical veneer to capitalism as it was already practiced, you need to read Meiksins Wood's 'The Origin of Capitalism'.
I don't think that's a great example. If Kahneman claimed not to be susceptible, it would have greatly undermined his claims about the universality of these phenomena: many other people would presumably also not be susceptible.
I settle this question for myself every month: I try asking ChatGPT and Gemini for help, but in my domains it fails miserably at anything that looks new. But, YMMV, that's just the experience of one professional mathematician.
I think you're misunderstanding the point this paper is trying to make. They're interested in trying to distinguish whether AI is capable of solving new math problems or only capable of identifying existing solutions in the literature. Distinguishing these two is difficult, because self-contained math problems that are easy enough for LLMs to address (e.g. minor Erdos-problems) may have been solved already as subcomponents of other work, without this widely known. So when an AI makes progress on such an Erdos problem, we don't know if it had a new idea, or correctly identified an existing but obscure answer. This issue has been dogging the claims of AI solving Erdos problems.
Instead, here you get questions that extremely famous mathematicians (Hairer, Spielman) are telling you (a) are solvable in <5 pages (b) do not have known solutions in the literature. This means that solutions from AI to these problems would perhaps give a clearer signal on what AI is doing, when it works on research math.
You're wrong. The mistake could have been unfixable. That happens quite frequently (see: countless retracted claimed proofs of major results by professional mathematicians).
The thought police already arrived, see Columbia grant cancellations and Mahmoud Khalil [1].
[1] "Khalil is a “threat to the foreign policy and national security interests of the United States,” said the official, noting that this calculation was the driving force behind the arrest. “The allegation here is not that he was breaking the law,” said the official." https://www.thefp.com/p/the-ice-detention-of-a-columbia-stud...
It's nice to live in a world where actions have consequences. When the media coverage got too much, Marc Tessier-Lavigne finally had to resign as president of Stanford, so he could focus on his job as a Stanford professor.
Interestingly, the asymptotically fastest known algorithm for minimum weight bipartite matching [A] uses an interior point method, which means it's also doing Riemannian optimization in some sense.
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Jonathan Friedman, Sy Syms director of PEN America’s U.S. Free Expression programs, said:
“The irony cannot be lost here: government officials have used their positions to muscle out a scholar of authoritarianism from a prestigious lecture,"
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But, the starting point of Neural Networks in the ML/AI sense, is cybernetics + Rosenblatt's perceptron, research done mathematicians (who became early computer scientists)
No. That's what's happening in some American unions (not all!), but it's not true in Denmark (for any union!), by the sound of it from the above post, in Sweden.