okintheory

"A strict complexity gap between gradient-based and derivative-free Lipschitz convex optimization" has been known for decades. It's covered in standard textbooks like Nesterov's "Introductory lectures on convex optimization". That AI result is about establishing the gap in a fairly niche accuracy regime.

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'.

How could any European govt use MS after Trump ordered MS to sanction an ICC prosecutor and MS complied? I imagine they're all trying to walk away

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).

I also can't tell whether Stanford is joking, but the notion that he's a good fit for the job of biology professor is definitely funny!

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.

It's right there, in the press release:

>>> 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," <<<