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)
These problems are literally already solved? Of course, the IMO problem designers make sure the problems have solutions before the use them. That's very different than math research, where it's not known in advance what the answer is, or even that there is good answer.
Interestingly, the same guy also works on making 'theory-only' algorithms work well in practice [1]. But, it seems like that takes another 20 years -- [1] is building on a theory breakthrough from 2004 [2], but these algorithms are only starting to work in practice in 2024, IIUC. I guess that means there's hope for practical min-cost flow algorithms in 2044.
I'm not arguing that X/Twitter is doing something that prevents it from being protected by section 230 as it exists. But, when Congress granted these protections, I think they had in mind things like forum moderation to remove hate speech, not selectively censoring journalists that write critically of a company. So one could rethink whether companies that censor journalists should be protected.
When Ken Klippenstein obtained footage of an FSD Tesla causing a major accident [1] and wrote about it, he got blocked from being found through X/Twitter search [2]. I remember verifying the search block on my phone at the time.
If I were a US lawmaker, I'd take a moment to reflect on whether a company such as X/Twitter, which actively censors journalists to protect the financial interests of its owners, should really be granted Section 230 protection.
Also, just to add, 'quantumalgorithmszoo.org' is quite out of date: For example, they claim a polynomial speed-up for network flow, but that's based on the SOTA in 2007. These days, there's an almost-linear time classical algorithm [A], and no matching quantum algorithm (and also the idea that you would want to use a quantum computer to shave a factor n^(0.0001) is ridiculous). Now, all these statements are only about asymptotics, but don't get me started on practicalities: The idea that in the next 50 years you would use a quantum computer for any problem with only a polynomial speed-up is silly.