2026-09-08 20:32 UTC
Replies (8)
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@tao@mathstodon.xyz 2026-09-08 20:32
In short, the indiscriminate use of powerful solution-extraction tools can achieve the immediate short-term goal of solving problems at hand, but at the cost of sustaining the ecosystem for the next wave of progress, or in understanding the progress already obtained. While it may be technically infeasible to completely prohibit the use of automated tools to perform indiscriminate solution extraction, I believe that we can still designate many classes of problems as being desirous of a careful analysis that not only solves the problem, but identifies insights from the solution process, and learn more about the difficulty landscape for nearby problems, and for which raw solutions without such analysis would be of negligible or even negative value for these purposes. This is analogous to how a modern food donation drive no longer accepts arbitrary contributions even when they are verified to be technically edible, but instead maintains explicit and socially accepted standards on what level of contributions are actually sought. (4/4)
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@poetaster@mastodon.gamedev.place 2026-09-08 20:48
@tao@mathstodon.xyz ah, who opened Pandora's box?
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@wordmark@mas.to 2026-09-08 20:55
@tao@mathstodon.xyz #ai still FAILS at tasks where there is not sufficient data, such as #tetris in #bash
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@spicyricecaker@fosstodon.org 2026-09-08 21:48
@tao@mathstodon.xyz Reminds me of Janko Meteldon's post on The Cognitive Dark Forest...
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@PhilGopon@mountains.social 2026-09-08 21:55
@tao@mathstodon.xyz I remember having to do a literature data compilation for part of my PhD, that took the better part of 4 months. Today I recreated the same survey in less than 20 minutes with Claude, and I don't really think that is a good thing. Data compilations used to force you to read and think about the other work, and in the end you had something you understood and could nicely write up to help others understand. The speed of publishing is outpacing the ability of ppl to read it. It was a problem before AI and now it is just out of control. AI to create papers that only AI will actually read, is where we are at now. All this at the cost of the exponential increase in energy and water resources, where is the benefit?
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@TaliaRinger@mathstodon.xyz 2026-09-08 22:25
@tao@mathstodon.xyz yeah this is the most depressing part to me
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@aceroe@mathstodon.xyz 2026-09-09 02:00
@tao@mathstodon.xyz (1/4) I don’t really agree with the idea that “good problems are being consumed by AI like a non-renewable resource.” Problems are not the whole of mathematics. An open problem is better understood as an entrance, not as mathematics itself. What truly matters is what structures we come to understand through that entrance, what mechanisms we discover, what new connections we uncover, and what new questions are generated from them. If a problem is valuable only while it remains unsolved, and its research value rapidly disappears once an answer is found, then perhaps what is actually scarce is not mathematics, but difficulty itself. AI is certainly flattening the difficulty landscape very quickly. But: Flattening the difficulty landscape is not the same as flattening the mathematical landscape. For a long time, we have often used questions like “where are people stuck?” and “how many years have mathematicians spent on this?” as proxies for importance. If AI makes that way of navigating mathematics increasingly unreliable, that only means we need better criteria for mathematical value. It does not mean mathematics itself is being depleted.
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@itgrrl@infosec.exchange 2026-09-09 06:58
@tao@mathstodon.xyz I think the same thing could happen more broadly too 👇 https://infosec.exchange/@itgrrl/117224205365020276