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The AI revolution in math has arrived
- claysmithr 6mo agoI wonder when AI will be able to discern the passage of time
- maplethorpe 6mo agoAltman has estimated one year until ChatGPT is capable of measuring time passed. https://tech.yahoo.com/ai/chatgpt/articles/chatgpt-fails-miserably-set-timer-233000429.html https://tech.yahoo.com/ai/chatgpt/articles/chatgpt-fails-mis...
- ambicapter 6mo agoSounds like Musk setting deadlines for Mars landings.
- keyle 6mo agoIt's so hard to predict you know, these planets keep moving...
- VladVladikoff 6mo agoCan’t tell if you are being sarcastic but Altman’s whole job is to make bullshit near future predictions about rapid development of AI in the public.
- random__duck 6mo agoThankyou for stating the obvious, for some reason we need to repeat this. ^^;
- viccis 6mo agoThere's no need to "estimate" it. "Time" is not something built into training and sampling a generative distribution. He might as well have told you your Naive Bayes email filters will measure time passed.
- x-complexity 6mo agoTaking the task at face value: - 1 week to prototype: The tool + its accompanying JS sandbox + System prompt updates + context injection - 11 months of public testing to go through i18n + a11y edge cases & fix them
- 1970-01-01 6mo agoIt already does time in prompt-blocks. It knows time is linear and what just happened, what happened before that, and what happened before that.
- claysmithr 6mo agoWhen I tried to use it as an AI CEO and Life Coach, it never was able to discern time passing, what I've already done, what needed to be done. It just said the same stuff over and over, stuff I've already done. That and it's kind of stuck in the era it was trained in. If it felt time passing like a human maybe it would be conscious? Nevertheless not having a sense of time makes it really bad at planning anything. I used Gemini Pro.
- Buttons840 6mo agoCan't you just give it the time in each prompt? Would that work? I've seen this mentioned a few times though, so I think maybe it's more complicated than this?
- themafia 6mo agoThere are several high value prizes for mathematical research. Let me know when an "AI" has earned one of them. Otherwise: > When Ryu asked ChatGPT, “it kept giving me incorrect proofs,” [...] he would check its answers, keep the correct parts, and feed them back into the model So you had a conversational calculator being operated by an actual domain expert. > With ChatGPT, I felt like I was covering a lot of ground very rapidly There's no way to convert that feeling into a measurement of any actual value and we happen to know that domain experts are surprisingly easy to fool when outside of their own domains.
- gxs 6mo agoWow that was your takeaway? > “2025 was the year when AI really started being useful for many different tasks,” said Terence Tao I think I’ll go out on a limb and agree with Terrence Tao, I think the dude is well known in the math community, or something
- noobermin 6mo agoIf anything his simping for AI models makes me more suspect of him than I ever was because my own eyes show me their limits.
- jryle70 6mo agoAny chance your eyes are wrong? Or only people who disagree with you are.
- deleted 6mo ago[deleted]
- p1dda 6mo agoI think he means useful for mathematicians getting paid shilling for AI models
- themafia 6mo ago> go out on a limb and agree with Terrence Tao Is AI his specialty? > I think the dude is well known in the math community, or something I believe this is called "appeal to authority." Which is why, instead of disagreeing with him, I suggested a more cogent endpoint that could be used to establish the facts the article's title suggests.
- dogscatstrees 6mo ago> As they did so, they also learned how to improve the prompts they gave AlphaEvolve. One key takeaway: The model seemed to benefit from encouragement. It worked better “when we were prompting with some positive reinforcement to the LLM,” Gómez-Serrano said. “Like saying ‘You can do this’ — this seemed to help. This is interesting. We don’t know why.” Four top logical people in the world are acknowledging this. It is mind-blowing and we don't know why.
- brookst 6mo agoDo we know why it works for humans? Models are trained on human outputs. It’s not super surprising to me that inputs following encouraging patterns product better results outputs; much of the training material reflects that.
- gxs 6mo agoIf I had to wager a lazy, armchair guess, I think it forces it to think harder/longer The answer is probably more straightforward than we think, e.g. “the user thinks I can do this so I better make sure I didn’t miss anything”
- latentsea 6mo ago> Do we know why it works for humans? Try to figure it out. You can do it.
- CivBase 6mo agoThis seems pretty obvious, no? It's pattern matching on training material. There is almost certainly an overlap between positivity and success in the training material. Positive prompts cause the pattern matching to weight towards positivity and therefor more successful material.
- lamasery 6mo agoThe training or system prompts have shoved the probabilities toward a space that tends to select “halt” sooner. You need to drag the probability weights around until they are less likely to reach “halt” so soon. Nice language often sorta does this for whatever model(s) they looked at, and is also something people are likely to try. Probably lots and lots of nonsense token combos would work even better, but who’s gonna try sticking “gerontocratic green giant giraffes” on the end of their prompts to see if it helps? Positive or negative language likely also prevents pulling the probabilities away from the correct topic, being so generic a thing. The above suggestion might only be ultra-effective if the topic is catalytic converters, for some reason, and push the thing into generating tokens about giraffes otherwise. How would you ever discover the dozens or thousands of more-effective but only-sometimes nonsense token combos? You’d need automation and a lot of brute force, or some better way to analyze the LLM’s database.
- norejisace 6mo agoInteresting development. It feels like AI is getting much better at symbolic reasoning, not just pattern recognition.
- sparin9 6mo ago[dead]
- sm0ss117 6mo agoMathematics seems like the ideal candidate for AIs to achieve absurd results. It's a purely abstract grammar with true auto-verifiability. Even SWE has the requirement of interacting with real physical things. In math there's no external feedback required, you're solely bounded by the rate and quality of token generation.
- drivebyhooting 6mo agoThis misses the mark on at least two accounts: 1. Proofs without human understanding have less value for mathematicians 2. At least for now, interestingness depends on human judgment. It is subjective and not as verifiable.
- dyauspitr 6mo agoEvery new mathematician that comes along doesn’t know everything that has come before him. He needs to go learn all the math that his predecessors did. I don’t see how an LLM coming up with these proofs changes that.
- streb-lo 6mo agoBecause the problem space is basically infinite. If a person is working on a problem, its probably interesting to at least one person. Randomly walking through the problem space might be interesting, but I don't know how the signal will fare against other humans.
- sm0ss117 6mo ago1. The four color theorem is a useful case study, for which the original proof was validated and 400 pages long. My prediction is that the first couple waves of proofs will be hard enough that a layman couldn't produce them, but simple enough that experts can verify them. Over time the most advanced proofs will get more and more complicated until humans can no longer verify them, this process could happen over the course of a few month or could take literally hundreds of years. 2. Especially early on the overwhelming majority of the proofs are likely to be uninteresting and more novel just because actually producing them would take expert time that's better spent elsewhere. That being said, as above over time I expect the interestingness of proofs to go up until they eventually regularly produce interesting proofs. The vast majority of proofs are likely to maintain their position as of no interest to humans for the simple reason that the vast majority of proofs are of no interest to humans. In neither case will I make any particular guesses about a timeline beyond it seems like the way things will go.
- bgirard 6mo agoLast week I got together with my math alumni friend. We cracked some beers, we chatted with voice mode ChatGPT and toyed around with Collatz Conjecture and we sent some prompt to a coding agent to build visualizations and simulation. It was a lot of fun directing these agents while we bounced off ideas and the models could explore them. I think with the right problem and the right agentic loop it’s clear to me improvements will speed up.
- drakenot 6mo agoI think voice mode uses weaker models, just an FYI relative to the SOTA
- scrollop 6mo agoDefinitely, seems like gpt 3
- SOLAR_FIELDS 6mo agoCan get around this with a local STT model and use text input but UX is probably clunkier
- pxc 6mo agoThe bigger problem for me is that the realtime voice modes lack tool use, so they can't look anything up or do anything. Model strength definitely also matters, but even dumb models can be helpful when they can look things up and try things out. And smart models that don't do those things kinda suck.
- yabutlivnWoods 6mo agoWe can define a Dyson Sphere in math. We cannot build one. AI outputting axiomatically valid syntax isn't going to be all that useful. It's possible to generate all axiomatically correct math with a for loop until the machine OOMs Physics is not math and math is not physics.
- djsjajah 6mo agoYou just failed the Turing test.
- goatlover 6mo agoThe Turing test just failed you. I'll go one better, physics isn't reality, it's a model of reality utilizing math.
- johnthescott 6mo agowe only perceive the past.
- dugidugout 6mo agoAnd I'll go one better, you haven't said anything here at all, you've just left a representation of what you understand to be saying.
- keyle 6mo agoMaybe he passed the Turing test with 88.2% which is 1.8% higher than the competition.
- yabutlivnWoods 6mo agoFortunately for me equivalents to Turing exist: https://en.wikipedia.org/wiki/Turing_machine_equivalents https://en.wikipedia.org/wiki/Turing_machine_equivalents
- djsjajah 6mo ago
- viccis 6mo agoWhat is the telos for AI chewing around the edges of pure math problems? Does AI care about math?
- 4ajsH17 6mo ago[flagged]
- Wissenschafter 6mo agoMore neo-luddite nonsense.
- homarp 6mo agohttps://www.quantamagazine.org/about/ https://www.quantamagazine.org/about/ says "launched by the Simons Foundation in 2012" and https://www.simonsfoundation.org/about/ https://www.simonsfoundation.org/about/ has "Since its founding in 1994 by Jim and Marilyn Simons" https://en.wikipedia.org/wiki/Jim_Simons https://en.wikipedia.org/wiki/Jim_Simons explains how Jim Simons got rich. The book 'The Man Who Solved the Market' - https://www.gregoryzuckerman.com/the-books/the-man-who-solved-the-market/ https://www.gregoryzuckerman.com/the-books/the-man-who-solve... is a nice read. HN discussion on a review of the book - https://news.ycombinator.com/item?id=29392041 https://news.ycombinator.com/item?id=29392041
- 440bx 6mo agoBoring mathematical reality here. This is nice and all that but as a (part time) corporate mathematician, I'd like an AI that organises conference trips, picks the best accommodation and food and gaslights the execs into approving it. Then fixes the perpetually broken coffee machine. Everything else for me starts on paper and is mostly undergrad level problems which I need to do by hand to keep my brain going for when I actually might need it one day. And with the geopolitical instability out there at the moment I'm not that willing to put my eggs into the basket.
- pyuser583 6mo agoI just want it to cook and clean.
- doubledamio 6mo agoAll these overly optimistic articles about AI solving maths problems are very annoying. Can we agree that maths is not about solving problems, but about understanding them by developing a language and the conditions for new insights? It is misleading because GPTs do provide easy access to new information, but they do not deepen understanding. I think AI-assisted research will likely have a very negative net impact on mathematics in the long run by lowering the average level of understanding within the community. Also, research directions are influenced by what people can solve, and this will slowly shift research toward purely algebraic/symbolic manipulations that mathematicians no longer fully keep track of.
- somethingsome 6mo agoIt's highly dependent of why you use it. For me a problem looks like 'a step in the proof I'm not familiar with', and I use LLMs to help me undersand it deeply. Make visualizations, check some difficult step, do parallels with something else I know,... I don't really care that the llm could 'solve the global problem I'm facing'. I use it more for insights on smaller parts to be able to go through difficult steps and teach me areas I'm not familiar with. The more the llm is capable of doing complicated proofs by itself, the more it is trustworthy to help me without making errors that I could miss in unknown Maths areas.
- mif 6mo ago[flagged]