19 ms·

AI isn’t outthinking mathematicians, it’s out-remembering them

• danielrmay 2mo ago
  Choosing what to remember is my biggest challenge!
• user982 2mo ago
  Metacommentary: how did this post get to #5 on the front page with 1 upvote within 2 minutes of submission?
  • LoganDark 2mo ago
    How fast the upvote happened?
  • dgellow 2mo ago
    That’s how HN works, I had that multiple times over the years with my own submissions. Sometimes it gets picked up quickly, sometimes not. A post can also down rank very, very fast. It depends a lot on the level of engagement and the type of engagement
  • grebc 2mo ago
    Follow the money.
• d--b 2mo ago
  It is obvious that super intelligence comes from more working memory. It is the scary thing actually. Cause once AI makes arguments that require a working memory of hundred items, then we as humans will have no way of understanding the arguments… We can decompose and write things but only up to a point. when Ai can have a working memory that spans hundreds of books, we are necessarily going to have to trust the system.
  • jacquesm 2mo ago
    We offload working memory to paper if we want to understand something that does not fit into the regular meat bits.
  • logicchains 2mo ago
    >Cause once AI makes arguments that require a working memory of hundred items, then we as humans will have no way of understanding the arguments… That doesn't follow. We could still understand it just by studying it and committing it all to long-term memory, it just takes longer. And there's a hard cap on the working memory of LLMs, due to the quadratic scaling cost of the full attention layers that have proved unescapable for all SOTA LLMs.
  • flatline 2mo ago
    This is why we have hierarchies of abstraction. Pretty much every field of mathematics relies on constructing notations, models, and other tools to simplify things in a way that is verifiable. LLMs rely on the same basic technique, they can just pull from a wide variety of these abstractions at once. So far we've been able to understand their proofs just fine. Computer-assisted proofs in the past that relied on brute-force is where we have run into trouble. We cannot reason about millions of possibilities at once, and we had to trust that the computer program that analyzed them was correct, which is a really hard problem and leaves humans fairly unsatisfied. I think we are actually progressing in terms of understandability in computerized proofs.
  • tired-turtle 2mo ago
    “is obvious” -- that’s what my Russian math professor said in college before skipping the rest of a proof. But was it?
• a2ff6eeb0 2mo ago
  Does it matter? It's going to produce proofs far more intricate than humans can understand, outdoing humans and opening new frontiers. The age of humans comprehending things is coming to an end: our brains just won't have the capacity to make meaningful contributions to science, math, or technology.
  • jansport123 2mo ago
    That maybe true at some point, but i don't think we are there yet.
    • a2ff6eeb0 2mo ago
      Yeah, it's probably a few years out.
  • mettamage 2mo ago
    But apparently we can teach machines to do it for us
    • a2ff6eeb0 2mo ago
      Yeah. We can also teach machines to move hundreds of miles an hour, but we could never do it ourselves.
  • orphereus 2mo ago
    "The age of humans comprehending things is coming to an end" That's something AI companies would really want you to believe.
    • ianm218 2mo ago
      > That's something AI companies would really want you to believe. Why would I care what they want me to believe? Intuitively it would make sense that you can put math ability on a chart with a value for “general public” “smart high schooler” “smart undergrad” “smart PhD/ professional”. And you could place frontier AI somewhere on that chart over time from GPT 2 to now and see the trend. Then you’d have to consider that either you believe there is a fundamental limit that is below peak human mathematician level or there’s not.
• bewareofscams 2mo ago
  "It's not X, it's Y" hot take AI slop.
  • seeknotfind 2mo ago
    100%. Context is big for AI, but it's nothing compared to everything a human can learn. If you efficiently represent everything in context, it may be many papers, but if AI is actively working through proofs, it will quickly fill up. They're no denying AI is making strides, but pinning it to memory is an oversimplification.
  • mrob 2mo ago
    I don't have to open the article to be confident it's not worth reading. Anybody knowledgeable in the field should be familiar with AI writing tells and the message they send. It only takes a few seconds thought to transform the title into something like "AI beats mathematicians by out-remembering, not out-thinking." Regardless of whether the article is slop or not, I expect any competent writer to avoid slop phrasing in their titles. To do otherwise signals laziness.
• ComplexSystems 2mo ago
  It's also "out-brute forcing them." It just never gets tired. If a mathematician picks a research direction and spends a whole week on it and it doesn't pan out, they will likely be annoyed, need a break for a while, etc. This thing just does not ever get tired or discouraged or care; it's just onto the next thing until something ends up working.
  • thaumasiotes 2mo ago
    > If a mathematician picks a research direction and spends a whole week on it and it doesn't pan out, they will likely be annoyed, need a break for a while, etc. Your timelines are a bit unambitious. There's nobody expecting to make significant progress with a week of work.
    • nmstoker 2mo ago
      They were just illustrating their point, I wouldn't take that literally.
    • bananaflag 2mo ago
      > There's nobody expecting to make significant progress with a week of work. You underestimate my ADHD. Source: I am mathematician.
      • dijksterhuis 2mo ago
        Hell, I underestimate my own ADHD. Source: the post-it notes, ALL OF THEM.
  • EA-3167 2mo ago
    The key here is that it’s depending on the human inability to connect the sum of relevant knowledge, but said knowledge comes from humans. Theres going to be this field day of low-hanging fruit that ML can round up, but after that I suspect it will be in fits and starts as a “connection maker” rather than some proof producer.
• hparadiz 2mo ago
  Outside of math you can basically take the entire corpus of research papers on any topic and have the AI read all of it and provide an analysis cross referencing everything all at once. This applies to everyone and everything.
  • doc_ick 2mo ago
    Once you do that you can ask it a question and there’s a highly likely chance there’ll be a hallucination and the answer is wrong anyway.
• nick_pro7 2mo ago
  [flagged]
• mschuster91 2mo ago
  > But chunking does not eliminate the limit. It merely compresses the information. Yeah, as expected, an article about AI that's at the very least been polished using AI. For fucks sake we need an LLM flag to filter out slop.
• tipsytoad 2mo ago
  And the goalposts must move once again..
• orphereus 2mo ago
  C'mon AI companies, pivot to lawyers or doctors already. Trying to convince us that mathematics and software engineering are "solved" is getting very tiring. The pushback would probably be too much for the soon-to-be IPO-ed companies.
  • dgellow 2mo ago
    They can rely on compilers, solvers, theorem provers to validate the generated softwares and maths. That’s what makes it possible to iterate quickly in a loop and self correct. You cannot do that in soft industries like legal and medicine
    • orphereus 2mo ago
      That is not the point I was making. I am not talking about validating software or maths. It can generate stuff that is valid, but bad and incomprehensible.
      • dgellow 2mo ago
        What I’m saying is that AI labs are talking so much about software and maths because we already have tools that can say « it’s all good ». That makes it possible and worth it for them to spend 1 week of compute on a problem until the validator passes, then publish marketing pieces. You cannot do the same in medicine or laws (modulo some niche areas)
  • __natty__ 2mo ago
    They try to sell AI as lawyer or doctor replacements as well. But because it’s HackerNews we are biased towards our domains to see them more often.
    • AngryData 2mo ago
      Well also you can't just throw AI slop as doctors or lawyers advice and fail multiple times until you find the right answer. With code and math you can have failures 1000 times for every success and still get rewarded.
• Animats 2mo ago
  Yes. That's how LLMs do programming, mostly. It's also why LLMs don't need abstractions or parsimony as much as humans. They can work on something complicated without simplifying it first. This has major implications that haven't been fully realized yet. On the math side, there are long machine generated proofs. On the code side, there are high volumes of code with similar code not being folded into functions.
  • breadzeppelin__ 2mo ago
    I've been working on generating a large code base for the last couple of weeks. Finally got around to generating a sort of code-duplication report and have spent the last week just having it de-duplicating logic that had been strewn all over the place (eg 11 different functions all doing date math to add x days to a date). dozens of items that had each been similar functions duplicated numerous times. crazy. (opus-5-utracode)
    • greazy 2mo ago
      Can LLMs not do this for you? Or would they go too far?
      • chrz 2mo ago
        They add and add new code to the point when adding more is going to become very messy and then spagetti
        • greazy 2mo ago
          What I was suggesting is to ask the LLM to compare function usage across files to identify overlap/redundancy. The idea is to use the LLM to analyse the code base first before taking any action. I think I'm suggesting a centaur approach.
      • nomel 2mo ago
        No, the problem is they're still really dumb, and lack the ability to make logical connections that are obvious to us. "should I walk or drive to the carwash" being a very recent example of the larger problem.
• throw93949990 2mo ago
  Most mathematicians are quite simple creatures. I can do basic math, some derivations, but my bright days of solving differential equations are far gone! Computers are simply better at math now, like in chess or go!
• kardianos 2mo ago
  This is why education used to start with rote memorization. Functional intelligence isn't abstract, it is based on useful information you can quickly recall.
• LogicFailsMe 2mo ago
  What I'm looking forward to amidst all the negativity, fear, and loathing is for some 20something mathematician to outdo both humanity and machines by leaning hard into centauring to expand the frontiers of mathematics. Pretty much what I think the future will play out to be as well, but I don't think people are ready for that yet.
  • podgorniy 2mo ago
    People aren't ready to prove/disprove every one who access to LLM and claims has changed course of humanity
  • meroes 2mo ago
    Takes the rogue having access to the $$$$ models though.
    • LogicFailsMe 2mo ago
      That currently costs $20 a month, $200 per month if you need the premium extreme deluxe package subsidized by everyone else paying for it and not utilizing it. When I was in grad school I budgeted the equivalent of $3,000 per year to maintaining my compute hardware and that was a very long time ago. So I think it's doable.
• RRRA 2mo ago
  Exactly, it's making connection across vast set, not bringing the magic intuition. Has anyone tried feeding all of human knowledge to an LLM prior to Einstein's work and tried to have it reinvent physics?
  • mattnewton 2mo ago
    Where would you get enough pre internet tokens to train a near-frontier model?
• lowbloodsugar 2mo ago
  Specifically working memory. If you can’t hold enough concepts in your head then you can’t see how they all relate in one giant theory.
• tarekabi 2mo ago
  [flagged]
• shepardrtc 2mo ago
  [dead]
• philipfweiss 2mo ago
  One thing about human mathematicians is that they only publish positive results. Professors etc might have file drawers full of "negative results", but the incentives and bandwidth of human mathematicians makes publishing these useful results impossible. But AI agents have no such limitations and can publish and re-use negative traces easily. There have been some recent projects (https://www.theoremdb.org https://www.theoremdb.org) aimed at exploiting this fact. https://news.ycombinator.com/item?id=49227505 https://news.ycombinator.com/item?id=49227505 In general though, LLMs do not have the same limitations and incentives as human mathematicians, and the next year's tsunami of change will make this abundantly. clear.
  • tomrod 2mo ago
    What is next years tsunami of change? Asics?
    • tcp_handshaker 2mo ago
      They are always out of stock, and you can never find your size..
  • paulpauper 2mo ago
    Yeah, LLMs are great at generating negative results for math-related prompts "We scanned values {a,b,c} from 0-100 and no results" Great..too bad journals will not publish this. But good job, I guess. A negative result is only truly useful if it can be bounded, requiring an actual proof.
    • delusional 2mo ago
      > A negative result is only truly useful if it can be bounded, requiring an actual proof. That's at least true for current journals, since they're supposed to be read by actual humans. I suppose one could imagine a sort of "AI" pure data journal that just "publishes" (in actuality aggregates) any sort of partial result. This body of knowledge would be entirely useless to humans, but could serve as a sort of "computation cache" for these stochastic systems.
• keeda 2mo ago
  While TFA itself makes sense I disagree with the title and the conclusion. I would not consider referencing working memory during thinking as “remembering” but as a part of thinking itself. Working memory is the RAM to the much larger but higher latency indexed database that is our long-term memory. As such I would say AI is out-thinking us, even if in a brute force sort of way. I think where you could say it is out-remembering us is when it can contemplate the vast universe of patterns, gleaned from essentially all human disciplines, encoded in its weights, that may let it draw connections that a human could not, unless they just happen to be familiar with multiple disciplines. Which is why I think the analogy with Von Neumann / Einstein is also a bit off. From TFA it seems Von Neumann was more akin to what AI does, than Einstein. I don’t get the impression that it was Einstein’s memory but his ability to look at things from a radically different perspective. So far I don’t know that we can categorically say that LLMs can or cannot do that.
• cineticdaffodil 2mo ago
  [dead]
• dev_dan_2 2mo ago
  I love the term "Out-Remembering"! I have been trying to find a way to communicate that "intelligence", "creativity" and so on might be misleading about the true nature of LLMs, and they would better be described as genious "reproducers" as in, they are very capable at reproducing what they have already seen - and they are a bit less capable, but for many use cases still good enough, at reproducing a mix of concepts seen previously. This also nudges into how to use it best: By knowing where the "piles" of if training data are (i.e. when it comes to a CLI in rust, I just briefly describe the use cases, and I have a very high confidence the code will work exactly as intended by me since there will be a multitude of examples in the training data), one can predict where the LLM is likely to go wrong an prompt/guard accordingly. This skill grows with domain expertise, and is one of the many reasons LLMs can be (and probably should be) used to outsource busy work, but never understanding and learning. ("never" is a not meant literaly of course - I for one am glad that I do not have to wrap my head around CSS and other frontend topics and go straight to the topics that interest me most) "Out-Remembering" captures that perfectly, I feel. Also goes nice along with "asking it leading questions" as we know how to do in real live; if you want a person (LLM) to confess (produce output tokens) something, sometimes you do that by leading the interogation (chat, context) to where you think the truth lies.
• solid_fuel 2mo ago
  Aka - it’s a stochastic parrot with a good memory, for anyone still struggling to understand this. It should be obvious, imo, but some people seem to have trouble with the concept.
  • Eufrat 2mo ago
    I don’t understand why this is such big news. OpenAI has essentially made a bunch of marketing copy by gussying up an algorithm being given a near unlimited budget to stochastically permute through its lossy memory. I think the real scandal is that we are almost 3-4 years into this (I think the release of GPT 3.5 is a good marker of when this public frenzy started) and all we’ve seen is OpenAI and the other major AI frontier companies constantly retracting their preposterous claims every time. We appear to have reach a local maxima in that it has some value in places that tend to be a little easier to scope and limit (computer programming, mathematical proofs). So, given the actual useful economic value this has provided, does this justify the investments? I think we are approaching 1 trillion in CapEx for AI [0]. For context, I believe the annual GDP of Norway is $600 billion. [0] https://www.fool.com/research/ai-companies-spending-on-data-centers/ https://www.fool.com/research/ai-companies-spending-on-data-...
    • solid_fuel 2mo ago
      It's crazy, you see someone spin up 50 instances of chatGPT or gemini or whatever to tackle a math problem, it succeeds by brute forcing through a ton of existing theories to find one that extends the problem, and the takeaway is that this technology is magic and going to solve all of our problems. Whereas I see that and say - if we properly funded the sciences we could have had a bunch of grad students tackling that problem and found this application 20-30 years ago. Sure it's 'nice' that LLMs can fill in for people in brute force work like that but people are perfectly capable of doing that work and if we focused on properly staffing our research institutions we would achieve a lot more a lot faster. Instead this is obviously going to be used to replace staff and further reduce headcounts.
    • focxle 2mo ago
      [flagged]
• Razengan 2mo ago
  That's exactly what the strength of AI is, no? Reading all the world's knowledge and recalling it in an instant and seeing where it applies. Precisely perfect for replacing lawyers, if nothing else..
  • bogzz 2mo ago
    For instance, Opus 5 yesterday critiqued my resume and mentioned a ridiculous little detail-- my phone number area code didn't match the state in which I currently work (I know, I should anonymize but I couldn't be bothered). It failed to notice that I both worked in (previously) and studied in the state of my area code. This was just two pages of text, set to highest effort. Why would you want to replace your lawyer with a set of tensors that does not actually think and makes mistakes like this? Lawyers tend to get hired in high stakes situations. Why wouldn't you instead say that this would be a great tool for lawyers to use judiciously in researching precedents, etc? I don't understand what people are doing with models that makes them assign agency or intelligence to them. When I manage to forget the financial fuckery of the AI buildout and its implications, when I manage to forget scaremongering by loathsome CEOs, I still have the same fascination and excitement at the idea of LLMs as I did when I was playing with the GPT API prior to the release of ChatGPT. LLMs are, to me, truly amazing tech. It's so fascinating to me that they now DO have emergent properties that look at face value like reasoning and intelligence. But every day that I work with them, I am repeatedly clobbered over the head with the fact that they do NOT reason and are NOT intelligent. Why can't we be fascinated by emergent properties of intelligence without immediately jumping 10 steps into the future and, like a limit in calculus, assume that "this is it-- we're on the cusp of AGI"? To me, the fact that LLMs can combine existing ideas that people hadn't thought of combining in solving a novel problem is extremely cool. But my first thought is-- this is an amazing new tool for mathematicians and researchers. Instead, most everyone seems to jump the gun to the "humans are obsolete next year" conclusion.
    • Razengan 2mo ago
      > Why can't we be fascinated by emergent properties of intelligence without immediately jumping 10 steps into the future Because, when computers first came out, and filled entire rooms, people predicted they'd eventually shrink down to fit the crevice of your bum and everyone on the planet would have them When the first cars came out, people predicted that within 10 years, city streets would no longer be drowning in horse dung Do people who rant and rail against every new technology in its infancy, actively choose to forget history, or simply didn't learn about the many similar instances in the past?
• sehw 2mo ago
  Nobody IRL cares about nerds btw.
• anal_reactor 2mo ago
  As AI keeps improving, the definition of intelligence will keep changing in order to exclude it without explicitly saying so.
  • segmondy 2mo ago
    Well, some folks are going to keep trying to exclude it, but the reality is we are almost at the point even those folks would concede that they are not smarter than LLM. I suspect we will see then see more discourse about consciousness, morality, agency, emotions as being the most important part of intelligence.
• senfiaj 2mo ago
  This means some AI proofs might be impossible to comprehend by humans, right? I guess AI still lacks human intuition for many concepts, but AI might beat humans in narrow areas, such as discrete math and combinatorics.
• rms1000watt 2mo ago
  Mathematicians don’t forget things they haven’t learned. So is it that forgot history, or they didn’t learn parts of history to begin with?
• Zigurd 2mo ago
  There are plenty of high value endeavors where being a superhuman knowledge remixer is right on target. But even capturing all of the knowledge is proving elusive. I use coding agents. I think they're pretty good overall. They save me a lot of tedious coding. For example I probably wouldn't spend the time to implement native splash screens for all the build targets of a Flutter app, but I'll have the coding agent do it. Nevertheless, for all the time that we've had coding agents, it's still trivially easy to find the jagged edges of their training. For example, Gemini evidently doesn't know if the Xcode part of a Flutter tool chain is misconfigured. That's not exactly a Millennium Prize problem. But it is shaped wrong for a training set for a coding agent.
• fooker 2mo ago
  This misses a very important point. It’s not just about out remembering, it’s about breadth. Mathematicians are all about depth. It’s pretty much impossible to become an expert in more than one narrow field of mathematics. AI is happily applying techniques and abstractions across these silos.
  • soupspaces 2mo ago
    Euler would like a word. Maybe it's our institutions.
    • traes 2mo ago
      There was no depth to mathematics in Euler's time, he made the depth. He arguably didn't understand analysis on a technical level as well as a modern undergraduate; as I recall, he was confident that all smooth functions are analytic. He had incredible intuition and was able to make great progress by trusting it, but playing around with tools you don't understand stops being effective once a field is past its infancy.
• dj_axl 2mo ago
  So build a better proof/paper/technique search engine?
• hibikir 2mo ago
  I suspect that a lot about what we call being very intelligent is ultimately out-remembering people around us. I think of all the times in my software career when I did something that others considered very high performance, it either came down to either having more energy than others at tackling a problem they thought was more trouble than it was worth, or just bringing back random knowledge from previous jobs or self study, and being able to apply it to the problem at hand. I don't think I've had a truly original idea in my life. Combine A + B, when it's rare for people to know A and B at the same time. So from that perspective, what LLMs are doing is basically the same thing. Sometimes I am faster than the LLM because my context might be better organized, but it typically needs just a hint from me to steer itself correctly. It claims something is a memory leak, but smelling a rat, I suggest it to double check the garbage collection statistics too, at which point it's clear it's no leak, but a tuning error, at which point the LLM is better at tuning than me, because it has more energy than I do. Maybe there's true brilliance out there, when something doesn't come out of combining data and building hypothesis until you get really lucky. My experience is not comprehensive. But I look around me, and it sure seems I've not been lucky enough to see it. Even the shiniest people I've worked with, which most of the audience here would recognize, have never shown me that they can go past this.
  • DoesntMatter22 2mo ago
    I think that’s true for genius is in general. I don’t mean the Einstein type but I mean the child prodigies that graduate high school at 10 years old or whatever I mean if you can read something once and remember virtually everything about it that’s a gigantic leg up on everyone else who has to study drill the stuff into your head, etc. Even in a debate, if somebody just has the ability to remember tons of facts and figures, the other person will seem unintelligent by comparison, even if the other person is correct
  • grahamburger 2mo ago
    People have told me I was smart since I was a kid, but I can't remember for shit. I had a thought when I was fairly young that the only reason I was (maybe, sometimes) outperforming others intellectually is that I was habitually compensating for my poor memory by working things out on the fly, while others could rely more on rote memorization. Anyway, takes all kinds I guess!
• alienbaby 2mo ago
  True, but as it pieces together new mathematical truths from the pieces we have discovered ourselves, it then has more truths upon which to build new solutions. And so on, so while it is just remembering things we have forgotten, the amount of progression an LLM can make may still be several steps ahead and touch areas we have not yet been able to consider or make any progress on ourselves. It's a bit like a pyramid though, eventually it will have tiued together all teh things we know, found all the things we could have known, and then .. perhaps, be unable to actually come up with something genuinely new.
• eek2121 2mo ago
  IMO, That's the case for all subject areas. It is also one area where AI excels at, and it could be of real use if we can find a way to stop hallucinations. The sum of all knowledge being at our finger tips would allow everyone to focus on the hard stuff.
• enquirewithin 2mo ago
  Is there anyone on the planet who doesn’t think ”thinking” also includes memory?
  • vmilner 2mo ago
    There's a depressing number who dismiss the benefits of things like learning times tables as 'rote learning' rather than force-multipliers that free your brain for other things.
• spread2009 2mo ago
  [flagged]
• light_hue_1 2mo ago
  There is a certain amount of slack that we have. Things that are obvious consequences of what we've already discovered but that we haven't taken advantage of yet. AI is reaping that slack. It isn't adding brand new ideas at the moment. We will run out of this slack pretty quickly.
• bunkydoo 2mo ago
  [dead]
• re-framer 2mo ago
  I can't help but think of Michael Nielsen's essay "Augmenting Long-Term Memory" [1]. > Many people's model of accomplished mathematicians is that they are astoundingly bright, with very high IQs, and the ability to deal with very complex ideas in their mind. A common perception is that their smartness gives them the ability to deal with very complex ideas. Basically, they have a higher horsepower engine. > It's true that top mathematicians are usually very bright. But here's a different explanation of what's going on. It's that, per Simon, many top mathematicians have, through hard work, internalized many more complex mathematical chunks than ordinary humans. And what this means is that mathematical situations which seem very complex to the rest of us seem very simple to them. So it's not that they have a higher horsepower mind, in the sense of being able to deal with more complexity. Rather, their prior learning has given them better chunking abilities, and so situations most people would see as complex they see as simple, and they find it much easier to reason about. I once tried out his Anki approach during a math lecture. Whenever I reiterated a card, say about some lemma, I noticed something interesting about it. This was delightful and many lemmas became much more streamlined over time. It's not a "solution" to mathematics, but I found it delightful while it lasted (before akrasia or lack of time kicked in and I stopped doing it). [1] https://augmentingcognition.com/ltm.html https://augmentingcognition.com/ltm.html
  • margorczynski 2mo ago
    > many top mathematicians have, through hard work, internalized many more complex mathematical chunks than ordinary humans Do you really think an average person can internalize complex math? Them compressing it effectively and then remembering it is a sign of (very) high intelligence.
    • elbear 2mo ago
      If explained in a specific way and order, yes, it's likely.
      • deleted 2mo ago
        [deleted]
  • 2mo ago
• amelius 2mo ago
  Sounds like another attempt to frame AI in a way that makes them feel better about themselves. The simpler explanation is that a working memory is a requirement for intelligence, and a larger working memory will make you more intelligent. Hence the AI can in fact be more intelligent than the mathematician.
  • slopinthebag 2mo ago
    Depends how you define intelligence. I struggle to consider LLMs actually intelligent, at least for any definition of intelligence that I would be content by.
• milchek 2mo ago
  Memory and intelligence are linked. Someone with a better memory for ideas or concepts will be able to more quickly incorporate those into novel ideas or recall them when necessary to assist in solving a problem than someone with worse memory. To those here challenging this with “yes but I’m smart and my memory is bad” - a) define smart and b) perhaps your memory for trivial things like life events, what you did two weeks ago on Monday or people’s names is bad, but I suspect your memory for “work” or problem solving is strong. Another example I used to see (hear, rather) is how musicians rip off each others riffs and hooks without noticing (unintentionally - they claim), which I long suspected as simply “forgotten” riffs they heard in other songs that once they started playing themselves by chance they attributed to their own creativity. Creativity and intelligence are somewhat linked that way I suspect. In any case, this all boils down to the same thing, you can think of yourself as a dynamic model made up of memories and biases to some degree, and your ability to store and recall useful information to solve problems increases what we call your intelligence.
• ilaksh 2mo ago
  "OK, it might be much better at math than me, but it's not smarter, it just remembers more". Heh. Every day, a new type of cope. It's like, coping as hard as possible. If this was an ML researcher trying to come up with a way to improve performance then you could say it wasn't cope but rather practical observation to serve a goal. But it's just cope. Also, AI is going to continue to get smarter. A lot smarter. There are already systems in R&D that will continue increasing efficiency and performance of hardware by more orders of magnitude.
• mcv 2mo ago
  I thought this was fairly obvious. I don't consider any of the AI models I've worked with particularly smart, but they've read orders of magnitude more than I ever could in my entire lifetime. They have far better book knowledge than I have, so that's how I use them. I use them for things that I suspect other people out there would know, but I don't. But when I work on something that I suspect is truly new, the models rarely understand what I'm doing, and I've got to do it myself. Although I still poll them for basic principles, best practices, and other advice.
• MelonArmiger 2mo ago
  It's probably worth checking the site history before posting. The guy who wrote this looks like some kind of "race science" crackpot.
  • inerte 2mo ago
    Gee, 100%. Articles basically saying genetics explain everything.
  • AnodicElegy 2mo ago
    Interesting fellow. He has publicly claimed that he has ESP: https://www.splcenter.org/resources/hatewatch/wikipedia-wars-inside-fight-against-far-right-editors-vandals-and-sock-puppets/ https://www.splcenter.org/resources/hatewatch/wikipedia-wars... https://openpsych.net/forums/18/thread/25/?page=1#124 https://openpsych.net/forums/18/thread/25/?page=1#124
    • aeve890 2mo ago
      To be fair, he's been doing this shit since 2014, years before LLMs, so he's a real one, a full time crackpot.
• jiggawatts 2mo ago
  Seeing what AI has been doing to maths conjectures recently, I decided to give theoretical physics a go. I threw some random "crank" ideas into GPT 5.6 Ultra and let it consume most of my weekly Pro subscription quota. I'm actually pretty impressed with what can be achieved! If your simply ask it to use adversarial agents and include honest self-ratings, it can produce output competitive with a smart but not exceptional PhD. Not imaginative, but the effort that it sinks into even the smallest matters is just amazing to watch. Did I get anywhere by applying AI to my toy models? Probably not! I ran out of quota before I could tackle anything really interesting. But... GPT did seem to discover something genuinely new by essentially brute-force effort of combing through hundreds of papers on ArXiV: a new "constant" extracted from a formula over quark masses that seems to evaluate to exactly 3/4 at the one-loop level and then with various corrections at two-loop and higher. Link: https://chatgpt.com/s/t_6a80f2c794388191971350733a6f4378 https://chatgpt.com/s/t_6a80f2c794388191971350733a6f4378 This is "novel" in the sense that it found a vaguely similar formula in a paper where the authors simply missed the opportunity to extract a simple ratio from quark masses, combined with updated constants the AI found in in later papers that allowed it to guess that this might be exactly 3/4. This is the super-power of mechanised attention! We've produced a truly ludicrous amount of scientific and mathematical output, far past the ability of any single human mind to be even vaguely aware of all of it. Now we can scrape together all of the jigsaw pieces we have made and see what fits together. I wouldn't be surprised if someone, perhaps even a hobbyist comes up with a viable theory-of-everything in the next year or two simply by feeding in some clever starting point and then turning the handle on the machine until a complete and consistent theory pops out the other side. Exciting times!
• incognito124 2mo ago
  That intelligence is "just" retrieval within a huge corpus is an old one, and the one I am thinking about a lot these days: https://news.ycombinator.com/item?id=44060672 https://news.ycombinator.com/item?id=44060672
  • tim333 2mo ago
    I think there's definitely more than just retrieval, although no doubt it helps.
• doginasuit 2mo ago
  A superhuman working memory is exactly how I've been describing the LLM advantage. Paired with the unreliability of its reasoning and judgment, it is what makes AI a supplement to human intelligence, not a replacement. On the other side, LLMs make random mistakes and wrong choices and they have a bias toward writing more code instead of less. You can make up for this to some degree by running another LLM against their output, but with very diminishing returns. Even if they were perfect, there will be an ongoing cost to little or no human awareness and understanding of the codebase. It may take some time for people to recognize the cost of AI code generation and their value for virtually everything else, but I believe we'll get there.
  • smj-edison 2mo ago
    I also find them bad at what I call "abstraction compression." They're really bad at noticing when a helper function is needed, when some structure they repeated five times slightly differently can become a struct, when a whole section of code can be encapsulated in a simpler design. I'm lucky that for my side project (an interpreter) I've written all the code myself, so I've built up its design in my mind over the past year, and so as I mull over what I'm writing I start coming up with simpler designs. Interestingly using Opus 5 (and LLMs in general) has made me worse at this, since I don't feel the pain of writing something over and over again. On the other hand, I don't really want to implement a whole stdlib, so I have it write more of the auxiliary code. The hardest thing is that I have to manually manage the context, which is painful when I personally remember every helper function and why. I have to remember to keep the list of helper functions updated, which is irritating.
    • doginasuit 2mo ago
      I have that issue too. One thing I suspect they are amazing at is documentation generation, maybe a good solution is to have it generate a concise summary of helper functions that are available for any given context. Then have it use the same doc when you call in some code generation. You touched on another baked-in limitation of LLMs: their inability to follow Don't Repeat Yourself. To anthropomorphize a little, LLMs love to repeat themselves. I remember in the early days before reasoning algorithms came along, you could ask an LLM a question and it would often explain the same answer two or three times in a slightly different way. You also see this in image generation with multiple people, they will often do essentially the same person several times with minimal variation. Applied to code, in your functions they saw an earlier pattern and they can't help but write it the same way. This isn't entirely at odds with producing good code, I often force myself to wait for another occurrence of where a helper function is needed before I write it, lest I create a slew of utilities that are easy to forget about. Maybe it just needs an additional pass: "analyze your output and create a set of helper functions where logic is repeated or the abstraction is wanted."
• metalman 2mo ago
  "AI" does not exist, it knows nothing and has no experience of doing or seeing anything to "remember", a data bank filled with the jumbeled accounts of things that humans have said or written about what they may have made up or may have done or may have heard someone else say, and changing the label to "memory" is only that , and can only have the predictable outcomes.
• whateverboat 2mo ago
  It was duuring my PhD I realised that thinking is essentially 1. remembering all the different information to remember all the tricks 2. trying all the different tricks in the problem 3. optimizing deciding which to try based on different information 4. trying random things to discover patterns (and hence new tricks) 5. explaining your tricks to others so that they can do the first 4 steps independently and come up with even better tricsk 6. refactoring tricks into common and special parts to create a well organized theory 7. documenting for future generation in a language they can understand that's all thinking is.
  • vatsachak 2mo ago
    LLMs are enormously good 1. and 2. But humans are much better at the rest. I don't want to read another LLM documentation ever again. The information to text ratio is way too small.
  • calf 2mo ago
    > That's all thinking is That's more because a PhD (especially in this socioeconomic era where academia is also a microcosm of capitalism) does not automatically teach or focus on deep inquiry, rather narrow research programmes. Case in point, fallaciously reducing it all to a superficial theory of tricks means that by the same light, general relativity or any other major scientific result is merely just a trick. That framing ought to be absurd on the face of it but the fact is many PhD students do still graduate with very unexamined understanding of science, cognition, philosophy, etc. (Another way to see this is to note how such an argument is letting the notion of "trick"/"heuristic" do all the heavy lifting--something an advanced education should have nurtured the critical thinking skills to avoid that very metacognitive pitfall in the first place, which is ironic.) (Yet another way is to see how increasingly experts of one field or another insist that one must stay on their lane when speaking about a complex topic.) There are professors and thinkers who have written/spoken about this but they are a minority. Even Einstein himself when he complained that more and more scientists were trained/structured to miss the forest for the trees, and that was almost 100 years ago.
    • mimischi 2mo ago
• Fr0styMatt88 2mo ago
  I often think “what will ‘maintainable’ code look like in the future?” given this kind of thing. Much of ‘good coding’ is about optimising the codebase for workability for a human intellect with human working memory. Though having said that, from the studies I’ve come across it seems like LLMs tend to generate more verbose code but perform better over the long-term when the code is maintained and not allowed to sprawl all over the place. I’m mainly using AI for tools development and in that context I often wonder if I’ve just developed a career-long habit of over-abstracting. Like, the tools work without all the fuss I might have put in at the beginning and you just iterate and evolve as you go.
  • yladiz 2mo ago
    Is this assuming or studying human maintenance?
    • Fr0styMatt88 2mo ago
      I’ll dig up the references but I believe they were studying what happened to LLM performance as the complexity and verbosity of the code went up (ie- just letting the LLM rip without doing explicit code cleanup and simplifying).
• stevenalowe 2mo ago
  and yet very little access to common sense :) the article cites the ability to keep a ton of details in working memory as an advantage, I'm not so sure that it is - perhaps it is quality over quantity; a compression of everything known into a smaller set of interlocking patterns should provide a more useful generalization (if correct, of course). The human's ability to perceive 7-10 concepts might just be the tips of the icebergs composed of a gazillion micro-concepts, i.e. our working memory is enormous, but not consciously accessible also, the older neural capacity estimates are way wrong [1]: dendrites also compute [1] https://www.yahoo.com/news/science/articles/neuroscientists-vastly-underestimated-brain-cells-110000384.html https://www.yahoo.com/news/science/articles/neuroscientists-...
• ramesh31 2mo ago
  Yet it still can't make a symphony
  • tim333 2mo ago
    Well, not a good one.
• ffwd 2mo ago
  LLMs are still missing a part of working memory. Part of working memory is being able to attend to small amounts of information and then understand and parse all the pieces of that information. When LLMs use their "working memory" they just analyze different probabilities of tokens and there is no prioritization or understanding of the information in the way humans have it. If there is no training data or data in the context that leads it to the correct result then it can't do it, whereas a human seems to be able to generalize and abstract a goal and then repeat an action or thought process in a 'recursive' manner to reach the result. AFAIK LLMs don't do this. Just as an example to illustrate. I recently asked an LLM to organize a bunch of artists albums into whether they were released by a major label or an independent label, and for the most part it did a good job. But there were albums that it classified as independendent that weren't. I presume because it either didn't run into the right data when searching or it misunderstood the data it did find. A human would not do this because if a human had a list of all major labels, it could instantly detect whether an album was or wasn't indie, because it doesn't do any complicated parsing or token probabilities that LLMs do, it just recognizes a pattern (either an album is indie or it is not, a human brain needs simply one piece of information to decide this), an LLM is not that simple. In a way human brains are simpler than LLMs. The algorithms it runs mentally can detect a piece of information and then see most / all of the consequences of that information whereas an LLM thrawls through megabytes of text and does a token probability distribution and so on without any simplicity.
• socalgal2 2mo ago
  Which is why love it! It can easily did through the code flows of our 250k file project and more often than not and understand what's happening much faster than I ever could.
• fragmede 2mo ago
  which is why the programming language for LLMs hasn't been invented yet. I cannot read someone else's code that is filled with single letter very real names, but I can write it but as a human with human working memory, I can only hold so many variables in my head to reason about an LM is a much greater working memory unless does not need the same abstractions as a human does if I'm no longer riding the code and I don't need to read it, then not only is goto's considered harmful is holding us back, but bring on the big ball of mud spaghetti code that outperforms human written code, that no human has a chance of maintaining or reasoning about. The problem with this is there is going to be a bug that needs fixing that the LM can't fix and if the humans can't understand the bug that we're just gonna have to live with the bug! But let's not talk about that.
• mgsh7 2mo ago
  Consciousness and self awareness should have been created by now if the memory and nodes pool had exceeded the human brain, right?
• random3 2mo ago
  if you take any system and break it down you get to simple building blocks (like memory). Then you claim that it isn't "outthinking" but "out remembering". Sorry, but what do you think the brain is doing? Not using memory? Especially working memory. How about frequency, brain is operating in Hz whereas most processors are in GHz, etc. Finally if we were to think in terms of a proper memory/latency hierarchy, what's a Model + CPU's equivalent of working memory? Registers? L1 cache?
• r0s 2mo ago
  So does the cheapest calculator?
• harhargange 2mo ago
  Along with remembering facts, it is important to insightfully give ‘weight’ to the facts. AI gives ‘weights’ to things depending on its own priorities or corporate interests. Our brain gives the weights to various facts depending on personal insights. (X is painful, Y is more effort but less painful etc etc) In a real world scenario these both can act in complimentary ways and AI just supplements the human working memory at the end of the day.
• shevy-java 2mo ago
  Yet it is still incredibly stupid.
• pitiflautico 2mo ago
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• deleted 2mo ago
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• singularity2001 2mo ago
  it's a paradox given how extremely complex each single neutron is. my current solution is that most of the complexity of neurons is just for maintenance
• badgersnake 2mo ago
  This is not surprising. I find LLMs in general have pretty limited utility, but there a niches where they are good and one of those niches is finding little snippets of information in a way that you used to have to sift through pages and pages of google results to find.
• _hlqj 2mo ago
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• tsoukase 2mo ago
  Working memory is literally the L1 cache of the brain, which holds a mere 3-7 objects. The article seems to conflate w-m with regular memory, which is what a human knows at a specific time. Computers, especially now with LLMs, have everything vastly larger than human like speed and complexity. What LLMs lack is more qualitative traits, like creativity, abstraction and intuition.
• throwaway2037 2mo ago
  @dang: The title is incorrect. Can you please change it to the original? > AI Isn’t Outthinking Mathematicians. It’s Out-Remembering Them.
• ZackMomily 2mo ago
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• popcornco 2mo ago
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• vmilner 2mo ago
  "There is a strong family resemblance about misdeeds, and if you have all the details of a thousand at your finger ends, it is odd if you can’t unravel the thousand and first." Sherlock Holmes, 'A Study in Scarlet'
• odyssey7 2mo ago
  A convenient fact about mathematics is that people have put a lot of effort into making it a self-contained logical universe. AI doesn’t need a conventional world model to be great at mathematics.
• popcornco 2mo ago
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