alt Hacker NewsAI has access to a vastly larger working memory than the human brain
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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) aimed at exploiting this fact. 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.
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.
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).
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.
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.
It's probably worth checking the site history before posting. The guy who wrote this looks like some kind of "race science" crackpot.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
Choosing what to remember is my biggest challenge!
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-...
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.
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.
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.
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?
Is there anyone on the planet who doesn’t think ”thinking” also includes memory?
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.
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
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!
"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.
"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.
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.
So build a better proof/paper/technique search engine?
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.
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?
Nobody IRL cares about nerds btw.
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.
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.
As AI keeps improving, the definition of intelligence will keep changing in order to exclude it without explicitly saying so.
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..
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.
> 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.
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.
Metacommentary: how did this post get to #5 on the front page with 1 upvote within 2 minutes of submission?
And the goalposts must move once again..
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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.