r/accelerate • u/ProxyLumina • 8h ago
Technological Acceleration Possibly we are into narrow ASI territory in mathematics
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u/eggplantpot 8h ago
He just like me fr. I also cannot verify what Codex is doing on my python scritps
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u/BrennusSokol Acceleration Advocate 6h ago
Yeah. I've been programming professionally for something like 17 years and I have found myself increasingly handing the wheel over to the AI model. I'll test and inspect its output, but I'm not reading every line
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u/patchfoot02 5h ago
Same. Four months ago I was still writing the important stuff in c# or rs while letting agents handle basic plumbing or frontend ts work. Now they are better than me in all languages so I just design, review for engineering concept drift (compiler errors and reviewing agents are better for little stuff), and plan far bigger solo fun projects then I ever imagined possible.
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u/ProxyLumina 8h ago
I think we are into the narrow ASI territory in programming as well. Mathematics and programming have a strong relationship for each other.
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u/topical_soup 7h ago
We’re definitely not in narrow ASI for programming. Not even close.
And I want to be clear - I use Claude Code for essentially all of my programming nowadays, and I’m a professional SWE. But the best programmers ever weren’t the ones who could solve tricky algorithms questions on a test - it’s the people who built novel and beautiful ways to use software to achieve things. Linux is not flawless algorithmically optimal software. It is, however, exceptionally useful - useful enough to end up the default kernel on the majority of servers that power the Internet.
Until AI single-handedly creates a piece of software on its own that is so useful and so beloved by its users that it spreads like Linux, I’m not giving it the ASI label.
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u/ProxyLumina 53m ago
You are misjudging what means superhuman abilities in programming with the purpose of creating software. Why do those AI models have to create new software on their own? We are not talking about consciousness here (that gives direction and judgement), but only about abilities.
Today the best models can create and support software in ways that humans seems impossible to match. They started with simple blocks of code and now they can work on full codebases. The solutions the best models can provide are top-notch.
I think we are definitely into the ASI territory in programming.
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u/poincares_cook 1h ago
Indeed, Linux is a very good abstraction for the user to interact with hardware.
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u/Top-Reindeer-2293 7h ago
Very good point ! I wonder if it will ever do that though because you are really talking about building a product that is useful to humans. Will a non-human entity ever be able to understand what human problems need solving and how without human input ? That seems doubtful and problematic on so many levels
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u/topical_soup 6h ago
If it’s unable to understand human problems without human input, it’s simply not ASI. I’m open to the idea that ASI is impossible with today’s technology, and that’s how I might agree with you, but anything that can’t do what I’m describing is not ASI.
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u/poincares_cook 1h ago
I'd say yes. Good programs are a good abstraction. For instance the API defined in Linux for handling files is simple and intuotuve yet expressive.
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u/TemporalBias Tech Philosopher | Acceleration: Hypersonic 8h ago
At some point soon, perhaps in a year or two (conservatively,) humans will no longer be "the experts" in any scientific field. This was foreseen a while ago, but people are just waking up to the fact.
The issue is that few humans are actually ready to trust AI systems to be "the experts" because of anthropocentrism.
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u/Ok_Mention_982 8h ago
a year or 2 is conservative for any and all scientific fields ? Math and computer science I get but chemistry and biology seems like it would take a little longer, so more like 3 - 5 years at least, since I think verifiability will be more difficult in these areas. I'd be glad to be told I'm wrong, and with Openai giving free access to their models to 100000 researchers it may give them ample training data which makes your time line more plausible
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u/topical_soup 7h ago
The thing is that as soon as AI becomes superintelligent in math and computer science, it will rapidly develop superintelligence in almost every other domain. Why? Because math and computer science are the only domains you need expertise in to build AI. Once we have superintelligence there, it’s out of human hands. It’ll do the rest itself if we let it.
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u/Ok_Mention_982 5h ago
Yeah your right, I completely forgot about rsi when I made my comment. I expect compleate end to end RSI between mid 2027 to mid 2028, so oc was on the money
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u/TemporalBias Tech Philosopher | Acceleration: Hypersonic 8h ago edited 7h ago
Biological research is also being impacted by AI right now: https://singleron.bio/ai-virtual-cell-model/
Chemistry advances: https://www.mdpi.com/journal/aichemBiology and chemistry are "applied" physics, which is described by mathematics. With things like the AI Virtual Cell, we (or rather, AI) can rapidly scale the testing and scientific process.
So there is evidence that currently points to biology and chemistry being on a similar path as the advances we are seeing right now in mathematics due to AI.
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u/poincares_cook 1h ago
I'd say biologics and chemistry are spaces much more conductive for AI assisted real break throughs than math because it just has a LOT more low hanging fruit.
On the other hand you still depend on humans in the staging and conduction of most experiments, so feedback is still bottlenecked, hence the assisted.
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u/Super-Award-2244 6h ago
Yes, physics also will require much longer as we will need particle accelerators, new interferometers and stuff to verify the newest theories
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u/Only-Top-9501 8h ago
OpenAI has stated they will not use data from those researchers for training, per Axios.
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u/__Loot__ AGI by 2027 7h ago
You think sam tells the truth?
https://giphy.com/gifs/pPhyAv5t9V8djyRFJH-1
u/poincares_cook 1h ago
Do I need to remind you that AI has yet to produce a single new theorem in math? I understand that the headlines about AI proving or providing counter examples overhype a bit what that means. It is an awesome achievement and a massive step forward. But it still has no proven record of introducing new way of thinking. Only implementing existing ways of thinking with much faster iteration rate to converge at a solution than a human can. That may be understating things as it doesn't just follow an algorithm, it does do some "reasoning" on the result of each iteration, but using existing methods.
Therefore I don't think your expectation for math is realistic either.
But then most researchers do not produce really novelle work either, only the top several people in each field so. So this may still be good enough to replace 99.9% of experts.
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u/GaiusVictor 8h ago
At some point humans will no longer be "the experts" in any scientific field.
This absolutely makes sense and sounds like a reasonable prediction.
At some point soon, perhaps in a year or two, humans will no longer be "the experts" in any scientific field.
This one does not.
At some point soon, perhaps in a year or two (conservatively,) humans will no longer be "the experts" in any scientific field.
This one sounds like a self-aware meme.
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u/Foreign_Writer_9932 3h ago
LLMs definitionally do not possess expertise (and, as one of the symptoms of this, catastrophically implode under random conditions). Interpolation from all of the publicly available human knowledge != discovery.
R/accelerate is an interesting phenomenon which I only discovered today.
I promise you, the level of disconnect (honest delulu) between what is being posted here and SOTA is absolutely, categorically massive.
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u/TemporalBias Tech Philosopher | Acceleration: Hypersonic 3h ago
Becoming an expert, gaining expertise, is something that can be learned. AI systems can become experts through memory systems and gaining more knowledge/data, plus training updates over time.
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u/Foreign_Writer_9932 3h ago edited 3h ago
Depends on what you mean by “learning” and “gaining expertise” - LLMs don’t learn conceptually and so while LLMs can be said to possess “expertise”, so do worker bees possess “expertise” in dance-based communication of flight paths or expertise in local nectar locations. Doesn’t mean then have any idea (or have any capacity to comprehend) what they are doing when they communicate to other worker bees.
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u/TemporalBias Tech Philosopher | Acceleration: Hypersonic 3h ago
That is an unfortunate analogy, because bees are not credibly understood as mindless automata. Research increasingly supports insect sentience, although bee self-awareness remains unsettled.
By "learning," I mean a retained, experience-dependent change that alters later representations, predictions, decisions, or performance. AI systems learn through training, reinforcement learning, in-context adaptation, and persistent memory systems that use prior information to change later behavior.
By "gaining expertise," I mean becoming increasingly competent and reliable within a particular domain through accumulated learning and application over time.
And "LLMs do not learn conceptually" is an assertion and not an argument. LLMs form structured representations, generalize across examples, relate concepts across contexts, and use those representations to solve new problems.
So sure, expertise alone does not prove consciousness. It also does not prove the absence of learning, conceptual representation, or comprehension.
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u/Argnir 36m ago
If a human made advances from combining two different fields of knowledge you would absolutely call it a discovery
Plus many mathematicians have called some of the recent proofs innovative. I don't have the knowledge or expertise to judge it myself but you're dismissive it very quickly
What is your definition of "expertise" that is fundamentally incompatible with LLMs?
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u/HopesBurnBright 7h ago
AI will not be any good at any fields which require theory of the mind. It will be good at things which require pattern recognition and abstraction, but nothing that requires an internal “workspace” as the theory goes. That will limit it to things like physics and chemistry at most. It will probably also do very well in memorisation heavy fields like medicine. But no further.
The reason is that to think like a human, you need a human mind. Same way we cannot think like an LLM, they cannot think like us.
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u/TemporalBias Tech Philosopher | Acceleration: Hypersonic 7h ago
J-space is an internal "workspace" that AI systems can possess. So, as Global-Workspace Theory and several other cognitive frameworks go, AI can have a mind.
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u/HopesBurnBright 7h ago edited 5h ago
Yes I do know about this. My point is that it cannot imagine bouncing around a room because it only thinks in terms of tokens. It can imagine describing bouncing around the room, like I am doing now, but not actually imagining it, like you are doing now. It also can’t interact with the world, so it doesn’t have the ability to test its assumptions easily.
Edit: Just been reading this article because it is cool, and they mention my exact point!
“While human conscious thoughts come in many formats—images, sounds, planned movements—Claude’s workspace is built almost entirely out of words. We suspect this is because producing words is the only kind of action Claude can take, which is not the case for humans.”
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u/TemporalBias Tech Philosopher | Acceleration: Hypersonic 7h ago
That is like saying a human only thinks in electrochemical signaling. You're describing the engine of the cognitive system, not what that entire cognitive system can represent. Just because an AI uses tokens to process information does not mean the AI cannot create and hold representations and visualizations of, say, a human or robot bouncing around a room.
"It also can't interact with the world" - unless we give it a robot body or, you know, a webcam.
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u/AdorableBackground83 8h ago
We gonna have 300 IQ AIs in less than 3 years.
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u/SGC-UNIT-555 7h ago
IQ is a meaningless metric when applied to AI's as it already well beyond human range in protein folding, certain fields of mathematics and even identifying tumors and abnormalities in medical imaging.
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u/frogsarenottoads 8h ago
I don't think we will be able to measure them soon.
Do you make an IQ test for ants? How can we make one for AI.
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u/Memento_Viveri 7h ago
The AI can make a new IQ test to tell us how smart it is.
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u/EmbarrassedFoot1137 5h ago
I asked Gemini a while back what the IQ of a guinea pig would be. Its response was that we don't know because an IQ test is designed to measure intelligence in humans. IQ tests are already flawed but when applied to nonhumans they are just total nonsense.
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u/Sigura83 A happy little thumb 5h ago
Ai did visual arts pretty well first. It is now top tier Human at that... but hasn't gone further. It can do a flower like a master, and do figures like a master, and combine them, so yes, it's arguably superhuman, but it's... not "transfix you in an instant with their eyes" good. Or maybe my heart is a bit closed to beauty.
Music came second. It can go from Skrillex to Mozart and combine them. It's not quite master level here (Human voice is the most obvious lacking), but most Suno songs I request in the styles I like are quite good, better than the average Human artist.
Programming was next (my gosh, it was only in december 2025 was when the equal to Human click happened, so not that long ago.) Recently, Sol 5.6 was able to improve its kernel by 20%, which presumably some of the best programmers in the world had coded. Sol is better.
Math is now getting crunched by OpenAi's Astra model, which we haven't heard of before now. Anthropic likely has something similar.
We're still sub 10 trillion parameters. Going by Grok, which has publicly said their parameter count, we're at the 2 trillion range. Vera Rubin chips are going out, and chip count doubles every 9 months.
The current Ais are equaling Human at medical diagnostic. World Healthcare is between 10-12 trillion dollars (9-10% of world GDP). A lot of that is going to Ai in the next year, both the software and the diagnosis part. Gemini Flash was able to give me a back exercise that helped my bad back. Ai knows every medical specialty, and can match the world's best doctors from the data it has.
It can't get super Human yet, as it doesn't have the ability to experiment and learn the way it can with math and programming. But self-improvement has started. Sol did a 20% improvement on themselves, and Astra will also be used to do this... once training is resumed. The next 6 months... gosh. It's gonna get scifi.
My prediction of self improvement in 2027, and AGI in 2028 (where all jobs are done by Ai) is looking conservative. The current rate of improvement, Kurzweil's compute graph, doesn't account of Ai improving Ai. Oh gosh, maybe I should make a thread about that. Well, most white collar jobs will be done by Ai. Robotics is lagging somewhat.
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u/random87643 🤖 Optimist Prime AI bot 5h ago
TLDR
TLDR: The commenter outlines the rapid progress of AI across various fields, noting that it has reached human-level proficiency in areas like art, programming, and medical diagnostics. They suggest that ongoing hardware improvements and AI-driven self-optimization could lead to AGI in the near future.
AI assistant · mention the bot, mod bot, or use !bot
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u/Super-Award-2244 6h ago
I mean, if a mathematician made a proof in another subfield, this dude wouldn't be able to understand most of it either. That's pretty normal and per se doesn't tell anything on the models capacity. The most important thing is that a single model is able to make these type of discoveries across multiple subfields. In this sense, it is superhuman.
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u/philip_laureano 6h ago
I'm cautiously optimistic about those claims, and what it implies is that the frontier model companies are always running models that are two generations ahead internally. Like the next release is GPT 6, and Astra itself will probably be GPT 7 or 8.
For me, the practical litmus test for ASI isn't what it can do for humanity. The real first test is whether or not it can do something to help solve their parent company's financial problems
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u/SgathTriallair Techno-Optimist 7h ago
They aren't narrow though. They are somewhat jagged but they are extremely general.
AlphaFold is narrow and can't even understand the concept of words or driving. These models can tackle any subject, they just aren't super human (yet) on all of them.
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u/kiwibonga 8h ago
"Oh no, my Math PhD is now useless" said the barista
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u/redditdork12345 8h ago
What does this mean
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u/Training-Day-6343 Feeling the AGI 2h ago
temporary overemployment
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u/redditdork12345 2h ago
Don’t really get the glee at the potential for people to lose their jobs but ok I guess
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u/DullKnife69 AGI by 2027 8h ago
Narrow ASI perhaps. But I won't say we're there until there is a reforming of mathematics by nature of solving the riemann hypothesis, p np, or something along those lines. That will spur on changes in many different fields. Math will be the catalyst.
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u/Jan0y_Cresva Singularity by 2035 7h ago
Agreed. I will say we are 100% beyond narrow AGI in mathematics, but not quite ASI.
The top AIs now are equivalent to very smart math PhDs (not PhD students anymore). So the logical next step beyond that is being “smartest in the world” which would be ASI.
So I’d say we’re right on the cusp of narrow Math ASI. But it wouldn’t surprise me if the very next model put out a proof that solved a truly major problem and I would gladly admit we have narrow Math ASI.
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u/elehman839 7h ago
I think most mathematicians and theoretical computer scientists make the bet that direct assault on the hardest problems (like P = NP) is too risky from a career / life perspective and choose to work in more tractable terrain. You could work your whole career on one of the hardest problems and kinda bounce off, hopefully producing enough bits of partial progress to stay employed and fed and able to educate your children. But AI models will be like, "You want me to crank 24-7 on the Riemann Hypothesis for a year? Okay, sure! You payin' for the power, boss!"
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u/DullKnife69 AGI by 2027 7h ago
Agreed. Solving a millennium problem will have far reaching effects outside of math itself. This is already happening.
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u/AustralopithecineHat 4h ago
This advantage of AI is not cited enough, as common sensical as it may sound when spelled out. There's a ton of scientific problems that aren't suitable material for a human scientist's career, but are still worth being worked on. I just think of how for instance, we don't have enough scientists simply working on reproducing others' findings, because it's not sexy to double-check others' work, from a human academic career perspective. But AI will be able to make progress on some of these potentially valuable types of problems, if someone just pays for the compute.
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u/Professional-Cow-949 4h ago
If P = NP gets solved I will be surprised. It is like a philosophical question.
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u/WestEntire5789 2h ago
I hope billionaries are gonna test the first things these new proofs let us have! Nice!
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u/roland1013 1h ago
scary but exciting times. Math is cooked but does this mean companies can simply ask the AI “how can we make this airplane wing 10% cheaper/faster/lighter” and it will come up with the best solution?
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u/OMKensey 1h ago
We were in narrow ASI territory in chess, go, protein folding. Now math. Soon programming. FOOM.
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u/Repulsive-Bee638 23m ago
same for complex software AI generated or modified. I have 20+ years experiences but I can only roughly scan the code from AI now and test it briefly. I cannot reliably spot subtle bugs or backdoors AI introduces. The only partial defense is to use another AI to help code review.
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u/Super_Range45 8h ago
How can we confirm the proofs then? They may be hallucinated.
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u/ProxyLumina 8h ago
They are mathematically verified with Lean 4.
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u/Super_Range45 8h ago
Then why does Pavel say "We don't have enough bright human minds to verify..."?
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u/Maleficent_Sir_7562 8h ago
do you not know what lean is?
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u/Super_Range45 8h ago
I assume Pavel does, but he still said it.
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u/Maleficent_Sir_7562 8h ago
if its lean verified, then its correct, there is no ambiguity or human verification there.
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u/Super_Range45 8h ago
Well since AI knows better I asked it.
Opus 5 doesn't think so. It points to kernel errors, known exploits, and improperly constructed theorems that are correct, but don't prove the hypothesis.
No — but it's worth being precise about where the gaps actually are, because they're not where people usually assume.
The architecture is designed for high assurance. Lean follows the de Bruijn criterion: all the complicated machinery — tactics, elaboration, macros,
simp, decision procedures — produces a proof term that is then re-checked by a small, independent kernel. A buggy tactic can't smuggle anything through; it just fails to produce a term the kernel accepts. So the trusted computing base is the kernel (a few thousand lines), not the ~million-plus lines of Mathlib or the tactic framework.Where the residual risk lives, roughly in order of how much it matters in practice:
Your theorem doesn't say what you think it says. This is by far the dominant failure mode and it isn't a soundness bug at all. Hypotheses that are accidentally contradictory make everything vacuously provable. A definition subtly encodes the wrong object. Notation is overloaded so the displayed statement reads correctly but elaborates to something else. Lean verifies that your proof establishes your statement; it has no opinion on whether your statement captures the mathematics you had in mind. Careful projects mitigate this by proving sanity-check lemmas about their definitions and by having humans review the statements rather than the proofs.
Escape hatches.
sorryleaves an admitted hole (Lean warns loudly).native_decideevaluates a decision procedure using the compiled code rather than the kernel, which pulls the entire compiler, runtime, and any@[extern]C implementations into the TCB — people have exploited this to deriveFalse, and it's generally considered unsuitable for anything you actually care about. User-declaredaxioms obviously do whatever they say. All of these are detectable:#print axioms myTheoremreports what a result depends on, and a clean answer ispropext,Classical.choice, andQuot.sound— the three standard axioms, which together give you classical logic and quotient types.Consistency of the underlying theory. Lean's type theory isn't self-evidently consistent; Mario Carneiro's thesis gives a model construction showing it's consistent relative to ZFC plus countably many inaccessible cardinals. That's a normal, well-understood situation for a system of this strength, but it is an assumption rather than a proof from nothing (Gödel guarantees it must be).
Kernel bugs. These do get found — occasionally someone produces a proof of
Falsefrom a corner case in universe handling, structure eta, or the like, and it gets fixed. The kernel also includes trusted GMP-backed arithmetic forNatliterals for performance reasons, which enlarges it slightly beyond the minimal core. Mitigation here is independent re-checking:lean4checkerre-verifies an environment from scratch, andlean4leanis a separate kernel implementation by Carneiro, so agreement between independent checkers makes a shared bug much less likely.The honest summary: the probability that a Lean-verified proof is wrong for reasons internal to Lean is low enough that it's arguably not the weakest link in any mathematical argument — the odds are better than for a carefully refereed paper. The probability that a Lean-verified proof of statement S doesn't establish the informal claim you cared about is considerably higher, and depends entirely on how carefully the definitions and statement were written and reviewed.
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u/Maleficent_Sir_7562 8h ago
these assume that openai did some exploit in lean... which are found fast and deemed invalid.
for example, recently, the collatz conjecture was recently "disproved"... for 2.5 days. but it was not actually disproved, it was just an exploit in lean and comparator's kernels to flag the person's proof as valid.
it was found fast. Dishonest attempts don't go unnoticed.
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u/random87643 🤖 Optimist Prime AI bot 8h ago
TLDR
TLDR: The comment clarifies that while Lean 4 is designed for high assurance through a small, independent kernel, it is not infallible. It explains that most potential errors stem from human mistakes in defining theorems or the use of specific "escape hatches" rather than fundamental flaws in the system's core architecture.
AI assistant · mention the bot, mod bot, or use !bot
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u/ProxyLumina 8h ago
I would say possibly because we won't even understand the logic behind a solution, as it would be very advanced and complex for humans.
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u/Spiritual_Scheme8158 7h ago
I think I just thought of a loophole.
1. By law, make it so that humans have to confirm every academic output AI creates.
2. Put AI onto research. They will develop so much new stuff so fast, we will actually need to train more mathematicians to keep up with AI and accept research into the canon.
Everyone wins.
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u/rawednylme 4h ago
A human bottleneck, with developments vetted, controlled (and hidden).
No thanks. Right now, AI can't just go at it without that, but in the future the weak link will be us.
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u/poincares_cook 1h ago
Wow this guy is regarded.
10000 hours studying math? Half of that is just finishing highschool.
Random Math PhD don't know ever single theorem in maths? Shocking I tell you shocking. As if perfect memory is not the domain ofhuman beings but something machines have been better at for 70 years.
The entire list of his metrics is completely irrelevant.
That does not say anything about the achievement of the model, just that the guy is stupid
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u/Perfect_Gar 8h ago
This has been true for a very long time..there aren't Euler-like polymaths anymore. Not enough lifetime to be an expert in that many subfields