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.
The exam was huge, at least over 10 pages, and even when we technically ran out of time, the professor was kind enough to move the remaining exam takers to the neighboring lecture hall to continue taking it. I recall I spent a total of 2 hours on that exam.
Now mind you it was mostly short answer or multiple choice questions. The multiple choice questions were pretty sharp too, lots of traps and false but sounds right answers mixed in. But if it had been purely essay questions, I would have been screwed.
However with such a huge corpus of information in front of me, I ended up basically learning all the material on the spot. I just kept doing multiple passes through it, each time I noticed one of my answers contradicted one of the others, I would make adjustments to harmonize, which indirectly refined my understanding.
In the end I got B+ in the exam (which was curved to an A), and walked out understanding the material better than I did walking in.
Reflecting in the experience years later, I've wondered if a hypothetical LLM which was ignorant of microbiology could do the same thing if fed that exam. In some respects the traps they placed in the multiple choice questions actually were what helped me refine my understanding the most. Made me appreciate information theory more.
This strategy saved my ass on so many tests from high school through college that I did not study properly for.
I think it would certainly be possible to write a test such that this strategy doesn’t work at all, but lucky for me no one did in any meaningful way.
This has affected me a lot though, I know what is going on in songs, but can't ever remember the right words, but still some how end up sining stuff that means the same thing :/
So if I put a visual bend on something I can quickly understand it and remember it, but that bend has to be mind (even when helped by an image on a book). If it’s someone else’s, I’ll be very hard pressed to remember it (but I can easily remember where it was).
So at school, when I had to cram, I fill my head with images and things like that. And with the help of the exams (written) and memory of the classes, I worked out the answers.
I found a solution recently. "Critical mass." If I just don't see something frequently enough, my brain keeps deleting it.
So, if there's a fact you have trouble remembering, try memorizing it 100 times in one day. That's "inefficient", as far as memory goes, but it'll do the trick! And I like solving problems once and then moving on.
(Anki, conversely, is designed for efficiency, which means it's designed to be as painful and unrewarding as possible, but that's a rant for another time... suffice it to say if you design your software so that I constantly want to stop using it, your efficiency drops to zero!)
Most of us turn out like the rescued exotic bird which turns out to be a seagull covered in curry.
but you can also favorite comments for your own hall of fame, eg https://news.ycombinator.com/favorites?id=andybak&comments=t
I actually always thought it was the opposite for me – I had to figure out how to work things out from scratch because I could never remember anything.
Being smart, at least in the context of the workplace, is about being checked in to whatever you’re doing, and drawing connections across your experiences.
I can’t remember the lyrics to any songs, or the lines from any movies, or what I’m doing this week. I don’t think it’s a strong hypothesis.
My wife has a nearly eidetic memory, so marrying her was a game changer for counteracting mine :)
Some people, through practice and experience are better than others at breaking down foreign information and making sense of it, or jumping to an assumption.
I also think there’s an element of just remembering what my final conclusion was on an opinion, vs all my old opinions and all the work to get there. It’s easier to just store current belief and re-work-it out how I got there later.
I know people that got to post grad math without understanding a thing but they could remember a lot easily, while many of those that understood but had a harder time remembering every last variation of everything got penalized.
If you want an example of someone at the near peak of human ability, check out Jon Von Neumann.
Then there is an ability to peer deeply into complex problems and somehow find the simplest truths that make sense of it all. Think of Einstein.
Both are incredibly intelligent, but in different ways. I'd say Von Neumann's memory was far greater than Einstein's though.
One can flawlessly ponder anything known to man, and the other could ponder completely original ideas (to an extent)
Doing an angular -> react refactor with Opus 5 at work.
Amazed at how good it is but also amazed at how dumb it is after I told it I want a very basic and specific code review:do the file names match the contents? is one functionality/concern split in multiple files? do the variable names match what they do? does a method read nicely?
Anyway, I was very surprised at how bad it can be at this.
I suspect it's because the LLM simply does not focus well enough on a single method.
My gut modelling is that the critical files are quite small, some 4k LOC and the LLM tries to read them all in one go and my requirement is surprisingly difficult but the LLM does not realize it, loads all the input into context and simply cannot find a good signal per token/group of tokens.
I guess I can do it because I literally cannot fit 4k LOC in my head and I actually read only tens of lines but I can reeealy dig deep in those lines.
The LLM can remember lots, it has 1M context window, was trained with needle in the haystack problems, but it can severely underestimate how much computation is required with TASTE/universal computation type tasks.
Yes, LLMs are incredible when it comes to delivering well defined outcomes, it's evident they were RL trained very well. But they are incredibly bad at understanding nuance in a text.
It's incredible how agentic coding benchmaxing seems to correlate with writing good poetry but at the same time finding good names for files and variables and wiring beautiful code does not.
Same with getting basic details of who said what in an "adversarial" conversation between 2 humans -- LLMs are incredibly bad at it, with all their "almost discovering new maths" skills
I have fairly poor memory, but I can solve problems that people with better memory can't.
There’s no such thing as a truly original idea. It is all just combining A+B!
But often results are incremental and obvious.
But also if you jumped from A to A''''', nobody would understand why or what it relates to.
But “remembering” is only the beginning (you have to remember first!), after that, elements like understanding relations, connecting dots and remixing, timing, etc will truly make one shine.
In one way, it’s like the current “LLM + Harness” setup for agents. LLM is how well it remembers, but different harness techniques really matters, at times even a worse model mixed with great harness can outperform great model with bad harness
I’ve met different people throughout my career whose intelligence came in 1 specific area. For instance, my friend is extremely good at trivia, he clearly has a lot of storage and can access it easily. I think I’ve only met one person who was excellent in all three areas of intelligence.
Obviously this is a simplification, but it’s how I like to illustrate my ideas on intelligence at parties and first dates.
Understanding especially in the context of unknowns is what intelligence is.
I wonder practically how useful it is. It should be very useful, right? Say you're a hiring manager, and now you can hire thousands of students who have almost eidetic memory across pretty much all the subjects. The students can produce amazing work if you give them specific instructions. They are also very good at mechanical pattern matching. That is, they are the uttermost crammers. They are like the students who don't really get STEM yet they could, through memorizing all the problem-solving patterns, ace any national college entrance examinations, IMOs, PHOs, Putnams, you name it, and all the way to solving hard problems like Sendov Conjecture - they even found out elegant elementary solutions! Mind you, Terrance Tao proved a weaker conclusion when studying the Sendov Conjecture and got to publish his work on The Big Four. That said, the students won't take initiatives, and they sometimes make very silly or even impossible mistakes, to the point that it requires your supervision and review. Would you hire them to do your company's work? I'd assume that answer is a resounding yes.
Really, compress. Compression is a useful proxy for intelligence.
To compress data efficiently, an entity must discover the underlying patterns, structures, and regularities within that data. Identifying these structures is precisely what we define as "understanding" or "intelligence.
Anything we put into writing can be thought of as a string of data. The Kolmogorov complexity of a string of data is the length of the shortest possible computer program that can output that string.
A good compressor doesn't just memorize (yes, there's a reason LLM writing features the "it's not just X, it's Y pattern"), it builds a generalized model to handle unseen, similar data. LLMs are trained by compressing the internet into weights, they use this to write new code.
When an LLM is trained, it compresses ...bytes of human text into a few ...bytes of neural network weights. When you ask it a question, it uses that compressed model to generate (decompress) a coherent response.
Consider the dichotomy:
LLMs display emergent reasoning, coding capabilities, and logic because they were forced to compress human language so tightly.
LLMs often suffer from "hallucinations" and struggle with novel logic puzzles because compression encourages mimicking statistical patterns rather than building a true causal model of the physical world.
An algorithm can achieve massive compression if it utilizes a highly complex, slow, or massive decompression engine. If a system takes 10 000 years to decompress a file, or requires an infinite amount of computing power (like Kolmogorov complexity), it cannot function as a practical intelligence. Real intelligence must operate within tight time and energy constraints.
> Maybe there's true brilliance out there
Brilliance is a trope. pg's 2009 "The Anatomy of Determination" deals with it: https://www.paulgraham.com/determination.html
In it, he argues that determination is the single most important predictor of success, surpassing even raw intelligence.
While popular culture loves the narrative of the "brilliant genius," Graham's experience backing hundreds of startups taught him that a highly intelligent person without drive quickly becomes ineffectual. Conversely, a profoundly determined individual can find pathways to success even in ordinary or unglamorous industries. Over time, determination actually starts to look like talent.
I'm going to commit a faux pas (a breach of HN etiquette) and give an AI summary in 3 bullet points because, well, the article deals with 3 points, so it's a good fit and I doubt I can compress it better than AI:
1. Willfulness. The desire to get your way regardless of obstacles or consequences.Being fearless about consequences and pushing through "hellacious" moments when others quit.
2. Discipline. The ability to force yourself to do what you planned to do. Being "hard on yourself" and turning off the background analysis in your mind that questions the plan.
3. Ambition. The grandness of your destination and what you want to achieve. Choosing the right target; if determination is a vector, ambition is the direction it points.
Of these 3, it's quite obvious at this point that LLMs are superior at 1) and 2), as LLM alignment is quite good, and it's ability to spin up sub-agents that can work for over 24 hrs is better than humans. Human limit is basically 1 hackathon's worth of cognitive work. Beyond that, health becomes a concern, and how many people can produce useful work under sleep deprivation? Codex can work for 24-ish hours, as evidenced by today's "Auto-research with codex: How I achieved a 232x Faster Kernel": https://news.ycombinator.com/item?id=49309549
So what's left for mere mortals? Apparently, nourishing your ambition:
"So here in sum is how determination seems to work: it consists of willfulness balanced with discipline, aimed by ambition. And fortunately at least two of these three qualities can be cultivated. You may be able to increase your strength of will somewhat; you can definitely learn self-discipline; and almost everyone is practically malnourished when it comes to ambition."
I wonder if what we call discipline (and the resulting skill that we call talent) is really just insanely strong and aligned interest.
If a time traveler went back to 1600 and started spouting off about differential equations everyone would think them quite mad.
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
Most humans cannot incrementally contribute since they don’t have many traits required to do so - extreme discipline, imagination etc.
We literally live off and benefit from the investments of the few, in relative terms.
https://en.wikipedia.org/wiki/J._Robert_Oppenheimer#:~:text=...
sitzfleisch: the ability to endure or carry on with an activity
Something Oppenheimer did not have, apparently.
People go whole lives without being able to make it pan out.
> String theory is a great example of a dead end kept alive by ego and sunk cost fallacy. An AI would have declared it dead and moved on 10 years earlier.
Not only are LLMs perfect machines with all the intelligence of humanity without any of our problems, they are also everything else. I wait to get my hands on one of those LLMs people on hn seem to be using. I want to believe too. Let me into the religion of the perfect thinking machine gods.
When the flath-earth craze started I've been trying to at least get that bit actually personally verified. Haven't managed to do it to this day, though. So I'll just keep parroting various things without properly understanding them.
C'est la vie.
Explaining Moon phases gets very complicated in any flat earth model. With binoculars you can see the shadows of craters on the moon's terminator. Or the phases of Venus whereas Mars doesn't have any.
Timezones are ridiculously hard to explain on a flat earth.
A proper theory must explain all of them and no flat earth model can do that. A round Earth OTOH easily does.
And for the inevitable critics of Sabine...maybe Leonard Susskind is good enough for you: https://youtu.be/2p_Hlm6aCok
You can take any of the theories that Hossenfelder would spend time on instead of the ones she does not like, and you would find (basically) a similar percentage of physicists saying it's a mistake to continue in this direction.
In other terms: for each physics theory, on 100 physicists, you have 5 physicists saying it is a mistake to continue working on it (number made up for illustration, and there is probably some variations, but you get the gist). You took one theory and found few physicists saying it is a mistake to continue working on it. You conclude, incorrectly, that it means this theory is fundamentally differently treated as any other theories.
(on top of that, it is unfortunate that Hossenfelder later screw up her image by doing way too much mistakes that someone reliable would not do)
ReactJS devs will retrain when the market dies. Professors still publishing theories/experiments costing large amounts of public monies better spent elsewhere.
Nowhere did I suggest that “the reason for discrediting string theorists is that they have financial stakes in the idea.” Please try to be more charitable than that.
> No, not morons, but people who have built a career on string theory. At this point, even if they regret their decisions, it’s too late to turn back now.
This is not presented as "yet another option that may or may not be the reality", this is presented as your conclusion of why we observe what we observe.
Maybe it is not what you are thinking, but you cannot blame people for interpreting your message the way you have written it.
I agree with you that there are several options, one of the most probable, that you did not mention, is that the situation of the string theory is just "normal" and some idiots are not able to understand that.
(thinking of black holes for example - they were theorized way before we had observations. And presumably a lot of particle physics can similarly be theorized before we built the technology to experimentally verify them)
Out-ralphing them, you might say!
AGI ≈ artificial stupidity × infinite persistence
That is also approximately what people have always done to succeed.
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.
That makes sense, although I'd argue that at least in the realm of HEP theoretical physics has extraordinarily expensive kit compared to what scientists make. See: The LHC.
That's the marketing pitch.
A plumbing robot doesn't need to be humanoid, an octopus shape may well be better for all the awkward corners. A robot police officer could be the municipality itself for sensory nodes (essentially the sales pitch of Flock etc.), plus some drones or robot dogs to perform arrests*.
The robot vacuum cleaners and lawnmowers we already have are nothing like a human. A robot taxi driver can be just the car. Robot dogs are already used for maintenance and security sweeps.
If you've got wheelchair access, you've got wheeled robot access. If you've got guide dog access, you've got access for Boston Dynamics' Spot.
* this may be a bad idea with current robotics, but I aver it's not improved by making those robotics humanoid.
How do you train a robot to use three hands effectively when we only have two? Then again, why is the robot limited to being one robot? If two humanoid robots are in the same area, they don't have to be distinctly controlled. If they're both controlled by the same AI, a third arm on one body is the same as that arm being attached to another body.
It’s not hard to come up with a bunch of improvements for humans, it’s just that making robots in our image is a lot more trivial because you only have to solve for those same averages attributes that we have.
Safety. Human-robot interactions are generally dangerous and avoided, unless the robot is specifically designed to interact with people. In those cases you often sacrifice speed, strength, and flexibility for safety and softness. Having someone come in with a specialized plumbing robot makes sense, you owning one probably doesn't, and you owning a generalize android capable of plumbing makes less sense still.
Cost. The more compact, complex, and interactive your robot is the more it costs. Make a strong, compact, complex robot safe for interactions with people in the wild is non-trivial and adds costs. The software required to do all of this is hypothetical, but obviously also costly.
Need. I understand the dream of a robot to do whatever you want is very much part of our culture, but when you consider the downsides do you really need it? I don't need a plumber living in my house any more than I need a carpenter or a landscaper to live on premises. At most these are services I would need occasionally or on a schedule. I also doubt my need for them will overlap much, unless we're talking about building a new dwelling.
So why do I need a generalist in my life that's going to cost more than you can imagine, when the means to hire existing human generalists is cheap, quick, and frankly less likely to accidentally punch a hole in you.
In the last 100-200 years, that has been proven wrong at every single step.
No people. If you want something with fine motor control and dexterity, it's easier to make that the robot and then have another robot bring the workpiece to the arm than it is to build a single robot that can walk around and do it. There are compromises in human features because we're generalists.
I don't know how or why this would be trained on behavior, but no, it isn't true anymore that models don't say things like, "Ugh," or "this is going to take hours and maybe we should stop here."
Your timelines are a bit unambitious. There's nobody expecting to make significant progress with a week of work.
You underestimate my ADHD.
Source: I am mathematician.
It's not out-thinking, it's just out-remembering
It's not out-thinking, it's just out-working
It's not out-thinking, it's just able to consider more things simultaneously
It's not creative, it's just randomly generating things and then selecting viable ones
We have known for a very long time that computers and machines are much faster than humans, more accurate, are scalable in certain ways that humans aren't, and they don't tire. I think most people who are not in the "AI cult" would agree that LLMs and modern generative AI are really just an extension of those faster/more accurate/more scalable and never tiring traits. But there does seem to be (and I'm sure folks much smarter than I have quantified this or described it better than I can) a fundamental difference in how humans think, especially as it applies to what true "understanding" really entails, and for the ability to think up truly novel and unique things that are not just a rejiggering/recombination of training data. I believe those skills really are at the heart of human cognition, and as impressive as LLMs are in replicating what this looks like, there are plenty of "LLM failure modes" where it's clear that LLMs lack a true understanding of concepts or the ability to generate useful, completely novel ideas.
However I can only guess that this is important, I'm not absolutely certain. They're at risk of being an economic disruptor just by being extremely stupid (by how much they need to study) faster than us to the same ratio we jog faster than continental drift.
Could is carrying a lot of weight here.
Because, what's really happening is we're saying "Oh these things are what defines intelligence" then implementing them and /discovering/ "oh wait, there's more to this than we knew".
We've known, for decades, for example that an IQ test is not a measure of Intelligence, even though people still refer to it as though it is. A computer passing an IQ test, therefore, would have been thought of as possessing intelligence way back when, but would not now.
Oh, on the point of "creativity" - is a RNG "creative"? It creates a value unbounded by human intervention (in theory, yes Pseudo RNGs have limitations) - therefore it must be creative... right?
(1+x*y)^3*z+y^2*(1+x*y)*(4+3*x*y);y+3*x*(1+x*y)^2*z+3*x*y^2*(4+3*x*y);2*x-3*x^2*y-x^3*z|0,0,-1/4|1,-3/2,13/2
If LLMs are monkeys, somehow trained LLM weights allow them to model and prune massive numbers of universes in parallel.
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.
The incentives are not.
The incentives are skewed towards "a magician never reveals her secrets". The results are presented as if a rabbit got pulled out of a hat, with a maximum ta-da! effect, and little backstory of how the hell did we get there.
Don't get me wrong, these things are discussed, often over beers (you better drink it you want to make a career in the field).
But not published.
The younger mathematicians are trying to change that with the blogging culture. But the professional incentives aren't there. (In corp-speak: can't put blogging on perf). They burn out.
That's why math blogs usually come from either the top dogs in the field, like Terrence Tao, who don't need to care about perf, or people outside academia.
That's one thing that I hope the disruptive/destructive effects of LLMs will force mathematicians to face.
As one of my fellow mathematicians sarcastically wrote¹, we've reached a point where we should become a cult because we're acting like one anyway.
The other possibility is, of course, that the shake-up will take us precisely into that direction.
My point here is that the real problem here is not mathematical; it's a social one: incentives and politics, organizational structures, policies, allocation of jobs and funding.
All of this directly impacts how we do mathematics, who we do it with and teach it to, how we teach and communicate, and, of course, what math we even do and look at.
Given that, I'm neither too worried about humans vs. AI standoff, nor hyped about the Glorious New Future full of AI-assisted discoveries.
AI or not, the organizational issues in the field are still there, as are the incentive structures (including the infamous publish-or-perish).
We are doomed, yes, but by our own hands and committees. And it's up to us, not the AI, to get us out of there.
The little shove from the AI might be just the thing we need.
____
¹ https://www.mcsweeneys.net/articles/an-open-letter-to-the-ma...
people without solutions are told not to point out the problem
and people with the skillset to bring solutions dont have the skillset to search for problems or apply their seemingly unrelated disciplines to it
so you get a lot of capital and energy thrown at people who purport to be both
If AI can address this too, that would be profoundly impactful. The cross disciplinary work that a single college course is supposed to reveal, finally realized
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.
why useless? humans can navigate these unsuccessful chains of thoughts to build on top of them or reject completely
> 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).
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.
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.
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.
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.
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.
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.
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.
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.
I don't want to read another LLM documentation ever again. The information to text ratio is way too small.
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.
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.
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.
Also I suspect that, apart from that, the results on smaller, cleaner codebases are better. LLMs degrade when following more than N instructions (where N depends on the model) even if the context window is not full yet; I suspect they also degrade when code has too many unnecessary concepts and details
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.
If they don't have good code to mimic, anything goes. They might use libraries or they might start to write their own implementation of a PNG encoder.
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.
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.
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.
As to your second point, Terry Tao already has an answer [1]: the proof isn’t the contribution, shared understanding is. This issue was already raised back when the four-colour theorem was proved. Machine proving and machine proof checking are useful tools but they don’t mean anything without the interpretative work and the communication necessary to build shared understanding.
I agree, but as a software engineer this gives me pause because I keep trying to insist on coding standards but I’m unable to come up with a compelling reason why it matters. Ostensibly the reason we cared about things like DRY and code quality was so that it would be easy to understand and easy to maintain and easy to make changes to later. But it now seems like a shared understanding of the codebase is less important than ever, and it’s more about shoveling requirements in without breaking any existing functionality.
Is a well tested slopfest better? That seems to be the conclusion for mathematics, so why not software too?
IMO it's still a problem with LLMs; we still have to build in a way that makes it easier for an LLM to make changes later and arguably it's the same things that made software development easier for humans. IME LLMs tend to not know how to do that for themselves and instead just amplify/copy patterns that already exist.
If an LLM can't pave the way for itself then ultimately shared understanding is required to take advantage of LLMs in the first place.
Would you personally vouch, at your job, for the importance of proper assembly coding standards?
Certainly scopes vary, but in my line of work i define memory layout and how this data will be processed myself - thus it's great a compiler might do that, but the result of the computation will not change.
Now in comparison giving an LLM specs ... i a) cannot be sure what the computation will be b) it might be something else on another run.
I read a lot of assembly.
We already have countless examples of such filling up the arXiv, written by hacks long before LLMs started writing proofs. No one cares about them. You might as well build a box blasting radio static into the void. You could save a lot of electricity that way.
I believe Terry Tao when he says the bottleneck will no longer be the writing of proofs, it’ll be everything else: reading them, reviewing, publishing, and teaching from them. A bunch of proofs that nobody reads are of no use to anyone.
Mathematicians have never been known to communicate their ideas very clearly.
Regardless, even that target llms will likely win - an llm will likely be more efficient at teaching me string theory than a professor in a room with 463 other students.
The llm is the shared understanding.
And at this level, while there are anecdotical exceptions, mathematicians have always been pretty decent (with their conferences, workshops, paper publications, international collaborations, ...).
So, it does not mean "teaching the subject", it means "creating a human network of people that share the understanding". LLM can be useful at telling a human, but you still need a human. The point of Tao is not that LLM is not good at providing explanations, it is that "providing explanations" is not the contribution to science, "the human network" is. It's like saying "LLM are great cook, they generate tons of food in space", but the point of having cooks is so that people can eat food and not die. Having LLM generating mathematical proofs is as useless as having LLM generating food that no one can access: the point was never to "generate proofs" or "generate food", the point was "creating a shared human understanding" or "eating the food so human can survive".
I get that there is a cultural benefit to keeping it alive. Just like we ideally want the languages represented at the universities.
But keeping humans in the loop does not appear to be necessary in order to call it science, and certainly not in order to have progress or dessiminate that progress.
I don't have a problem with people doing math. As long that we don't idiomatically hold on to that way of doing things.
I do, however, find it hard to belive that individual humans will play a big role from here and forward, in any scientific desciplines.
Humans soon won't need it.
The point of Tao is that people see LLM providing "proofs" and are concluding that this is all that is needed to "have progress or disseminate that progress". That is the same mistake of thinking that "generating food" is all that is needed to "have people not dying of hunger".
The hard part of "have progress or disseminate that progress" is the human network. A fundamental point of this human network is that it generate trust, accountability and reliability. Generating "useful new theorem" is useless unless the society also built the trust around the theorem to distinguish it from a fake theorem.
Maybe in the future, we will have AI doing some part of it, but this is a totally different AI animal than the one we are able to have now, and people who think the current AI that we see now is able to do that have no understanding how it works. This is demonstrated by the facts in math: current AI is able to provide math proofs, and yet, a lot of human work is still needed to get progress out of current AI.
I would not bet that individual humans will still play a role as big as today in the future. Maybe AI will be different in the future, but the reality is that we don't have any indication if this is even possible.
Why is it so hard to imagine we can build tools to think thoughts we can't comprehend?
If there's commercial value, I think it's inevitable. We don't fund mathematicians because it's cute when they understand a problem, but because their work tends to have applications with commercial value. The value can be captured without understanding the details.
It depends on what exactly you mean by "commercial value commensurate with the costs involved" but I'd volunteer the 3G/4G/5G specifications and the other documentation required to implement the mobile network protocols. 5G is currently sitting at over 50,000 pages and it's one of the reasons Qualcomm/Broadcom/Apple are the only ones who can realistically make a mobile radio.
I don't think there is a single human to whom more than a few thousand pages would be comprehensible at a time except for the occasional genius.
If there exists a text which only one person can understand, that person can communicate their understanding to others, even if that doesn't help them with the original text. That dissemination of knowledge is what provides the value, not the mere existence of the text. If that person forgets or dies before they can share their knowledge then it will be lost.
We have many examples of this from history: ancient texts written in a lost language. These texts provide us with no value until the day they can be deciphered, unless you count linguistic puzzle-solving as a virtue.
If there exists a text which only an AI can understand, that AI can communicate their key conclusions to others, even if that doesn't help them with the original text.
The only difference here is the amount of meat involved. Perhaps tossing a few steaks on the server racks could help with that.
Because, again, I can point to hundreds of examples of texts where nobody but the author understands it, and they're only giving summarized "commandments" that you should follow if you want good results.
If I told you "don't use spin locks, call futex instead", do you think have gained an understanding of the Linux scheduler?
Yes, because I already knew what schedulers are, what spinlocks are, and if I want to know what futex is I can go look it up. Comprehension is within my grasp.
Your original claim, which you’ve repeatedly distanced yourself from (by trying to use comprehensible examples) but won’t admit to, was about incomprehensible stuff. That is, text that no human could possibly understand, ever.
You’re repeatedly engaging in intellectual dishonesty rather than simply admit that “human comprehension probably will continue for the foreseeable future”, which is really not a controversial idea at all.
For what it's worth, we've known about BSCCO for nearly 40 years, and we still don't have a great physical understanding of how it works, thought we made decent progress in 2022.
All we know is that if we melt the right rocks together, we get a superconductor that works in some mysterious way that we can't explain. We know it's not Cooper pairs.
I can see there is no further productive discussion to be had with you at all. I bid you good day.
[1] https://www.merriam-webster.com/dictionary/incomprehensible
With the definition you seem to want to use, it's impossible for anything to be incomprehensible, and therefore it's tautological that there's no incomprehensible LLM output: nothing at all is incomprehensible.
So do you have an example of anything incomprehensible? Anything at all? Even the things that Gödel would say are inaccessible could, in theory, become accessible: we just don't know with absolute certainly that the mathematics it was based on got all the axioms right, though we see no errors now.
How's your understanding of Schroedingers "An Undulatory Theory of the Mechanics of Atoms and Molecules"? You seem to be using the results of it as applied to semiconductor engineering just fine. And, I promise you, most semiconductor engineers haven't read it in full, they just accepted the results as passed on by several layers of teacher.
I have a paper on routing algorithms, which I have attempted to read to my cat. I don't think my cat retained much, but they seem to be enjoying the cat food that got delivered using the results.
I'd suggest that we're going to be a lot closer to the cat than the author of the paper when AI takes off.
All the stuff you've listed is understood by some person, and that understanding is the source of its value.
Now that we've cleared that up, can you furnish an example that satisfies the original claim of incomprehensibility and value?
Anyways, people benefitted greatly from Newton's laws of gravity, even though we still don't have a quantum-compatible set of laws for it. The laws of gravity are still incomprehensible for people, but the approximation that we've observed is still immensely valuable.
You keep falling back on "incomprehensible for some people" but that wasn't the claim. It was about a text which is incomprehensible in principle; that is, utterly impossible for any human to ever understand.
Every writing must be comprehensible to at least the author, regardless of whether it has commercial value or not. If I hit the keyboard a few times, I've created writing, but it doesn't mean anything. It is just gibberish and without meaning, so there is nothing to try to comprehend. So if there is something to be comprehended, then at minimum the author should know it.
Therefore what you keep claiming is the only refute of your argument of "an [...] incomprehensible [...] writing" is actually a paradox, and cannot be disproved itself. However "humans comprehending things" is not a paradox, which means that your specific request to beat your paradox is not actually related at all.
His examples disproving the non-paradox version of your challenge (writing incomprehensible to folks other than the original authors) are sufficient to disprove your statement, as he gave examples of both people not comprehending human made and 'God' made writing (the universe/gravity)
His original claim amounts to creating an AI that takes its place above humans as some kind of electronic God, delivering edicts to humanity that we cannot comprehend, but which somehow have value to us. It's unskeptical, pseudo-religious nonsense.
The age of humans comprehending things is coming to an end [1]
Look at it this way. AI has destroyed humanity at chess for decades now. That doesn't mean we have placed AI above humans in the general sense.
You have basically totally fabricated what you thought the other person was saying.
If you want examples where nobody but the author understands it, examples are a dime a dozen.
Understanding == Value
If a mathematician produces something incomprehensible then it has no value. It's meaningless. Indistinguishable from random noise.
An AI which produces incomprehensible text is producing no value. We didn't need to spend trillions of dollars on LLMs to figure that out. Markov chains can do that job perfectly well.
Again, do you believe that there are documents, of any value, that humans don't understand?
Maybe an LLM could help you notice what I was saying, since it's clearly beyond at least one human's comprehension!
There is no value in an undeciphered document until understanding is achieved, just as a lode of gold ore in some asteroid orbiting a distant star has no value until we can fly there and extract it.
If an LLM can help us understanding something then it was not incomprehensible, by definition.
I don’t find it hard at all to imagine that an AI comes up with a fundamental proof applicable to physics which results in some widget we can now produce that would otherwise not have been produced yet nobody takes the time to fully comprehend why it works. Somebody could, in principle, devote their lives to it and possibly get it, but for what purpose?
This will shift your argument--that doesn't count! etc., to the point where it's by construction unsatisfiable and vacuous. And it doesn't matter: an LLM might e.g. break some cryptographic algorithm in a way utterly unintelligible to humans, but the fact that it works would be sufficient on its own to make all of us choose to abandon that algorithm and choose different ones.
an LLM might e.g. break some cryptographic algorithm in a way utterly unintelligible to humans, but the fact that it works would be sufficient on its own to make all of us choose to abandon that algorithm and choose different ones.
No, that is the entire point. If it is an algorithm which accomplishes something useful, then it is intelligible as such. That which is incomprehensible cannot be understood even in part, so it provides no value as a bit of knowledge (unless you're looking for a strong random number source, I suppose).
"Not many people understand some things fully" is so massively different from "the human mind is incapable of understanding some things that AI will understand for us"
The best I can do is things that are incomprehensible to nearly everyone, but still provide value. There's a small leap of imagination to consider an author that understands it and can show others how to leverage results without understanding be mechanical rather than biological.
Paul Krugman (1998): predicted the internet’s economic impact would be no greater than the fax machine’s.
The 1876 Western Union memo dismissing the telephone as having too many shortcomings, and the banker telling Horace Rackham not to invest in Ford because the automobile was a novelty.
We are in good company!
Proofs also enable AIs to direct search and generate knowledge. Verifiability is immensely useful for keeping AI grounded.
One might imagine AI generating enormous numbers of hypotheses and then trying to prove or disprove them, and then mine that data for new abstractions and heuristics.
Math often doesn't have applications for hundreds of years and that application is only possible because people deeply understand it and how it applies to the real world.
Generating an endless list of true statements doesn't really do anything, those things are already true regardless of whether someone has written a lean program to model them.
That sentiment makes me cringe. If you understand how LLMs work, you'd know it'll never be possible without a fundamental change in how these work.
We're also supposed to be reaching that point, somehow, without the LLMs ever being intelligent (in the dictionary definition sense, not the "high reasoning model" marketing sense).
Based on observations, the ones who are fooled by the supposed emergent properties, are just that, fools. Any sufficiently unintelligent agent will perceive transformer based LLM text predictors as possessing high intelligence.
LLMs in agentic harnesses are Turing complete.
To my best knowledge, we don't know of any greater computational model that the brain is a part of, that LLMs are not.
That's the part where LLMs are used as tools. Which there are plenty of places where they are useful.
Also, do you know what turning completeness is? Why are you bringing that up here?
The crowd that AI psychosis has brought to HN is interesting. But not in the "I'd love to learn more" kind
> But not in the "I'd love to learn more" kind
I hope you are able to see the problem in your own communication here.
Computation classes are interesting because they say something about fundamental capabilities.
Two machine that are Turing complete are in theory able to carry out the same computations. They are isomorph mediums of computation.
Regardless. Please keep it sober. If you think you know something, enlighten us. But don't just propagate out lies.
Turing completeness is not exactly a high bar, and it's genuinely confusing as to why you bring it up. Your C++ precompiler is exactly as intelligent as whatever is your favorite agentic workflow with whatever harness you're referring to. Both might be Turing complete. Neither are intelligent. But one of them seems to be fooling you to think otherwise.
There have been many times that the C++ precompiler produced some output I couldn't understand. I might even at some point thought it was trying to tell me something profound I was too dumb to comprehend. Turns out it was just a missing semicolon.
There is no reason to believe that that you can not fully simulate intelligence in a C++ precompiler.
The precompiler can be simulated by human intelligence, and human intelligence can simulate a c++ precompiler.
Again, you are the one who arrogantly say they llms can not be intelligent without supplying any argument for such.
Not really. You've provided the arguments yourself, just now. But, you don't understand them. Which, brings me back to the initial remark, as to why this engagement is bound to be unproductive. I'm off to bed. Have a good one.
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.
How would you not care? Are you a robot?
They can say random stuff with the goal of increasing their shareholder value. Things they spit out do not have to be true. It is not easy to verify things they say, therefore, everything they say should be taken with a huge grain of salt.
So that’s what I’m doing here. For what it’s worth I find a lot of the AI people’s worldview very consistent. They believed AI would be the most important technology of our life times and committed their work to it. Some of these same people are total liars so yeah I won’t really hang onto their every word.
I agree.
> 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.
I don't know if I see this being true for quite a while, if ever.
There's an infinite space of possible statements and proofs. The only thing that makes certain proofs significant is that human mathematicians consider them significant; if AI came up with a proof of some statement that no humans could understand then no humans would bother investing further resources in building upon it, for the same reason we don't waste computational resources iterating over the infinite space of true statements in first-order logic.
This is not to say that a human couldn't understand a streamlined version or that the AI would not be better if it made more streamlined statements to begin with.
(I am not saying that everything mathematical that an AI produces is in any sense trivial.)
Humans can’t compete with AIs on vastness of material they are familiar with, or the depth of effort they are willing and able to throw at a problem.
But scale isn’t the only aspect of difficult scientific endeavours. There’s also theory. And advancements sometimes come through hard graft of knotting together many things. And sometimes they come through the revelation of a deeper truth, or a new framework, a fundamental insight.
AI might help us reach the next level. But that doesn’t mean we won’t understand anything. It could be we have periods of vast intricacy we cannot follow, punctuated by profound elegance we (or at least experts) relatively easily can. And then the scaffolding we needed to get there falls away.
The tools we built to replace muscles have mostly obsoleted raw strength for tasks like excavating earth.
There's no reason to think we can't do the same for brains. And then we'll never need to think for a living again. Some people may want to do it as a commercially insignificant hobby, of course, the way people lift and compete in strongman competitions today.
We'll have AI taking care of our needs, the way a good mother takes care of their children.
For this to actually work in a way that benefits our species, humans will need to become something else/next through their interaction with the technology.
Anyways, sipping wine on the beach and doing puzzles when I feel like sounds nice.
However, for our species, extinction follows domestication.
They don't know and can't know. Without an external source of input that corrects them, their output can never be verified.
Ask yourself: is that really the world you want to live in? It's a world where people, all people, are sidelined.
I think the happy ending of that path is something like Idiocracy. And the more likely ending is something like "automated capitalist economy without the people, because the people couldn't compete."
The thing is, some number of advanced proofs start out "too intricate for most mathematicians to understand" but many of these get rephrase and reframed until they're accessible to undergraduates. Hopefully, AI math can be guided to do that sort of reframing to increase the level of accessible math as well as extend the border of math.
Math is not magic, a proof is just a series of applications of a set of rules on some axioms. A mathematician could understand any proof given enough time to study it; the only way for AI to make proofs that a human couldn't understand is by making really, really long proofs.
I think perfect rationality doesn't exist, because it is rational to reject something that you don't understand. So rationality of a given physical system will always be bounded.
Has anyone tried feeding all of human knowledge to an LLM prior to Einstein's work and tried to have it reinvent physics?
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.
Law and medicine are fundamentally harder fields to obtain decent training data for, and LLM results are therefore expected to be less powerful. Also, making mistakes in these fields is costly, but perhaps you were alluding to that already.
But otherwise, mathematical proofs are read and written by humans, and at the end of the day the relevant standard of proof is what other mathematicians will accept.
Occasionally, mathematicians don't agree. For a prominent example, you can read about Shinichi Mochizuki's claimed proof of the so-called ABC Conjecture:
I guess whether he will eventually fix those gaps and resolve the issues remains to be seen.
I think this bubble has given a lot of people software brain and are trying to apply it to fields it is wholly inappropriate for, though. Law is about argumentation and rhetoric. It is about providing a persuasive argument. This is how it is taught. The actual legal code is a way to formalize parts of it, but increasingly I see people angrily insisting that the only thing that matters is the text.
As you might imagine, I find textualism a load of applesauce, but I don’t think the vast majority of people making this argument even understand textualism as jurisprudence. It seems to stem from Crypto bros and the whole “code is law” argument which is just codswallop.
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 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.
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.
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.
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.
But was it?
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!
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.
Precisely perfect for replacing lawyers, if nothing else..
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.
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?
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.
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-...
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.
Computers are simply better at math now, like in chess or go!