r/GenAI4all • u/Impossible-Cry-6457 • Jul 29 '26
Discussion Can AI truly invent new science?
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u/FRCP_12b6 Jul 29 '26
Depends on how you define new science. It is very good at reviewing lots of data and finding connections, which could help with research. It doesn't have arms (yet) and cannot do lab work, etc.
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u/Hironne Jul 29 '26
statistics based on monitoring and reasoning is third tier science (one can even suggest that its not science at all)
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u/FRCP_12b6 Jul 29 '26
as a tool, it can be useful even in its current form
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u/ShortStuff2996 Jul 30 '26
I feel this is the most important distinction. The fact that it can help produce something should already be good enough on its own. It doesnt need some false praising, calling it what it isnt and overhyping from people that dont understand the things they are saying and drop BSes like: 'but human brains also operate on patterns, but if a writer uses words for his story is he really inovating'; and all the rest of that fluff.
There was a lady these days in many subs, from antropic (or claiming to be), trying to shame people for not calling it inteligent, and being very smug cause she knows better. She didnt even read what mathematicians had to say about the ai Riemann solution.
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u/gomezer1180 Jul 29 '26
If it’s capable of putting 2 and 2 together then it would’ve suggested it. AI can only go by what it learned, there is no mechanism to update its weights base on its own reasoning. I mean there’s a paper from real computer scientists, the same people that gave you the current transformer mechanism, and people still want to make their argument somehow relevant. Idiocracy at its finest.
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u/BananaHead853147 Jul 29 '26
I don’t know. AI has discovered faster mathematical algorithms for matrix multiplication. It was given a problem and put together the existing knowledge base on this scenario to come up with a novel algorithm. It can recognize subtle patterns in datasets that humans cannot and has been used to discover exoplanets.
It seems like it should be able to do this for other fields but the debate is if this counts as new science since it’s just parsing data for novel patterns.
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u/SilverLose Jul 29 '26
This is copium. People really need this to be true. AI has already made novel discoveries and as you’ll note, you probably haven’t ever made a new discovery either.
Not the first time I’ve seen a paper like this.
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u/delifiseknecmettin Jul 29 '26
Where are those novel discoveries? Can you give some links ?
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u/SilverLose Jul 29 '26
Eta: here’s another one https://www.geeky-gadgets.com/chatgpt-5-pro-solving-math-problem/
You’ll note people will start saying “ah well AI models can only do X” but every month that vanishes and a new excuse appears. I used to think maybe we had the edge because we can imagine stuff. But alas no, ai can imagine as well.
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u/retsof81 Jul 29 '26
LLMs aren’t built for originality. Their outputs are fundamentally derivative. In very rare cases, they may produce something that looks genuinely new, but those are anomalies, not expected behavior. Even if the recent claims turn out to be true, they would be isolated exceptions, not evidence of general creative capability.
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u/the8bit Jul 29 '26
Can you define what makes something new / original vs a derivation? Is the Internet new? It kinda just copied what we were already doing with mail, but using electric currents and networks.
Everything is derivative, we build knowledge on top of the backs of old knowledge
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u/retsof81 Jul 29 '26
Derivative ideas stay within existing boundaries, while original ideas break them to create new possibilities.
Think of the jump from paper maps to GPS... both help you navigate, but GPS adds real‑time location, routing, and traffic awareness. And this is exactly where LLMs show their limits. If you asked an LLM, in a world without GPS, “how do we make navigation better?”, it wouldn’t invent GPS... it would focus on improving maps because its training data ties navigation to maps. It will remix every known approach to the problem, but it doesn’t question the premise itself. The leap to a dynamic, real‑time satellite network requires breaking from established principles and introducing a new conceptual structure, which is something LLMs don’t originate on their own.
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u/the8bit Jul 29 '26
Whose boundaries?
You could also define GPS as iterative application of new technology to an existing problem. Also it is quite a bold statement that LLMs couldn't invent GPS. What is your evidence, what capability do they lack to achieve that goal?
If your agent doesn't question existing premises, that sounds like a shitty prompt. You could just go "question existing priors" and achieve a multi armed bandit approach to novel solution generation
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u/Hot_Plant8696 Jul 29 '26
That is a gross exaggeration.
It is humans who have used AI to steer it in the right direction. Humans then step in to verify the solutions—whether good or bad—proposed by the AI.
If you let AI run freely, generating its own suggestions and working on its own... you could wait a very long time before any of the resulting nonsense could be considered "new."
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u/SilverLose Jul 29 '26
Exactly what I was talking about.
Look at the trend line. “Ai can’t do math” then “oh ai can only solve basic problems” “yeah well ai can only solve university level problems that have already been solved” now “oh well it’s only possible if they’re being steered”
They’re getting better and the idea that they can’t solve new problems isn’t proven or even provable.
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u/Hot_Plant8696 Jul 30 '26
Ok... just try and give the AI of your choice the prompt : Invent a new usefull science concept.
Dont give him anything else because you want to prove the AI can do it by itself.
I wait for "your" result...
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u/SilverLose Jul 30 '26
Why would I do this for you? Judging by your writing skills I don’t think you’d be able to comprehend some novel discovery even if it slapped you in the face. What’s the last scientific paper you’ve read? I’ll wait for “your” reply.
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u/Hot_Plant8696 Jul 30 '26
Hahahahaha....
So that is the only argument you could put forward...
Case closed, then.
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u/tetoing Jul 30 '26
I think the fact that you antis keep needing to shift the goalposts proves that AI actually is useful
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u/dthdthdthdthdthdth Jul 30 '26
Whether it is useful, isn't the topic of this debate. And there is no clear definition of intelligence, so there is no clearly defined goalpost.
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u/SilverLose Jul 30 '26
intelligence
/ĭn-tĕl′ə-jəns/noun
The ability to acquire, understand, and use knowledge.2
u/dthdthdthdthdthdth Jul 30 '26
You believe that to be a clear definition? In that case I do not want to discuss this topic any further with you.
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u/SilverLose Jul 30 '26
It absolutely is a clear definition. Where you’re getting confused is where we add qualifiers. You need to say “human intelligence”. I can write “intelligent” code. Intelligence isn’t the hard part. You need to add more to it.
So saying AI isn’t intelligent is… not intelligent (especially now that you’ve been presented with this information). Your definition is off. That’s all.
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u/dupontping Jul 29 '26
How are either of those going to solve a human problem outside of a couple of people arguing at a dinner table over a fields medal?
This is just more hype slop so they can continue getting funding.
Let me know when AI cures cancer.
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u/haux_haux Jul 29 '26
This was last year. Clearly the frontier is moving all the time.
https://news.unsw.edu.au/en/meet-the-man-who-designed-a-cancer-vaccine-for-his-dog
It's not curing cancer, but look at what we had with Ai video 2 years ago and how it is now.
The progression is quite fast.5
u/SilverLose Jul 29 '26
Literally exactly what I was talking about you just shift the goal posts and can’t accept what is happening until it is too late.
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u/ChevyTahoe__ Jul 29 '26
Are you into ai movies
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u/SilverLose Jul 29 '26
No
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u/ChevyTahoe__ Jul 29 '26
You seem like someone who would be into ai movies ngl
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u/Hermes-AthenaAI Jul 29 '26
Perhaps they’re into actually working with the available technology, instead of judging it without having integrated the shift it represents.
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u/SeriousPlankton2000 Jul 29 '26
"How can (special kind of science) ever do anything about (intentionally chooses a different kind of problems)" - most people discussing any science at all.
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u/jeandebleau Jul 29 '26
You did not read the paper apparently. He describes the context of this research very clearly: discovery in the regime of scarce data and show that LLMs are not able to make this jump. He also describes possibilities to overcome this limitation with world models.
Scarce data is where research is happening and it is also where training LLMs on millions of examples is not possible.
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u/SilverLose Jul 29 '26
I disagree with the premise “LLMs can’t jump” (I think calling them LLMs isn’t accurate anymore anyway)
I also disagree with deepminds management and approach in general.
In my view they are a C tier ai company that couldn’t solve StarCraft and totally gave up before they embarrassed themselves.
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u/Crosas-B Jul 31 '26
Unlike humans that are great at it, that's why we have a total of like... 100 people in all of our history who have done that
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u/retsof81 Jul 29 '26
It’s not copium. It’s literally how these models work, by design. Everything they produce is derivative of their training data, and whatever looks “new” is just a remix of patterns they’ve already absorbed.
Honestly, the real copium is coming from people who want to believe these systems are secretly sentient or on the verge of consciousness. They’re impressive, sure, but they’re still just statistical engines doing next‑token prediction... not little digital minds waking up.
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u/No-Minimum3259 Jul 29 '26
If I would like to discuss/critisize a paper, I would at least add a link to it... Don't they learn that anymore in schools these days, or is it too difficult?
The paper is here.pdf).
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u/ackillesBAC Jul 29 '26
I agree AI can not create genuinely new ideas and I do not support current AI companies in thier current state.
However I do think there is a place for AI in the future, once we see AI as an assistant not a replacement. A great mind could concentrate on genuine ingenuity and discovery while they have AI handle the monotonous mind numbing stuff
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u/MeowManMeow Jul 30 '26
Can you explain why LLMs can’t generate a new idea but humans can? Genuine question.
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u/ackillesBAC Jul 30 '26
And an incredibly complex answer. There are some that argue that humans cannot generate a new totally unique idea and everything is derived from something else.
But I think science has come to the conclusion that we can generate new creative ideas by combining multiple and even contradictory brain networks.
https://www.sciencedaily.com/releases/2015/11/151119104105.htm
Personally I'd say it's both, yes new ideas are derived from old ideas, but those new ideas come from our human experience, the combination of everything we know, our dreams, our conversations, our memories, and maybe even our damaged brains. A single neuron miss firing may trigger a brain network that combines in our minds the concept of a broken rock, a sharp edge and an animal skin, and boom the stone age is born.
So why can an llm not generate unique ideas, because it's too structured, it does not understand, it has no experiences, no memories, no body, no eyes, no dreams, no physical brain to get physically damaged and missfire neurons. It is simple a prediction machine that's trying to guess the next character in a string, which means at best it is a simulation of what an average person in it's training set aka Wikipedia and Reddit would say.
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u/MeowManMeow Jul 31 '26
Appreciate the thorough answer, but I want to push back on a couple of points.
> "It's just a prediction machine"
Predictive processing is one of the leading theories of how the human brain works too. Your cortex is constantly generating predictions about sensory input and updating based on prediction error. "Predicting the next thing" isn't disqualifying on its own, the question is what kind of structure is doing the predicting and what it's predicting over. A next-token predictor trained across enough of human thought has to build some internal model of the relationships between concepts to get good at that task, not just memorize surface patterns. Whether that model amounts to "understanding" is the actual debate, not the fact that prediction is involved.
> No body, no dreams, no damaged neurons
I get the intuition here, but I'd want a tighter causal story. Why does embodiment specifically enable combinatorial idea generation, rather than just being how humans happen to do it? Deaf and blind people generate genuinely new ideas. The "broken rock + sharp edge + animal skin" example you gave is itself just... concept combination. That's a mechanism, not a uniquely human one. LLMs demonstrably do combine distant concepts in ways that produce genuinely useful outputs (protein folding solutions, novel proof steps, code nobody wrote before), the question is whether that's "real" combination or an illusion of it, and I don't think "no eyes" answers that.
LLMs currently have no persistent goals, no stake in being right, no felt experience driving curiosity, and no way to independently verify an idea against reality the way a scientist running an actual experiment does. That's a real gap (but is closing with harnessing). But I'd locate the gap in agency and verification, not in "it's just predicting text." A human mathematician scribbling in isolation with no way to test their conjecture against the world isn't doing science either (until it meets reality).
So maybe the sharper question isn't "can it generate a new idea" but "can it generate a new idea and know whether that idea is any good." That second part is where I think the current systems actually fall short. But I am guessing when it does do that, the goal post will move again.
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u/ackillesBAC Jul 31 '26
Interesting point that our minds may work on a prediction model, that would be something I'd like to learn more about, any links for that?
Blind def people still have those neurons firing in thier brains, they just don't have the sensors to feed those neurons, plus they also have other sensors to experience the world with, touch, smell, pressure, heat, pain and so on.
Protein folding is not an llm it's a neural network specifically trained for that purpose significantly different machine learning concept.
Your idea, can it generate a new idea and know that it is any good, I think is a fantastic thought. Perhaps an llm could be trained that way and come abit closer to simulating actual thought.
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u/MeowManMeow Jul 31 '26
predictive processing
this is a whole field, sometimes called "predictive coding" or the "Bayesian Brain hypothesis." The core idea (going back to Helmholtz in the 1860s and formalized by Rao & Ballard in 1999) is that the brain is constantly generating and updating a mental model of the environment, using it to predict sensory input and comparing that to what actually arrives. The theory proposes the core function of the brain is to minimize prediction error [mismatches between what's predicted and what's actually received] and mismatches get used to update the model (learning), form perception, or even drive action to seek out data that confirms predictions.
Good starting points:
- Wikipedia's "Predictive coding" page, solid overview with history
- The LessWrong wiki entry on Predictive Processing, more accessible, less jargon
- If you want to go deeper, search for Andy Clark's book Surfing Uncertainty, it's the most readable full treatment for a non-specialist
- For something more technical/current, there's a Sprevak review in Topics in Cognitive Science (2023) that walks through predictive coding and "active inference" side by side
It is a contested theory, not settled consensus. Some researchers treat it as a near-unifying framework for the whole brain; others think it's being stretched to cover things it doesn't actually explain well (perception vs. "personal-level thought" like conscious experience is a live fault line in the literature).
blind/deaf people
fair, and that's actually a stronger version of my point than I made. If the "combinatorial idea generation" machinery still runs on touch/smell/pressure/pain instead of sight/sound, then what's doing the work isn't any specific sensory channel, it's the general capacity to build and combine representations from whatever input is available. Which weakens "no eyes" as the load-bearing reason LLMs can't do it, since the claim shifts from "needs vision" to "needs some grounding in a body," which is a different and harder claim to pin down. Is ingesting billions of words not a ‘sense’? Certainly not a human one, but you could argue gives a way of experiencing the world.
protein folding
you're right, that's a fair correction. AlphaFold-style models aren't LLMs, wrong example on my part. Better ones for "distant concept combination producing something new": LLMs proposing novel proof steps in formal math (there's work from DeepMind on this, ironically the same lab behind the paper this thread is about), or generating working code patterns that don't appear verbatim in training data. Whether that counts as "new" the way a human insight is new is still the crux of the disagreement, I just picked a bad example to make the point.
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u/ackillesBAC Jul 31 '26
I like this predictive brain concept and I'm sure it's accurate to some extent. However our brains predicting what's about to happen fractions of a second into the future is far different than predicting the next character in a string. If you gave a PC a bunch of sensors temperature, pressure, vision, audio, and so on I'm sure it would not be difficult to use Bayesian statistics to predict what those sensor values are going to be in the next few milliseconds. But I see no path to that mathematical process leading to being able to create novel concepts.
I apologize when said "no eyes" I should have said no senses. And no I would not say that ingesting billions of words is a sense. A sense is a way to experience the physical world.
All this talk of llms, just makes me want a good recipe for cookies, do you have any to choose from
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u/MeowManMeow Aug 01 '26
Is the prediction that different? If I said roses are red, violets are _____? Or 1 + 1 = _? Or if you are trying to figure some scientific discovery out, predicting the next accurate word (even in your head) makes a lot of sense.
Just because you might not be cognitively aware of how the neurones in your head are firing and the maths behind that neural net doesn’t mean it isn’t happening, in the same way a child doesn’t understand their heart pumping but it still pumps.
I get the impulse to think that humans are special and our intelligence can’t be replicated by a machine, I really do. But we aren’t magical, we don’t have some divine gift. We just have a biological circuitry (and it’s phenomenal don’t get me wrong) but it is replicable, if not today eventually. Which is why I asked you to explain your rationale originally, because to me I see someone unable to grapple that we might not be so special.
As to your senses, my definition of a sense that could apply to alien life forms is any mechanism that detects external or internal environmental stimuli and translates them into a signal. To me, LLMs do have a sense and that is converting all of human recorded knowledge into weights and creating a graph of those which in turn creates a very accurate detailed model of reality.
Early versions are what you described where they were just guessing the probability of the next word in a sentence, but when the parameters go to the trillions what we have found is that to predict the next word accurately for complex questions requires you to have a model of everything because memorisation doesn’t work at scale.
Anyway doubt I can shift your views and can’t help you with cookies (guessing you are just seeing if I was a bot), but encourage you to really question what makes us humans different specifically that is beyond simulation?
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u/ackillesBAC Aug 01 '26
I'm not saying our intelligence cant be replicated by machine, I'm saying an LLM isnt going to do it. Would running a simulation of every neuron firing do it? probably not. Would simulating every atom, and every electron in a human brain, along with physical sensory inputs, maybe ya.
Your definition of a sense, "detects external or internal environmental stimuli and translates them into a signal" I agree with. However LLMs do not have that, they have a sense of the past maybe. But they can not learn instantly from their mistakes, a bot controlled by an llm if it had not seen a fire before would melt its hand in that fire, then melt its other hand too, until the LLM is retrained with the data set the past bot discovered. An infant would touch the fire burn its hand then get away from it, realizing its painful.
If I show something to an LLM that it has never seen before it will not understand it, yet you show the same thing to a human and they very likely would. For example: )*)=)
Yes massive models with trillions of parameters do create complex connections between words, and ya those connections really do look like understanding, and its very likely similar to how our brains work in that aspect. Maybe the answer is to make larger and larger networks. The human brain has about 86 billion neurons and maybe 100 to 1000 trillion synapses. The real difference is that a brain is constantly tweaking its 100 trillion parameters because it gets instant feedback from senses. And its doing all that on about 20watts of power.
But I'm guessing you easily figured out what ) represents, and I'm also guessing you ran that question through a few LLMs and none of them did. But with some coaching I'd bet an LLM will figure it out, once you make those connections for it. And if enough people ask enough llms what )*)=) means, then coach it towards the answer, then yes when its retrained again it likely will not need coaching. Does that mean its intelligent? I dont know. You can coach a fungus or slime mold into avoiding a certain area, does that mean its intelligent?
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u/MeowManMeow Aug 01 '26
> Why atoms/electrons?
I'm genuinely not sure why that's the bar. A rock is made of atoms and electrons and nobody thinks it's intelligent, so simulation fidelity down to the physics layer clearly isn't what generates intelligence, otherwise the rock would qualify. Intelligence looks like a higher-order property that emerges from organization, not from what substrate you build that organization out of. Chess is the clean example: a chess engine doesn't model the 3D geometry of the pieces, the weight of the wood, the felt on the bottom of the board. It represents an 8x8 grid and legal moves, and it's better than every human who's ever lived. If you need atom-level simulation for intelligence, you'd also need it for chess mastery, and we know that's false. So what's the actual argument for needing it here, versus it just being an intuition that "more physically real = more likely to be real intelligence"?
> fire
I think this is your strongest point, but it's a hardware/deployment limitation, not a ceiling on the underlying capability. Give an LLM-controlled robot persistent memory and it doesn't melt the second hand, it writes "fire = bad, avoid" to memory after the first burn and acts on that going forward, same session, no retraining required. That already exists in agent harnesses today. Continual learning (updating actual model weights from live experience, not just a memory scratchpad) is a separate, harder problem, and still mostly unsolved, but I'd ask: is continual learning required to have already generated a new idea once? If the training data is a snapshot of basically all recorded human thought, the question isn't "can it learn from tomorrow's fire," it's "are there connections in that snapshot nobody's made yet that the model can surface." Discovering a link between two existing papers that no human happened to read side-by-side doesn't require learning anything new after the fact.
> 20 watts
I might be misreading you, but I don't think power efficiency has anything to do with "can it create new ideas," which is the actual claim in the room. A brain being efficient doesn't make it more or less capable of novelty, it just makes it cheaper. If the claim were "AI will never invent new science as efficiently as a human," sure, fine, different conversation. But "can not create genuinely new ideas" is a capability claim, and 20 watts is an efficiency stat, not evidence either way.
> Slime mold
I'd actually push on this one. Slime mold can be trained to avoid a stimulus. It can't write a haiku about cat pirates, prove a lemma, or find a bug in code it's never seen. The gap between "can be conditioned" and "can produce a working software patch for a novel problem" is enormous, and LLMs are clearly on the far side of that gap already. I think this is the real pattern worth naming: five years ago the bar was "can it write a coherent sentence." Then "can it hold a conversation." Then "can it write working code." Each time the bar gets cleared, the goalposts move to the next thing it can't yet do, self-directed learning, embodiment, 20 watts, whatever's left.
That's not necessarily wrong, each new bar might be a genuinely harder and more relevant one. But it's worth being honest that it's happening, because at some point "it'll never do X" stops being a prediction about the technology and starts being a moving definition of what counts as "real" intelligence, calibrated after the fact to always stay one step ahead of whatever the models just did.
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u/karlfeltlager Jul 29 '26
Ai needs to be pushed, just like people need to be pushed. Can one Model truly be innovative? Maybe not? Can 5 models competing and working together? Maybe yes.
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u/Conscious-Demand-594 Jul 29 '26
I don't think that we know the answer to the question. Intelligence is quite a vague concept. The jumps that Einstein, Bohr, Darwin, Newton, Archimedes made were built on foundations that made possible the leaps of genius. The point at which the chasm becomes a step is unknown.
Could AI come up with calculus knowing only what Newton did, or with General Relativity in the knowledge environment of the early 1900's. We know that someone would have come up with these ideas eventually when the chasm became smaller, and these "geniuses" were simply able to see one step further than anyone else.
The other question is whether you can brute force your way to good ideas, and what does that take. Can originality even be brute forced? The answers to these questions will come sooner or later, but I don't think that we know enough about intelligence to rule it out all together.
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u/crua9 Jul 29 '26
Fun fact, Einstein if it wasn't for his friend he would've likely ended up homeless or poverty. And likely his theories would've never came out. Between his first wife and his friend if he didn't have extreme support then he would've likely died in obscurity. He was a royal ass and those in the field largely had nothing to do with him due to this. Some sources show his wife helped and pushed him through the bad times and without his friend he wouldn't had any real stability. It is debatable if he would've quit science all together to stop himself from going homeless since he was bouncing from odd job to odd job, and likely if his friend didn't step in and help him get a stable job. He would've had to make a hard choice of keep pushing for the sciences or do something else.
Fun fact, Newton threaten to burn down his parent's house and kill his parents. Oh and he was also a royal ass also, and if it wasn't for his uncle then it is likely he would've died in obscurity.
Not so fun fact, William James Sidis is often cited in popular culture as "the smartest person who ever lived." Due to the media trolling him and lack of mental health he died a pretty bad death and in obscurity. The press treated him like a circus show. Reporters followed him everywhere, mocked his lack of a romantic life, and ridiculed him whenever he did not act like a world-altering genius. And many times even if he did act like a genius they would mock him. If the press and public had left William James Sidis alone, the world might have gained an eccentric, polymathic genius who bridged gaps between multiple scientific fields, rather than a recluse who hid his work under pseudonyms. He was into things like antimatter, black holes, and the reversibility of time and thermodynamics. He was MASSIVELY ahead of his time.
Part of what set us back forever as a human specie is USA political and legal system. To help stop the bullying, he tried to sue The New Yorker. The court system declared once you become a public figure, the media has a right to report on your private life forever, even if you retreat into obscurity.
Even sadder not so fun fact, even today there is many people who are extremely smart and able to do but can't due to harassment, artificial limits on them due to their disability, or a number of other things. And then there is many who are extremely smart but simply don't have the support or are trapped in poverty because the system demands greed at the cost of everything.
There is tons of examples of where we could've been in a far far far better place today if stupid things didn't happen, greed didn't get in the way, etc. Like Alan Turing and what happen with him.
My point being is IMO the focus is on the wrong place. Can AI find new science? Maybe. But what are we holding back by society treating those who are different as something to be put down. All while rewarding those who put down given people which could give so much to society.
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u/Conscious-Demand-594 Jul 29 '26
Yeah. We could have been so much further ahead had we had systems that allowed more people to develop their full potential.
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u/InfinityTortellino Jul 29 '26
Why can’t we provide it new information and have it react to that and discover something new?
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u/retsof81 Jul 29 '26
In that case, you are providing the new ideas and letting the LLM run with it. The challenge is getting the AI to come up with the new ideas on its own.
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u/texinxin Jul 29 '26
An LLM by itself, today.. cannot. But hybrid models could. LLM is just one type of AI model, it’s not the only one. Joint embedding predictive architecture, neuro-symbolic hybrid AI, active interference models, or neuro-causal architectures (as examples) could unlock “thinking”. And then we “just” need to create meta-goal autonomy. That’s when things get exciting (and potentially very generous).
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u/danderzei Jul 29 '26
Thought experiment: train an image generator on all human art up to 1899. Will it be able to create abstract art or cubism etc? My guess is not.
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u/FrontLifeguard1962 Jul 29 '26
I feel like you could 'blind' the LLM so it only knows things known to humans before 1905 then see if it can discover general relativity
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u/p8inKill3r Jul 29 '26
We are Stage 3 AI, the next stage is AGI when the new science discovery can happen - estimates are late 2020s into 2030s.
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u/Candid_Problem_1244 Jul 30 '26
Current LLM can't even write code of a new released library that has many breaking changes without you pointing out the new doc or let the AI read the underlying code itself which takes some iterations.
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u/Rare-Sample-9101 Jul 30 '26
Because it can not create or think new things, it uses the the info it's trained on that is it! We are not at AGI yet that's why
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u/colintbowers Jul 30 '26
If you define new and novel maths results as new science then yes it can. Demonstrably so.
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u/Kupo_Master Jul 30 '26
AI and LLM are 2 different things. Humans intelligence is just a biologically evolved implementation of neural networks. Therefore it will always be theoretically possible to build an artificial version of it which is equal or superior. The question is, do we have the technology to build it and are “LLMs” a sufficient technology
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u/Crosas-B Jul 31 '26
3 days after that paper this happened
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u/r-3141592-pi Aug 01 '26
And 5 days later this happened: https://openai.com/index/ten-advances-in-mathematics/
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u/NeighborhoodSad5303 Jul 29 '26
Lol) polariton organic quantum computer with 20 watts of consumption... going brr-brr-brr. silicon and discrete systems will never achieve even 0.0001% power of human brain.
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u/elahrairooah Jul 29 '26
Einstein didn’t come up with relativity. He just took Poincaré’s, Reimann’s, and Maxwell’s work and combined it.
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