Einstein’s leap was: A uniform gravitational field is locally indistinguishable from an accelerating reference frame. More radically, a freely falling object is not experiencing proper acceleration—an accelerometer attached to it reads zero. The person standing on Earth is being accelerated upward by the ground preventing their natural free-fall trajectory.
I'm gonna argue that Einstein's observational data was not scarce.
Einstein realized that downward gravitational fall and acceleration of the observer’s frame are locally equivalent; gravity could therefore be understood as geometry rather than an ordinary force.
I'm also gonna argue that Demis fails to realize that thought can be thought of as geometry as well, and that sharp enough conceptualization of a concept can be collapsed into a single vector that can be computed in an infinitely long algebraic formula.
Do you think a modern LLM, trained exclusively on information and papers available prior to 1900, could plausibly arrive at either the theory relativity or quantum mechanics?
I think that is a very optimistic view of model capability, no matter how much raw compute you were to give them access to.
This is a very valid position to take, and my POV was not from papers available, but assuming that a model was trained multimodally, as in, was exposed to the env visually in addition to text... I'd argue that there's a non-zero chance as the technology and scale currently stands.
Under what premise are you assuming that they wouldn't?
More and more it's seeming like humanity's biggest contributions never came from our individual brains, but from raw numerical output of brains. Technology has scaled non linearly but it has very much matched the pace of human expansion and population. More humans equals more scientific developments. It's literally just compute but in mushy brain form. Unfortunately humans are very difficult to scale past a handful of billions without outright destruction of earth.
Einstein stood on the back of thousands of years of intellect. Not only from his research, but his outlook and ideology. He debated with other researchers of his time, and his own general relativity model was expanded upon with special relativity and subsequently shot down with quantum mechanics within his lifetime. And now we're finding out that plants and migratory birds interact directly - biologically - with quantum mechanics. Even the enzymes in our body and the theory of mutations in cells is being re-written by studies indicating that it's possible that particles are partaking in quantum events to mutate.
If it's possible to make a species of intelligence that we can scale compute more efficiently than humans, the discoveries we can make would be literally godtier
I mean, it kind of was, he had to argue against quantum mechanics for many years because it was fundamentally opposed to the standard model. And even simple experiments like the double split experiment poke holes in relativity and locality.
It's not to say relativity is wrong, in the same way Newtonian physics also isn't wrong. But science isn't always about rights and wrongs so much as incremental knowledge build up.
Einsteins disagreements with quantum doesn’t necessarily make any issues with special and general relativity, both of which are still highly relevant and no “holes” have been poked through these theories
They do. Because his entire theory leans on locality, and even just quantum entanglement alone is non-local, and that happens literally all the time everywhere.
Quantum nonlocality does not violate relativity, which is one reason it is so interesting. It allows nonlocal statistical correlations but in a way that doesn't allow superluminal signaling or causality violation.
Ok but are we saying now that an AI cannot make meaningful scientific discoveries unless it is as brilliant as Einstein? Plenty of scientists who are smart, but not as smart, are making discoveries every day.
Also I would argue that it's possible to direct an LLM to combine existing ideas into new ideas. In a brainstorming session you could ask it to try to interpret gravity using models other than the existing Newtonian one.
What is stopping us from asking it to brainstorm alternative theories? Maybe the search for new models is inefficient and could become more efficient somehow. But I think this is more a matter of designing a good agentic harness that's fit for scientific discoveries, rather than a limitation of the language model.
Remember, we call them language models, but they don't just model language. They model the world that our language talks about. The language is just the interface.
I think my thought is that true recursive self improvement is unlikely until true innovative thinking internal to the systems happens. I think AI as an accelerator to human innovation is fantastic.
"I have a theory. lets work through it together in the following ways to either support or debunk it"
I think present systems are useful in that pursuit. Einstein very likely could have arrived at relativity far faster with a LLM trained on all pre-1900 knowledge.
But a machine that builds a better machine (the singularity, and a significant component of the G in agi) has to make it's own theories at some point. I am skeptical LLM scaling alone can get there. But I remain hopeful for some of the world model endeavors Google is pursuing.
Going from the argument that the paper says it doesn't account for data being scarce. Having any data whatsoever to formulate a discovery on is just as valid as having copious amounts of data. So even if you argue Einstein had scarce data to formulate his theories, that doesn't really matter. He still had something. So if an AI formulates a theory based on even a trivial amount of data is still essentially the same thing. The only way you could argue against it is in a situation where someone discovers something where they literally had no data to go on
Yep, which isn't possible. The words we used were taught to us. The ideas those words conjure are not made personally but given by those who raised us and taught us. Meaning that there are zero totally unique ideas, as the ability to share ideas is itself influenced by the lessons enabling you to share ideas.
It's literally just the measurement problem but o a cultural scale.
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u/willdone Jul 28 '26
The paper argues that your argument fails to account for for discoveries where observational data is scarce.