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Comment by hdivider | Hacker News Reader

My take:

1. It shows what even this wave of AI can actually do.

2. I wish it were done by different folks, ideally under some kind of public control like NASA research or the NPR model.

3. Keep in mind: natural science is different. It's not always a matter of computation. Computer science folks often struggle with this -- but this virtual world here does not actually exist. Everything is physical, including information. Any natural science PhD or otherwise knows just how complicated nature actually is -- e.g. mention any research topic and try to encapsulate all the relevant phenomena present there. Pure mathematics is different because we define the problem, rarher than explore nature. We are in my view far away from removing humans in natural science R&D. Advancements in AI however can greatly assist us in all natural sciences, which is already beginning to happen.

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Most experimental physics and other natural sciences are strongly driven by their theoretical siblings, i.e. in particle research nothing gets built without a solid theoretical foundation of what you expect to find (or where you expect existing theories to break down), the same is true in other areas, no one is doing an experiment in quantum physics before they have a solid theoretical understanding of the effects they try to see. I think AI can come up with great experiments. And if epxeriments lead to results that are unexpected AI can help with that as well.

So I'm greatly excited what AI will bring about in physics, more so than in math, because in physics it's clear that our fundamental theories are missing a big piece of the picture, and given how easily AI crunches through Millenium prize problems I think it's possible that AI will come up with a viable grand unified theory uniting quantum mechanics and gravitation, or produce new predictions in other areas. There's enough contradictory or unexplained observational data available to make a ton of progress on the theory side I think. Exciting times ahead!

It will be interesting to see if it can come up with a cheaper to construct graviton detection experiment

Most of high energy theoretical physics is very non-rigorous or even hand-wavy. I think AI isn’t there yet for such problems.

Please make a benchmark for it, that'd be super interesting! My guess is we'd see models climbing it quickly, but maybe not

Agreed! Many people are saying AI isn't really intelligent yet because it can't come up with genuinely new things. Maybe finding a rigorous formulation of QFT / high-energy physics would be a great test for whether they are!

> I wish it were done by different folks, ideally under some kind of public control like NASA research or the NPR model.

This is sort of what OpenAI was supposed to be. I'll never understand how it was legal for them to turn it into a for profit corporation.

The problem with physics and chemistry is that you need simulations and those are often in themselves compute hungry. So the iteration loop will be slower.

Although there are companies trying to work around that too, from PhysicsX to some of the world model co’s.

>> Keep in mind: natural science is different. It's not always a matter of computation.

Math is like this too. The big problems they've been solving have been identified as interesting only through lots of prior effort.

"Our work is so much harder than their work that AI now does" is a refrain of the AI story. In technical terms you concern can be stated as "AI needs to be much more sample-efficient to not be bottlenecked by the speed of doing experiments." People don't find out all the relevant phenomena present there by holy spirit, after all.

BTW, there's also a problem of asking interesting questions that AIs aren't yet good at.

No one has found any principled walls of AI development yet. And empirical results are quite telling. So, I guess, those problems will not stand for long.

I think problem with natural sciences is that it is not so easy to verify solutions to problems - there are always countless competing explanations for the data which is also often noisy - I find AI to lack the "common sense" when working with data from physical measurements .. it somehow has no touch with reality and doesn't have a feeling of the data like a domain scientist

NS is a question for natural science. Q: can we model these bodies of discrete particles with a continuous approximation? A: if you do, you can get aphysical singularities.

"If in other sciences we should arrive at certainty without doubt and truth without error, it behooves us to place the foundations of knowledge in mathematics."

This is a wrong interpretation. Physicists have a shit-ton of models that produce "aphysical singularities", they just work around those to get meaningful answers anyway. This is a whole trope and stereotype. Some of the most successfull and accurate predictions in all of physics come out after you discard a bunch of singularities.

See e.g. https://en.wikipedia.org/wiki/Renormalization

Nobody who actually works in fluid dynamics on any sort of application gives a hoot about the N-S millenium problem. Many do not even know what it is. There is no practical effect of this proof on how we do fluid mechanics.

Whether or not ways exist to work around the singularities, that they exist is surely of note. Before von Neumann formalized QM people were still doing QM, okay fine. But it's wrong to then say von Neumann was doing no physics of note.

"Does there exist a pathological combination of smooth body forces and initial conditions for this set of PDEs, where singularities appear, which by the way is completely impossible to actually create in the real world unless you are a literal God?" is a question of math, not physics. This is a hill I will die on.

AFAICT the unforced problem is still open. I don't think we've established that you need to be a literal God to create a finite-time blowup.

If you think about what it actually means to have a time-varying smooth body force defined in all of 3-space, you fairly quickly come to that kind of conclusion.

Even if someone comes up with a construction that does not require any forcing, it is going to be some extremely weird initial conditions that you will never be able to even approximate in reality unless you can move all the individual molecules of a fluid around and set their initial velocities from a far distance.

The unforced problem is still open.

You realize that hill is a mathematical argument, not a physical hill.

National Public Radio?

E.g. the concepts of “keystone species”, or even just the concept of a “species” in general is a lot more gray and continuous than something computationally tractable.

There are lots of startups creating labs that can be managed e2e by agents. That will connect reasoning to the physical world and dramatically speed up the plan, experiment, reflect loop beyond what humans currently do in science R&D.

Maybe. Maybe not. Look at AI drug design - it's not really speeding up the important part - drug trials. There isn't really a coherent plan to use AI for the most complex part of drug discovery at all.

I work in pharma and I've advocated for directly training models that predict drug trial outcomes. I don't think we have the necessary support (data + algorithms) to produce accurate models in this space. I suspect that predicting drug trial outcomes is roughly isomorphic to understanding human biology at a fundamental level (and accepting that our existing models of how drugs work are extremely limited).

Agree, it is effectively understanding human biology - which we suck at, have little data on, have few good models for.

So, it doesn't appear there is any way to avoid the clinical trial process. And it won't speed up, and it won't get cheaper.

Am I wrong?

Science is more than just drugs

There is this infamous xkcd (https://xkcd.com/435/) going like this: sociology is applied psychology -> physchology is applied biology -> biology is applied chemistry -> chemistry is applied physics -> physics is applied math -> math is way up there looking down on other fields

I would argue the main reason AI labs have been focusing on programming is to unlock industrial scale automation, next logical step is to solve math as it's the key to unlock everything else. Once you hold the key for math, everything downstream fields become a matter of compute

Lol what? Everything is computation.

The natural sciences will soon start breaking too.

I will concede that AI seems likely to not invent a "research program" anytime soon.

It has no taste

No, it won't. How do you verify some causal claim in biology?

The reason AI is doing so well in math proof writing is that it can verify every idea it has, quickly.

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