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The article initially appears to suggest that all AI in science (or at least the author’s field) is hype. But their gripe seems to be specific to an architecture named PINN that seems to be overhyped, as they mention in the end how they end up using other DL models to successfully compute PDEs faster than traditional numerical methods.
I think this is mostly just a repeat of the problems of academia - no longer truth-seeking, instead focused on citations and careerism. AI is just a.n.other topic where that is happening.
Where are the AI-driven breakthroughs? Or even the AI-driven incremental improvements? Do they exist anywhere? Or are we just using AI to remix existing general knowledge, while making no progress of any sort in any field using it?
Does anybody else find it peculiar that the majority of these articles about AI say things like "of course I don't doubt that AI will lead to major discoveries", and then go on to explain how they aren't useful in any field whatsoever? Where are the AI-driven breakthroughs? Or even the AI-driven incremental improvements? Do they exist anywhere? Or are we just using AI to remix existing general knowledge, while making…
> To investigate AlphaEvolve’s breadth, we applied the system to over 50 open problems in mathematical analysis, geometry, combinatorics and number theory. The system’s flexibility enabled us to set up most experiments in a matter of hours. In roughly 75% of cases, it rediscovered state-of-the-art solutions, to the best of our knowledge.
> And in 20% of cases, AlphaEvolve improved the previously best known solutions, making progress on the corresponding open problems. For example, it advanced the kissing number problem. This geometric challenge has fascinated mathematicians for over 300 years and concerns the maximum number of non-overlapping spheres that touch a common unit sphere. AlphaEvolve discovered a configuration of 593 outer spheres and established a new lower bound in 11 dimensions.
https://storage.googleapis.com/deepmind-media/DeepMind.com/B...
(this is an LLM driven pipeline)
Are complex math problems just solvable by LLMs, as a stream of language tokens? I mean, there ought to be an element of abstract thought, abstract reasoning, abstract inter-linking of concepts, etc, to enable mathematicians to solve complex math theorems and problems. What am I missing?
One typical use case is that the simulation data takes months to generate. So for experimental use cases, it is very slow. So the idea was, to train a model that can learn the underlying physics. The model will be small enough so that inference won't be prohibitively expensive. So you can then use the ML model in lieu of the classical physics based model.
Where this usually fails is that while ML models can be trained well enough to replicate the training data, they typically fail to generalize well outside of the domain and regime of the training data. So unless your experimental problems are entirely within the same domains and regimes as the training data, your model is of not much use.
So claims of generalizability and applicability are always dubious.
Lots of publications on this topic follow the same pattern: conceive of a new architecture or formalism, train an ML model on widely available data, results show that it can reproduce the training data to some extent, mention generalizability in the discussion but never test it.
Does anybody else find it peculiar that the majority of these articles about AI say things like "of course I don't doubt that AI will lead to major discoveries", and then go on to explain how they aren't useful in any field whatsoever? Where are the AI-driven breakthroughs? Or even the AI-driven incremental improvements? Do they exist anywhere? Or are we just using AI to remix existing general knowledge, while making…
It’s people making money off hype until it dies and move on to the next scam-with-some-use.
Most scientists aren’t trying to mislead anyone, but because they face strong incentives to present favorable results, there’s still a risk that you’ll be misled.
In other words scientists are trying to mislead everyone because there are a lot of incentives; money and professional status to name just two.
A common problem across all disciplines of science.
The article initially appears to suggest that all AI in science (or at least the author’s field) is hype. But their gripe seems to be specific to an architecture named PINN that seems to be overhyped, as they mention in the end how they end up using other DL models to successfully compute PDEs faster than traditional numerical methods.
The only realistic evaluations of "AI" so far are those that admit it's only useful for experts to skip some boring work. And triple check the output after.