this post was submitted on 10 Apr 2026
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The problem is that you saw AI and thought LLM.
Machine Learning is a big field, AI/Neural Networks are a subset of that field and LLMs are only a single application of a specific type of LLM (Transformer model) to a specific task (next token prediction).
The only reason that LLMs and Image generation models are the most visible is that training neural network requires a large amount of data and the largest repository of public data, the Internet, is primarily text and images. So, text and image models were the first large models to be trained.
The most exciting and potentially impactful uses of AI are not LLMs. Things like protein folding and robotics will have more of an impact on the world than chatbots.
In this case, generating fast approximations for physical modeling can save a ton of compute time for engineering work.
Watson beat Ken Jennings over a decade ago. Protein folding was already done too, the people who did it even won a Nobel prize for it a couple years ago.
LLMs being the most visible part of AI after over 75 years of AI, isn’t because they’re the biggest or latest or greatest or whatever, it’s marketing. Plain and simple marketing.
Protein folding is far from "done" lol. Models have gotten a lot better but there is still more to do.
I didn’t say it was finished. I said people had won a Nobel prize for having done it. It takes decades to win a Nobel prize. My point was that it had been done years and years ago, not recently.
Probably more that it's the only AI normal people will interact with regularly. Your average person isn't going to run a protein folding application, but they will probably talk to chatgpt or use Google AI summaries.
Other people in this thread say physics simulations are inherently chaotic. If an AI model is trained on inherently chaotic data, how will the results not be chaotic or not worse?
Because physically speaking, chaotic and unpredictable are two different things - and why it works so well on this case: it's becoming a stochastic problem, not a deterministic one.
It's an awesome area for machine learning: you didn't need to understand the result and how it got created, it just needs to be "close enough".
The universe is chaotic. But chaos doesn't mean something isn't reproducible or doesn't follow a set of rules.
I work for a company that's been using machine learning software for slightly over a decade.