New AI models trained on physics, not words, are driving scientific discovery

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 New AI models trained on physics, not words, are driving scientific discovery


While popular AI models such as ChatGPT are trained on language or photographs, new models created by researchers from the Polymathic AI collaboration are trained using real scientific datasets. The models are already using knowledge from one field to address seemingly completely different problems in another. While most AI models — including ChatGPT — are trained on text and images, a multidisciplinary team, including researchers from the University of Cambridge, has something different in mind: AI trained on physics.
Recently, members of the Polymathic AI collaboration presented two new AI models trained using real scientific datasets to tackle problems in astronomy and fluid-like systems. The models — called Walrus and AION-1 —can apply the knowledge they gain from one class of physical systems to completely different problems. For instance, Walrus can tackle systems ranging from exploding stars to Wi-Fi signals to the movement of bacteria. That cross-disciplinary skillset can accelerate scientific discovery and give researchers a leg up when faced with small samples or budgets, said Walrus lead developer Michael McCabe, a research scientist at Polymathic AI.

“Maybe you have new physics in your scenario that your field isn’t used to handling, and just can’t burn the time working through all the possible models that might fit your scenario,” said McCabe. “Our hope is that training on these broader classes makes something that is both easier to use and has a better chance of generalising for those users, as the ‘new’ physics to them might be something another field has been handling for a while.” The Polymathic AI team recently announced Walrus in a preprint on arXiv.org and presented AION-1 at the NeurIPS conference in San Diego.

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