Scientists Build AI For Scientific Discovery Using Tech Behind ChatGPT

Scientists Build AI For Scientific Discovery Using Tech Behind ChatGPT

A team of international scientists, including those from the University of Cambridge, have started a new research collaboration that will leverage the same technology behind ChatGPT to build an AI-powered tool for scientific discovery. Many professionals also build their own AI machines to improve their professional work.

ChatGPT deals with words and sentences, but the AI will use numerical data and physics simulations from across scientific fields to help scientists model everything from supergiant stars to the Earth’s climate.

Scientist Building AI

A series of related papers were published on the arXiv access responsibility this week to accompany the launch of an initiative called Polymathic AI.

“This will completely change how people use AI and machine learning in science,” said Polymathic AI principal investigator Shirley Ho, a group leader at the Flatiron Institute Center for Computational Astrophysics in New York City.

New research shows how broadly trained AI models can match or surpass AII models specifically designed to stimulate turbulent fluid flow.

Even if the training data is unrelated to the issue, using large pre-trained models has significant advantages to creating one from the start.

Researchers For Building an AI 

Co-investigator Siavash Golkar, a guest researcher at the Flatiron Institute’s Center for Computational Astrophysics, said polymathic AI can show us commonalities and connections between different fields.

The team of scientists working on polymathic includes experts in various fields like mathematics, AI, neuroscience, astrophysics, and physics from the Simons Foundation, Flatiron Institute, New York University, Lawrence Berkeley National Laboratory, University of Cambridge, and Princeton University.

Transparency and openers are a big part of the project, Ho said. “We want to make everything public.

It is our goal to democratize AI for science in such a way that, in a few years, we will be able to serve a pre-trained model to the community that can improve scientific analysis across a wide variety of problems and domains.

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