THE RIGHT ACCELERANT

THE POSSIBILITIES - AND LIMITATIONS OF AI IN SCIENTIFIC RESEARCH

- DAMIEN MARTIN

When it comes to scientific research, AI can't come up with new angles or paths to follow. But what it can do extremely well is synthesize, predict, and generate data. And those qualities make it extremely valuable to advancing many fields, according to Positron Networks CEO and co-founder Siddhartha Rao.

"Unfortunately, you will not see ML models solving the theory of gravity. They won't be able to tell you what happens in the black hole yet," he told BOSS. "They're going to tell you a lot about how black holes operate and what is the current state of experimentation and hypothesis related to black holes. But they can't come up with novel concepts yet."

What they do well is very quickly process large amounts of data, help scientists build their research plans, and reproduce difficult-to-replicate experiments. It can be difficult to recreate the exact conditions of one researcher's lab in another, but with AI they can recreate the conditions and reproduce the results of physical and virtual experiments easily.

"Being able to solve across the reproduction side of the problem with AI/ML is super exciting in terms of how scientists are using AI and ML to accelerate their collaboration with peers."

PREDICTION TOOLS

With features like the ability to backspace, models like GPT 4.0 and Entropic can occasionally throw out a wild idea that's worth following up on and could lead to a breakthrough, but Rao likens that to playing roulette with scientific discovery. Hallucinations can be good, but they have to be rooted in logic.

"A lot of people thought Einstein's view of relativity was a hallucination when he came up with it, or that quantum mechanics was a hallucination as well. Your hallucination today is a scientific discovery tomorrow," he said.