Where did your AI Scientist idea come from?
In the past couple of years, my group has developed AI algorithms that can automatically discover symmetry principles from data. For example, our algorithm identified the Lorentz symmetry, which has to do with the constancy of the speed of light. Our algorithm also identified rotational symmetry — the fact, for example, that a sphere doesn’t look any different regardless of how you rotate it — which is something it was not specifically trained to know about. While these are well-known properties, our tools also have the capability to discover new symmetries presently unknown to physics, which would constitute a huge breakthrough.
It then occurred to me that if our tools can discover symmetries from raw data, why don’t we try to generalize this? These tools could also generate research ideas or new hypotheses in science. That was the genesis of AI Scientist.
What exactly is AI Scientist — just a fancy kind of neural net?
It’s not a single neural network, but rather an ensemble of computer programs that can help scientists make new discoveries. My group has already developed algorithms that can help with individual tasks, such as weather forecasting, identifying the drivers of global temperature rise or trying to discover causal relationships like the effects of vaccination policies on disease transmission.
We’re now building a broader “foundation” model that’s versatile enough to handle multiple tasks. Scientists gather data from all types of instruments, and we want our model to include a variety of data types — numbers, text, images and videos. We have an early prototype, but we want to make our model more comprehensive, more intelligent and better trained before we release it. That could happen within a couple of years.
What do you imagine it could do?
AI can assist in practically every step of the scientific discovery process. When I say “AI Scientist,” I really mean an AI scientific assistant. The literature survey stage in an experiment, for example, typically requires a massive data-gathering and organization effort. But now, a large language model can read and summarize thousands of books during a single lunch break. What AI is not good at is judging scientific validity. In this case, it can’t compete with an experienced researcher. While AI could help with hypothesis generation, the design of experiments and data analysis, it still cannot carry out sophisticated experiments.
How far would you like to see the concept go?
As I picture it, an AI Scientist could relieve researchers of some of the drudgery while letting people handle the creative aspects of science. That’s something we’re particularly good at. Rest assured, the goal is not to replace human scientists. I don’t envision — nor would I ever want to see — a machine substituting for, or interfering with, human creativity.