Doing Science with AI

Modern science experiments can generate huge amounts of data, making computerized analysis essential for finding key data points and patterns. For example, the Large Hadron Collider produces about 25 petabytes of data per year (and that doesn't include the data that's filtered out in real time during experiments).

Now scientists are turning to artificial intelligence for help, but that raises the question of whether and where human intelligence will be needed in the future.

The deluge has many scientists turning to artificial intelligence for help. With minimal human input, AI systems such as artificial neural networks — computer-simulated networks of neurons that mimic the function of brains — can plow through mountains of data, highlighting anomalies and detecting patterns that humans could never have spotted.

Of course, the use of computers to aid in scientific research goes back about 75 years, and the method of manually poring over data in search of meaningful patterns originated millennia earlier. But some scientists are arguing that the latest techniques in machine learning and AI represent a fundamentally new way of doing science. One such approach, known as generative modeling, can help identify the most plausible theory among competing explanations for observational data, based solely on the data, and, importantly, without any preprogrammed knowledge of what physical processes might be at work in the system under study. Proponents of generative modeling see it as novel enough to be considered a potential “third way” of learning about the universe.