Conveners
Roundtable: What Discoveries and New Techniques will be Enabled by AI?
- Richard Gaitskell (Brown University)
- Sean Carroll (Johns Hopkins University)
- David Shih (Rutgers)
- Jesse Thaler (MIT)
- Ilija Nikolov (Brown University)
Description
Moderator: Richard Gaitskell (Brown University)
Respondents:
Sean Carroll (Johns Hopkins University)
https://www.preposterousuniverse.com/
Sean Carroll is a theoretical physicist and is the the Homewood Professor of Natural Philosophy at Johns Hopkins and the author of "Spacetime And Geometry," "The Particle at the End of the Universe," "The Big Picture: On the Origins of Life, Meaning and the Universe Itself," and many other works.
Jesse Thaler (MIT)
https://jthaler.net/
Jesse Thaler is a particle physicist and professor at MIT. He was named the inaugural Director of the NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) in 2020.
David Shih (Rutgers University)
https://sites.rutgers.edu/david-shih/
David Shih is a theoretical physicist and a professor at Rutgers University. His recent research has focused on applications of machine learning and artificial intelligence to data from both colliders and astronomy.
Ilija Nikolov (Brown University)
https://sites.brown.edu/vfm-research-cmpnmr/
Ilija Nikolov is a PhD student and member of the Condensed Matter NMR Group under Professor Vesna Mitrovic. Their research group incorporates AI-designed probes that monitor the quantum phase during magnetic resonance experiments. This gives us an invaluable look into the emergent phases that involve a complex interplay between charge/orbital, spin, and lattice degrees of freedom.
TOPIC:
The growth in the capabilities of AI systems appears exponential, if a little unevenly distributed.
AI is making remarkable new discoveries possible in two ways. On the data discovery side, it has made it possible to sift through the mountains of new experimental data (for example in materials science, biochemistry, particle physics and astrophysics) and find new patterns that would not be noticeable without these techniques. On the theoretical side, it has allowed modelers to narrow down the physical configurations (in quantum systems or in protein folding). What are the next areas that will benefit?
A big unknown remains–when will AI systems be able to come up with their own questions and physics models beyond what humans alone have envisioned? This leap has not yet been made, but it may be coming sooner than we think, or maybe it will turn out that there are types of intellectual inspiration required for scientific progress that machines can never achieve.
Join us for a discussion on how AI is going to change the capabilities of physics and physicists forever.