Ai's Discovery of New Physics Poses Unexpected Challenges for Scientists
WHO:
Gizmodo - Adrian E. Bayer, co-author of the study and a cosmologist at the Flatiron Institute and Princeton University.
Gizmodo - Will Percival, co-spokesperson of the Dark Energy Spectroscopic Instrument (DESI).
WHAT:
Gizmodo - A study published in the Journal of Cosmology and Astroparticle Physics examined the use of AI in cosmology.
Science Daily - Scientists found that transfer learning can accelerate the search for new physics, reducing the need for expensive simulations.
Gizmodo - The AI developed biases that hindered its ability to identify new physics.
WHERE:
Gizmodo - The study was conducted at the Flatiron Institute and Princeton University.
WHEN:
Gizmodo - The study was published earlier this month.
WHY:
Gizmodo - The motivation for the study was to find methods that can learn efficiently without requiring extensive new simulation suites for every scenario.
Gizmodo - Testing alternative cosmological scenarios is critical for advancing understanding of the cosmos.
HOW:
Gizmodo - The team used a machine learning strategy called transfer learning, where a model learns from one task and applies that knowledge to related tasks.
Gizmodo - The AI performed well in understanding the standard model but struggled with new physics due to negative transfer, where it relied on previously learned information.
(UPDATED June 11, 2026 23:28 UTC)
A recent study published in the Journal of Cosmology and Astroparticle Physics, co-authored by Adrian E. Bayer from the Flatiron Institute and Princeton University, explored the application of AI in cosmology. The researchers discovered that while transfer learning can speed up the search for new physics and minimize the need for costly simulations, the AI developed biases that hindered its ability to identify new physics effectively. This work emphasizes the importance of finding efficient learning methods to test alternative cosmological scenarios and enhance our understanding of the universe.