Artificial intelligence systems based on neural networks — such as ChatGPT, Claude, DeepSeek or Gemini — are extraordinarily powerful, yet their internal workings remain largely a “black box”. To better understand how these systems produce their responses, a group of physicists at Harvard University has developed a simplified mathematical model of learning in neural networks that can be analysed mathematically using the tools of statistical physics.

“Toy models”, like the one presented in the study just published in the Journal of Statistical Mechanics: Theory and Experiment (JSTAT), provide researchers with a controlled theoretical laboratory for investigating the fundamental mechanisms of neural networks. A deeper understanding of how these systems work could help design artificial intelligence systems that are more efficient and reliable, while also addressing some of the current challenges.

Researchers at McGill University have discovered that moderate ultraviolet (UV) light exposure is best when the technique is used to enhance vitamin D₂ in edible mushrooms. Excessive exposure leads to nutrient degradation or a plateau effect, they found. The paper also provides quantitative guidance. 

A study focusing on fundamental aspects of quantum physics led by Cal Poly Physics Department Lecturer Ian Powell analyzed how a changing magnetic field can make matter behave in unusual ways. Working in collaboration with student researcher Louis Buchalter, an article coauthor, Powell published the journal article “Flux-Switching Floquet Engineering,” which demonstrates that changing magnetic fields over time in time can create quantum states that do not exist in any stationary material. By engineering new quantum behaviors by timing the field, physicists can potentially create technologies that are very stable and hard to disrupt by “noise” or imperfections that can interfere with quantum technology functionality and avoid system errors.