Probably Approximately Correct Mathematical Physics

Explore the intersection of machine learning and mathematical physics in this 46-minute lecture by Dr. Shailesh Lal, a researcher with a PhD from the Harish-Chandra Research Institute. Delve into the applications of machine learning to string theory, black holes in string theory, and higher-spin holography. Gain insights into the concept of "Probably Approximately Correct" mathematical physics and its implications for understanding complex physical systems. Learn how cutting-edge computational techniques are revolutionizing our approach to theoretical physics and expanding our knowledge of the universe's fundamental principles.

Shailesh Lal: Probably Approximately Correct Mathematical Physics