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Abstract:
AI becomes increasingly important not only in industrial revolution but also in reshaping paradigm of scientific research. The current strength of AI is on its prediction side, while whether AI understands the data at hand remains under a hot debate. This can be resolved by creating models to understand the capability and limitations of current AI tools. Interdisciplinary disciplines would be necessary, among which physics can provide many powerful concepts. In this talk, I will expand these thoughts.
Biography:
Dr. Huang received PhD degree in theoretical physics in 2011, from the Institute of Theoretical Physics, Chinese Academy of Science. He then visited the Hong Kong University of Science and Technology as a visiting scholar, and later became a JSPS fellow in Tokyo Institute of Technology (2012-2014). In 2014, he moved to RIKEN Brain Science Institute as a research scientist. Since 2018, he has led the physics, machine, and intelligence lab at the School of Physics, Sun Yat-sen University, focusing on the physics basis of various kinds of neural computations. His book “Statistical Mechanics of Neural Networks” was recently published by Springer in 2022.
Host: Prof. Liang Li
Alternative online link: https://meeting.tencent.com/dm/U2CRHiUm4Mje (id: 223671477 password: 123456)