Abstract:

The millennia-old division between physical and living systems has been gradually breaking down from the middle of the last century. Ever since the 1940s, when Bohr, Schrodinger, McCulloch, Pitts and other scientists showed that aspects of both life and mind can be understood in terms of simple physical models, it's becoming clear that living organisms can be understood using only physico-chemical processes without requiring any "vitalism"-like property that is supposedly unique to living systems. That life and mind are just processes which emerge through interaction between inanimate components is shown brilliantly in the elegant work of Hopfield, later extended by Hinton, that was honored by the Nobel Committee this year. In this talk we will trace how physics illuminated and inspired the breakthroughs that eventually led to deep learning and the present era of AI.

About the Speaker:

Sitabhra Sinha is Professor of Theoretical Physics and Dean of the Computational Biology Graduate Program at the Institute of Mathematical Sciences (IMSc), Chennai and was earlier also adjunct faculty of the National Institute of Advanced Studies (NIAS), Bangalore and the Department of Computer Science, IIT Kharagpur. He did his PhD on chaotic neural networks at the Machine Intelligence Unit of the Indian Statistical Institute, Kolkata and postdoctoral research at the interface of physics & biology, in the Department of Physics of the Indian Institute of Science (IISc), Bangalore and later in the DIvision of Cardiology at the Weill Medical College of Cornell University, New York City. After joining the faculty of IMSc in 2002, he has held joint appointments in the Theoretical Physics and Computational Biology groups. His research interests span complex systems, nonlinear dynamics and statistical physics with applications to systems biology, economic & social sciences and computational linguistics.