Philippe Jacquet

Title Artificial Intelligence and Information theory, Accelerated Artificial Intelligence.
Abstract

We enumerate some of the theoretical limits of Artificial Intelligence imposed by Information Theory, in particular when quantify the tremendous quantity of information (entropy) which would be needed to train AI beyond human capabilities. We show how the use of rather simple and fast algorithms can indeed accelerate the use of AI in tracking mankind thoughts on social media.

Bio

Philippe Jacquet graduated from Ecole Polytechnique, Paris, France in 1981, and from Ecole des Mines in 1984. He received his PhD degree from Paris Sud University in 1989. Since 1998, he has been a research director in Inria, a major public research lab in Computer Science in France. He has been a major contributor to the Internet OLSR protocol for mobile networks. His research interests involve information theory, probability theory, quantum telecommunication, protocol design, performance evaluation and optimization, and the analysis of algorithms. Since 2012 he is with Alcatel-Lucent Bell Labs as head of the department of mathematics of dynamic networks and information.