Claude Tadonki is professor in computer science at the MINES Paris - PSL (Paris/France). He holds a PhD (res. an HDR) in computer science from University of Rennes in 2001 (resp. Paris-Sud in 2013)  University. After six years of cutting-edge research in operational research and theoretical computer science at the University of Geneva, he relocated to France to work for EMBL, University of Paris-Sud, LAL-CNRS and then MINES ParisTech. His main research topics included High Performance Computing, Parallel Computing, Operational Research, Matrix Computation, Combinatorial Algorithm and Complexity, Mathematical Programming, Scientific and Technical Programming, Automatic Code Transformations. Claude Tadonki has worked at several laboratories and universities, has initiated various scientific projects and national/international collaborations, has given significant number of CS courses in different contexts including industries. He is an active member of well established scientific corporations and reviewer of high-impact international journals and top-rank conferences. He has published numerous papers in journals and international conferences.He is very active in international collaborations and has co-organized several HPC conferences and forums.



TITLE: Artificial Intelligence with HPC support

ABSTRACT: Artificial Intelligence (AI) has became a topic of extreme importance, both from the industrial and the research sides. Computers and so-called intelligent devices are now pervasive in our daily life and technical tasks. Basically, Artificial Intelligence aims at enabling computers to perform such intellectual tasks as decision making, problem solving, perception/identification, «understanding» human communication, modelling human behaviour so as to be able to predict his needs and actions, and the like.

I would define (pure) intelligence as the ability to mentally get into something you never learned. The fundamental difference with AI is the non determinism of the first and the determinism of the second, even if its is schematically perceived as a way to mimic the human brain by means of computer programs. A reference illustration of this duality is the blind test suggested by Alan Turing in 1950: if an observer fails in identifying the computer from its interaction with a human in a given delay, then the machine has succeeded.

Current and future expectations from AI are very challenging, and significant advances has been made in the topic. Powerful AI methods (e.g., Deep Learning) and  large-scale datasets (e.g., massive networks) processing are computationally expensive, hence the need for high performance computing. This talk will illustrate the fact and provide some technical insights related to high performance AI.


Última modificación: miércoles, 5 de julio de 2023, 17:29