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Hugues Bersini 


Université Libre de Bruxelles


Professor in Université Libre de Bruxelles and Co-Director of the IRIDIA laboratory. He teaches Artificial Intelligence, Object-Oriented technologies and Web Programming both for academics and for entreprises. Member of the Belgium Royal Academy of Science. He has been partner of various industrial projects and EEC esprit projects involving the use of adaptive fuzzy or neuro controllers, optimisation algorithms and data mining. Over the last 20 years, he has published about 300 papers on his research work which covers the domains of cognitive sciences, AI for process control, connectionism, fuzzy control, lazy learning for modelling and control, reinforcement learning, biological networks, the use of neural nets for medical applications, frustration in complex systems, chaos, computational chemistry, object-oriented technologies, immune engineering and epistemology. He is a pionneer in the exploitation of biological metaphors (such as the immune system) for engineering and cognitive sciences. He has written several books about computer science: some go along his teaching activity and have become with the years quite popular in the academic world, others are essays to help readers to better understand complex systems, and finally two books at the crossroad between science and fictions.


The two AI: conscious & unconscious 

Since the birth of AI 60 years ago, two RD directions have always compete: the conscious, symbolic and explicit AI, giving rise to software for planning, problem solving, expert systems, and the unconscious and implicit one, based on neural networks, machine learning and Big Data. Depending on the failures or successes of each research direction in overpassing human cognitive performance, they have alternatively been under the spotlight. I will explain the history and the reasons for these two scientific traditions on many different applications such as board games, selfless driving car, linguistic translation, and why, these days, the second one, re-baptized Deep Learning and Big Data (just old wine in new bottle) has gained this huge amount of attention. Should we worry about this impressive bifurcation?