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Avignon Université, le Laboratoire Biens, Normes, Contrats (LBNC) et le Laboratoire Informatique d’Avignon (LIA) accueilleront les

Rencontres HumanIA en Avignon 2019

26, 27 et 28 novembre 2019

Les Rencontres permettront de partager réflexions et travaux liés à l’intelligence artificielle, dans une perspective humaniste et multidisciplinaire.

Conférences et ateliers s’intéresseront  aux questionnements sociétaux associés au développement des applications de l’intelligence artificielle.

Les membres d’HumanIA sont invité.e.s à partager leurs travaux sous la forme de posters ou de courtes présentations (diaporama de 2 à 4 pages) à envoyer à humania (at) uqam (point) ca .

La forte implication dans les Rencontres de plusieurs facultés d’Avignon Université permet d’envisager de belles collaborations à venir!

Consulter le programme court et le programme complet.

S’inscrire aux Rencontres.

Recent excitement in artificial intelligence has been driven by advances in machine learning. In this sense, AI is a prediction technology. It uses data you have to fill in information you don’t have. These advances can be seen as a drop in the cost of prediction. The framing generates some powerful, but easy-to-understand implications. As the cost of something falls, we will do more of it. Cheap prediction means more prediction. Also, as the cost of something falls, it affects the value of other things. As machine prediction gets cheap, human prediction becomes less valuable while data and human judgment become more valuable. Business models that are constrained by uncertainty can be transformed, and organizations with an abundance of data and a good sense of judgment have an advantage.

Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare, and Professor of Marketing, at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab, Senior Editor at Marketing Science, and a Research Associate at the National Bureau of Economic Research. Avi’s research focuses on the opportunities and challenges of the digital economy. Along with Ajay Agrawal and Joshua Gans, Avi is the author of Prediction Machines: The Simple Economics of Artificial Intelligence (www.predictionmachines.ai) and editor of the NBER book The Economics of Artificial Intelligence: An Agenda.

Information et inscription ici ou écrire à Aude-Marie Marcoux