Yann LeCun is VP and Chief AI Scientist at Facebook and Silver Professor at NYU affiliated with the Courant Institute and the Center for Data Science. He was the founding Director of Facebook AI Research and of the NYU Center for Data Science. He received an EE Diploma from ESIEE (Paris) in 1983, a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU in 2003 after a short tenure at the NEC Research Institute. In late 2013, LeCun became Director of AI Research at Facebook, while remaining on the NYU Faculty part-time. He was visiting professor at Collège de France in 2016. His research interests include machine learning and artificial intelligence, with applications to computer vision, natural language understanding, robotics, and computational neuroscience. He is best known for his work in deep learning and the invention of the convolutional network method which is widely used for image, video and speech recognition. He is a member of the US National Academy of Engineering, the recipient of the 2014 IEEE Neural Network Pioneer Award, the 2015 IEEE Pattern Analysis and Machine Intelligence Distinguished Researcher Award, the 2016 Lovie Award for Lifetime Achievement, and a honorary doctorate from IPN, Mexico.

Title: La Prochaine Révolution de l'IA Sera Non-Supervisée

  


 

Maja Pantic obtained her PhD degree in computer science in 2001 from Delft University of Technology, the Netherlands. Until 2005, she was an Assistant/ Associate Professor at Delft University of Technology. In 2006, she joined the Imperial College London, Department of Computing, UK, where she is Professor of Affective & Behavioural Computing and the Head of the iBUG group, working on machine analysis of human non-verbal behaviour. From November 2006, she also holds an appointment as the Professor of Affective & Behavioural Computing at the University of Twente, the Netherlands. Prof. Pantic is one of the world's leading experts in the research on machine understanding of human behavior including vision-based detection, tracking, and analysis of human behavioral cues like facial expressions and body gestures, and multimodal analysis of human behaviors like laughter, social signals, and affective states. In 2011, Prof. Pantic received BCS Roger Needham Award, awarded annually to a UK based researcher for a distinguished research contribution in computer science within ten years of their PhD. She is an IEEE Fellow and an IAPR Fellow.

Title: Automatic Face Analysis

Human face is our preeminent means to identify the other members of our species and communicate affective and social signals. This talk summarises a number of aspects of human face and facial behavior and how far are we with automatic sensing and analysis of faces and facial behaviour by computers.


 

Alexandre Alahi is currently an Assistant Professor at EPFL. He spent five years at Stanford University as a Post-doc and Research Scientist. He has worked on the theoretical challenges and practical applications of socially-aware Artificial Intelligence in the context of transportation, i.e., systems equipped with perception and social intelligence. He was awarded the Swiss NSF early and advanced researcher grants for his work on predicting human social behavior. He won the CVPR Open Source Award (2012) for his work on Retina-inspired image descriptors, and the ICDSC Challenge Prize (2009) for his sparsity-driven algorithm that has tracked more than 100 million pedestrians to date. His research has been covered internationally by BBC, abc, PBS, Euronews, Wall street journal, and other national news outlets around the world. Alexandre has also co-founded multiple startups such as Visiosafe, and won several startup competitions. He was elected as one of the Top 20 Swiss Venture leaders in 2010.

Title: When AI meets Humanity for Transportation

Humanity is at the dawn of a digital revolution where Artificial Intelligence (AI) is poised to reshape the future of transportation with self-driving cars, delivery robots, and intelligent machines more broadly. To this end, a fundamental challenge is to develop machines that can not only perform intelligent tasks, but do so while co-existing with humans in the open world. Machines need to learn unwritten common sense rules, ethics, and comply with social conventions. Delivery robots should respect personal space, yield right-of-way, and ultimately “read” the behavior of others to effectively navigate crowded spaces.

While AI has made great progress in classifying images or playing games driven by well-defined set of rules, intelligent machines still lack common sense and the ability to make seamless, safe, moral and efficient decisions in crowded social scenes. To reach this ambitious goal, I propose empowering machines with a type of cognition I call socially-aware AI, i.e., systems equipped with perception and social intelligence. In other words, I aim to develop systems that have the capacity to i) understand human behavior and ii) effectively navigate and negotiate complex social interactions and environments. In this talk, I will present our latest works towards socially-aware transportation.

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