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GRASP Special Seminar: Alexandre Alahi, Stanford University, “Towards Socially-aware Artificial Intelligence: Human Behavior Understanding at Scale”

April 21, 2016 @ 2:00 pm - 3:00 pm


Over the past sixty years, Intelligent Machines (IM) have made great progress in playing games, tagging images in isolation, and recently making decisions for self-driving vehicles. Despite these advancements, they are still far from making decisions in social scenes and assisting humans in public spaces such as terminals, malls, campuses, or any crowded urban environment. To overcome these limitations, we need to empower machines with social intelligence, i.e., the ability to get along well with others and facilitate mutual cooperation. This is crucial to design smart spaces that adapt to the behavior of humans for efficiency, or develop autonomous machines that assist in crowded public spaces (e.g., delivery robots, or self-navigating segways).

In this talk, I will present my work towards socially-aware machines that can understand human social dynamics and learn to forecast them. First, I will highlight the machine vision techniques behind understanding the behavior of more than 100 million individuals captured by multi-modal cameras in urban spaces. I will show how to use sparsity promoting priors to extract meaningful information about human behavior from an overwhelming volume of high dimensional and high entropy data. Second, I will introduce a new deep learning method to forecast human social behavior. The causality behind human behavior is an interplay between both observable and non-observable cues (e.g., intentions). For instance, when humans walk into crowded urban environments such as a busy train terminal, they obey a large number of (unwritten) common sense rules and comply with social conventions. They typically avoid crossing groups and keep a personal distance to their surrounding. I will present detailed insights on how to learn these interactions from millions of trajectories. I will describe a new recurrent neural network that can jointly reason on correlated sequences and simulate human trajectories in crowded scenes. It opens new avenues of research in learning the causalities behind the world we observe. I will conclude my talk by mentioning some ongoing work in applying these techniques to social
robots, and the first generation of smart hospitals.


April 21, 2016
2:00 pm - 3:00 pm
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