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Title: Driving Autonomy - Machine Learning for Intelligent Transportation Systems
Abstract: Intelligent transportation systems, and autonomous driving in particular, have captured the public imagination. Most of us are excited about the art of the possible. Machine learning clearly has a role to play. In this talk I will argue that a systems view of autonomous driving affords the machine learning community particular opportunities - and poses some interesting challenges - beyond an out-of-the-box deployment of strategies and models developed in related fields.
Bio: Prof. Ingmar Posner leads the Applied Artificial Intelligence Lab (A2I) at Oxford University. He also serves as Deputy Director of the Oxford Robotics Institute, which he co-founded in 2016. Ingmar has a significant track record in designing machine learning approaches (shallow and deep) which address core challenges in AI and machine learning. Ingmar's goal is to enable robots to robustly and effectively operate in complex, real-world environments. His research is guided by a vision to create machines which constantly improve through experience. In doing so Ingmar's work explores a number of intellectual challenges at the heart of robot learning, such as machine introspection in perception and decision making, data efficient learning from demonstration, transfer learning and the learning of complex tasks via a set of less complex ones. All the while Ingmar’s intellectual curiosity remains grounded in real-world robotics applications such as autonomous driving, logistics, manipulation and space exploration. In 2014 Ingmar co-founded Oxbotica, a leading provider of mobile autonomy software solutions.
Author Information
Ingmar Posner (Oxford University)
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