September 29, 2022
Recent advances in AI, Machine learning and Robotics have significantly enhanced the capabilities of machines. Machine intelligence is now able to support human decision making, augment human capabilities, and, in some cases, take over control from humans and act fully autonomously. Machines are becoming more tightly embedded into systems alongside humans, interacting and influencing each other in a number of ways. Such human-AI partnerships are a new form of sociotechnical system in which the potential synergies between humans and machines are much more fully utilised. Designing, building, and deploying human-AI partnerships present a number of new challenges as we begin to understand their impact on our physical and mental well-being, our personal freedoms, and those of the wider society. In this talk I will focus on the challenges in designing trustworthy human-AI partnerships. I will detail the multiple elements of trust in human-AI partnerships and discuss the associated research challenges. I will also aim to identify the risks associated with human-AI partnerships and therefore determine the associated measures to mitigate these risks. I will conclude by giving a brief overview of the UKRI Trustworthy Autonomous Systems Programme (www.tas.ac.uk), a £33m programme launched in 2020 involving over 20 universities, 100+ industry partners, and over 200 researchers.
Speaker Biography: Prof. Sarvapali Ramchurn is a Professor of Artificial Intelligence, Turing Fellow, and Fellow of the Institution of Engineering and Technology. He is the Director of the UKRI Trustworthy Autonomous Systems hub (www.tas.ac.uk) and Co-Director of the Shell- Southampton Centre for Maritime Futures. He is also a Co-CEO of Empati Ltd, an AI startup working on decentralised green hydrogen technologies. His research is about the design of Responsible Artificial Intelligence for socio-technical applications including energy systems and disaster management. He has won multiple best paper awards for his research in multi-agent systems, energy management, and disaster response, and is a winner of the AXA Research Fund Award (2018) for his work on Responsible Artificial Intelligence.
November 17, 2022
We propose a method to introduce uncertainty to the surface reconstruction problem. Specifically, we introduce a statistical extension of the classic Poisson Surface Reconstruction algorithm for recovering shapes from 3D point clouds. Instead of outputting an implicit function, we represent the reconstructed shape as a modified Gaussian Process, which allows us to conduct statistical queries (e.g., the likelihood of a point in space being on the surface or inside a solid). We show that this perspective improves PSR’s integration into the online scanning process, broadens its application realm, and opens the door to other lines of research such as applying task-specific priors.
Speaker Biography: Silvia is a fourth year Computer Science PhD student at the University of Toronto. She is advised by Alec Jacobson and working in Computer Graphics and Geometry Processing. She is a Vanier Doctoral Scholar, an Adobe Research Fellow and the winner of the 2021 University of Toronto Arts & Science Dean’s Doctoral Excellence Scholarship. She has interned twice at Adobe Research and twice at the Fields Institute of Mathematics. She is also a founder and organizer of the Toronto Geometry Colloquium and a member of WiGRAPH. She is currently looking to survey potential future postdoc and faculty positions, starting Fall 2024.