How Neuroscience Can Help Computer Vision *and vice-versa
Details
**Please note, this event will meet in re-space (library lower level LL11B). There will be signs directing attendees to the space the day of the event.
Summary:
Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in computer vision—giving a sense that the problem of vision is getting closer to being solved. In this talk, I will provide a brief overview of recent deep learning developments followed by a critical assessment of our actual progress toward achieving human-level visual intelligence. I will discuss the implications of the successes and limitations of modern computer vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems.
Speaker Bio:
Thomas Serre is Associate Professor in Cognitive Linguistic & Psychological Sciences at Brown University. He received a PhD in Neuroscience from MIT in 2006 and an MSc in EECS from Télécom Bretagne (France) in 2000. Dr Serre is Faculty Director of the Center for Computation and Visualization and Associate Director of Brown's animal behavioral core and the “SmartPlayroom”. Dr Serre has served as an area chair for machine learning and computer vision conferences including CVPR, AAAI and NeurIPS. He is currently serving as a domain expert for IARPA’s Machine Intelligence from Cortical Networks (MICrONS) program and as a scientific advisor for Vium, Inc. He is the recipient of an NSF early Career award as well as DARPA’s Young Faculty Award and Director’s Award. His research seeks to understand the neural computations supporting visual perception and has been featured in the BBC series “Visions from the Future” and appeared in several news articles (The Economist, New Scientist, Scientific American, IEEE Computing in Science and Technology, Technology Review and Slashdot).
Parking on campus:
Please PRE-REGISTER for parking. Directions and information can be found at https://web.uri.edu/visit/parking-information/