Sustainable Artificial Intelligence
Cutting‐edge AI and Deep Learning technologies demonstrate outstanding performance in many important tasks requiring intelligent data processing under well‐known conditions, supported by massive computational resources and big data. However, the performance of these systems may drastically deteriorate when the data are perturbed, or the environment dynamically changes, either due to natural effects or caused by manmade disturbances. A neuromorphic perspective provides crucial support under such conditions. Human brains are efficient devices using 20W power (just like a light bulb!), which is drastically less than the power consumption of today’s supercomputers requiring MWs of power to solve specific learning tasks in an innovative way. Analyzing brain energy management helps developing computational and hardware implementations with drastic improvement in using resources, including energy, and provides a path towards sustainable AI. This talk overviews the challenges to intelligent systems, outlines crucial insights from brain studies, and introduces system designs combing the benefits of deep learning and neuromorphic technologies.
Date and Time
Location
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Registration
- Date: 06 Feb 2024
- Time: 11:30 AM to 01:00 PM
- All times are (UTC+11:00) Canberra
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- SR06
- UNSW Canberra
- Canberra, Australian Capital Territory
- Australia
- Building: 32
Speakers
Robert Kozma of University of Memphis
Sustainable Artificial Intelligence
Biography:
Robert Kozma (Fellow IEEE, Fellow INNS) Ph.D. in Applied Physics (1992) from Delft University of Technology, The Netherlands. He has held faculty positions at the University of California at Berkeley, USA; Otago University, Dunedin, New Zealand; Tohoku University, Sendai, Japan. He has been Professor of Computer Science and Mathematics at University of Memphis since 2000, where he is the founding Director of the FedEx Center for Large-Scale Intelligent Optimization & Networks. Past affiliations with US Air Force Research Laboratory; NASA Jet Propulsion Lab; Lawrence Berkeley Lab; Sarnoff Co., Princeton; U. Massachusetts, Amherst. He has 9 book volumes, over 300 papers, and 3 patents. Dr. Kozma has been the President of the International Neural Network Society (INNS), served on the Board of Governors (BOG) of the IEEE SMC Society, the AdCom of IEEE CIS, and BOG of INNS. He is EIC of IEEE Transactions on SMC: Systems. He is recipient of INNS Dennis Gabor Award.
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