Dr. Narayan Srinivasa (tbc)
IEEE Fellow, Intel Corporation, (USA)
Opportunities, Challenges, and Threats Using Synthetic Data for AI
Industry and governments are making massive investments in AI to boost productivity and automation. Forward-thinking businesses are already ahead of the game. Research by Forrester in 2019 found that just over half of decision-makers in the global data and analytics space have already implemented, are currently implementing, or plan to expand/upgrade their AI investments. IDC predicts that three-quarters of enterprises will embed AI into their technology and process development by 2022 and that by 2024, AI will be integral to all parts of these businesses. A key requirement for AI is data. You can’t do AI without data. And not just any data. You need huge quantities of high-quality, accurate data to build useful models of AI for the real world. Although data-gathering behemoths like Google, Facebook, and Amazon have no problems in obtaining this data from their products and services, other businesses often lack access to the datasets they need. In this talk, I will first discuss synthetic data generation approaches using AI to mitigate this data access problem. I will then discuss the opportunities, challenges, and threats arising from these approaches to the democratization of AI and in accelerating AI adoption.
Dr. Narayan Srinivasa is an expert in machine learning and neuromorphic computing with a focus on novel algorithms, models and architectures for real world applications. He is currently with Intel Labs as Director of Machine Intelligence Research Programs and a Senior Principal Engineer, AI Research Science. His current focus is to accelerate research across Intel Labs in high risk but with potentially high impact for Intel and he is leading the efforts to work with US government agencies such as DARPA to enable this goal. From 2017-2019, he was the CTO of Eta Compute, developing energy efficient AI applications for edge devices. Prior to Eta Compute, he was at Intel Labs as Chief scientist and Senior Principal Engineer and was the chief architect for the 14 nm Loihi neuromorphic chip with on-chip learning. Before Intel, he was a Principal Scientist and Director for the Center for Neural and Emergent Systems at HRL Labs in Malibu, California and served in various capacities including the principal investigator for DARPA programs SyNAPSE, Physical Intelligence, UPSIDE and Co-Prinicipal Investigator for the DARPA BICA program. He also served as Principal Investigator on several programs within HRL Labs on topics ranging from sensing and robotics, adaptive control, autonomous vehicles and biologically inspired models for both Boeing and General Motors. He has 97 issued US patents and published over 96 articles in peer-reviewed journals and conferences. He was a TED-X speaker in 2012. Reports about his work have appeared in The Economist, MIT Technology Review, Wired Magazine, IEEE Spectrum and Forbes among others. He serves as an associate editor for the Frontiers in Neuromorphic Engineering journal and is a Fellow of the IEEE. He is routinely invited to be on panels across government and industry to review proposals and publications. He has a Ph.D. from the University of Florida and was a Beckman Post-Doctoral Fellow at the University of Illinois at Urbana-Champaign.