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CIRSS Seminar - Quest for Scaling Earthly AI Holistically for a better Society


Friday, February 5, 2021
4pm - 5pm

Zoom

Event Details

Session leaders: Dr. Jinjun Xiong, Senior Researcher & Program Director, IBM T.J. Watson Research Center, Yorktown Heights, NY
Description: AI has become an increasingly powerful technology force that promises to impact all aspects of our society, from transportation to healthcare and education to sustainability.  But the diverse layers of software abstractions, hardware heterogeneity, and data privacy concerns have made the development of optimized AI solutions extremely challenging. This results in the business world’s expensive investment only on a handful of selective and “profitable” AI solutions, leaving many critical societal needs, such as equitable education and sustainability, much less addressed than deserved. To truly democratize the power of AI for the benefit of the society and humanity (“earthly AI” as compared to “Flashy AI”), a holistic approach with AI automation and application-system co-optimization holds the key to drastically simplify the development of AI solutions and greatly improve AI productivity at a much lower cost than current practices. This talk will discuss some of my related research efforts in the past decade on developing enterprise-scale AI solutions for the energy and education domains, lessons I learned, and areas I would like to call for more deep collaboration across applications, software, infrastructures and systems. I will contextualize these efforts in an ultimate research goal of transforming the current AI innovation ecosystem to truly democratize AI for a better society.
 
Bio:
Dr. Jinjun Xiong is currently a Senior Researcher and Program Director for AI and Hybrid Clouds     Systems at the IBM Thomas J. Watson Research Center. He co-founded and co-directs the IBM-Illinois Center for Cognitive Computing Systems Research (C3SR.com). His recent research interests are on scaling AI solutions with improved productivity by co-optimization applications, software and systems. Many of his research results have been adopted in IBM’s products and tools. He published more than 100 peer-reviewed papers in top journals and conferences. His publication won seven Best Paper Awards and eight Nominations for Best Paper Awards. He also won top awards from various international competitions, including the recent champion for the IEEE GraphChallenge on accelerating sparse neural networks, and champions for the DAC'19 Systems Design Contest on co-designing an object detection neural network model for low resource edge devices (both FPGAs and GPUs).