Thinking About the Technology Platform for Next-generation AI

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Machine capability has reached an inflection point, achieving human-level performance in tasks traditionally associated with cognition (vision, speech, strategic gameplay).  However, efforts to move such capability into the real world, where it can pervasively integrate in our lives, have in many cases fallen far short of the relatively constrained and isolated demonstrations of success. A major insight emerging is that structure in data can be substantially exploited to enhance machine learning. This talk explores how the statistically-complex processes of the real world can be addressed by preforming sensing in ways that preserve the rich structure of the real world. This evokes questions like: what sorts of structure are useful; what sorts of models can exploit such structure; what sensing technologies enable such structure; what computational architectures are required to harness such structure? While the many eventual applications of embedded AI are difficult to define, these foundational questions can help us prepare for providing the technology platform required in those applications nonetheless. This talk will investigate the algorithmic and technological implications, spanning from machine-perception models for sensor fusion, to large-scale form-fitting embedded sensors, to mixed-signal architectures for in-memory computing.



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  • San Diego, California
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  • Starts 22 March 2021 12:29 PM
  • Ends 07 April 2021 12:29 PM
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  Speakers

Prof. Naveen Verma

Topic:

Thinking About the Technology Platform for Next-generation AI

Machine capability has reached an inflection point, achieving human-level performance in tasks traditionally associated with cognition (vision, speech, strategic gameplay).  However, efforts to move such capability into the real world, where it can pervasively integrate in our lives, have in many cases fallen far short of the relatively constrained and isolated demonstrations of success. A major insight emerging is that structure in data can be substantially exploited to enhance machine learning. This talk explores how the statistically-complex processes of the real world can be addressed by preforming sensing in ways that preserve the rich structure of the real world. This evokes questions like: what sorts of structure are useful; what sorts of models can exploit such structure; what sensing technologies enable such structure; what computational architectures are required to harness such structure? While the many eventual applications of embedded AI are difficult to define, these foundational questions can help us prepare for providing the technology platform required in those applications nonetheless. This talk will investigate the algorithmic and technological implications, spanning from machine-perception models for sensor fusion, to large-scale form-fitting embedded sensors, to mixed-signal architectures for in-memory computing.

 

Biography:

Naveen Verma received the B.A.Sc. degree in Electrical and Computer Engineering from the UBC, Vancouver, Canada in 2003, and the M.S. and Ph.D. degrees in Electrical Engineering from MIT in 2005 and 2009 respectively. Since July 2009 he has been a faculty member at Princeton University, where he is also currently director of the Keller Center for Innovation in Engineering Education. His research focuses on advanced sensing systems, exploring how systems for learning, inference, and action planning can be enhanced by algorithms that exploit new sensing and computing technologies. This includes research on large-area, flexible sensors, energy-efficient statistical-computing architectures and circuits, and machine-learning and statistical-signal-processing algorithms. Prof. Verma has received a number of research and teaching awards, and has served as a Distinguished Lecturer of the IEEE Solid-State Circuits Society as well as on a number of conference program committees and advisory groups.

 





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