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Expo Workshop
Perspectives on Neurosymbolic Artificial Intelligence Research
Alexander Gray · David Cox · Luis Lastras

Sun Dec 06 10:00 AM -- 01:55 PM (PST) @ None

Neuro-symbolic AI approaches have recently begun to generate significant interest, as urgency in the field appears to be growing around various ideas for somehow extending the strengths and success of neural networks (or machine learning, more broadly) with capabilities typically found in symbolic, or classical AI (such as knowledge representation and reasoning). A general aim of this research is to create a new class of far more powerful than the sum of its parts, and leverage the best of both worlds while simultaneously addressing the shortcomings of each. Typical advantages sought include the ability to:

-Perform reasoning to solve more difficult problems
-Leverage explicit domain knowledge where available
-Learn with many fewer examples
-Provide understandable or verifiable decisions

These abilities are particularly relevant to the adoption of AI in a broader array of industrial and societal problems where data is scarce, the stakes are higher, and where the scrutability of systems is important.

This research direction is at once an old pursuit and nascent, and several perspectives are expected to be needed in order to solve this grand challenge. In this workshop we will explore several points of view, both from industry and academia, and highlight strong recent and emerging results that we believe are providing new fundamental insights for the area and also beginning to demonstrate state-of-the-art results on both the theoretical side and the applied side.

Author Information

Alexander Gray (IBM Research AI)

Alexander Gray serves as VP of Foundations of AI at IBM, and currently leads a global research program in Neuro-Symbolic AI at IBM. He received AB degrees in Applied Mathematics and Computer Science from UC Berkeley and a PhD in Computer Science from Carnegie Mellon University. Before IBM he worked at NASA, served as a tenured Associate Professor at the Georgia Institute of Technology, and co-founded and sold an AI startup in Silicon Valley. His work on machine learning, statistics, and algorithms for massive datasets, predating the movement of "big data" in industry, has been honored with a number of research honors including the NSF CAREER Award, multiple best paper awards, selection as a National Academy of Sciences Kavli Scholar, and service as a member of the 2010 National Academy of Sciences Committee on the Analysis of Massive Data. His current interests generally revolve around the injection of non-mainstream ideas into ML/AI to attempt to break through long-standing bottlenecks of the field.

David Cox (MIT-IBM Watson AI Lab)
Luis Lastras (IBM Research AI)

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