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.