On the Relevance of Logic for Artificial Intelligence, and the Promise of Neurosymbolic Learning

In this position paper, we examine some of the assumptions held about logic and its relevance to the development of modern artificial intelligence (AI), which is primarily driven by deep learning. The paper aims to address fundamental misunderstandings about logic and ultimately argue for the benefits of symbolic formalisms in modeling uncertain worlds. While it is now recognized that statistical associations learned from data are limited in their ability to understand the world, there is still a great deal of criticism and hesitancy regarding the use of symbolic logic to achieve or support a broader vision for AI. By arguing that symbolic logic is more flexible than nonexperts believe, we make a case for neurosymbolic AI, which offers the best of both worlds.

Authors

• Vaishak Belle

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