Friday, Feb 03 2023 - 10:00 am (GMT + 7)

Neurosymbolic Artificial Intelligence: Theory, Applications, and Challenges

About the speaker

Son Tran is a lecturer in computing at the University of Tasmania, Australia. His interest is in Artificial Intelligence, particularly in investigating whether Artificial General Intelligence is achievable. His current focus is on neurosymbolic computing for robust, transparent AI, and Human-AI teaming. He was a postdoctoral research fellow at the Commonwealth Scientific and Industrial Research Organisation, Australia. He obtained his Ph.D. in Computer Science at City, University of London, the United Kingdom, for the work on representation decomposition for knowledge extraction and sharing.

Abstract

Connectionist models such as deep neural networks have shown great power in learning with big data, achieving state-of-the-art performance in many narrow problems. Recently, much attention has been focused on improving their reasoning capability to solve intellectual problems logically in a larger context. For this challenge, neurosymbolic computing has been emerging as a promising approach where symbol manipulation can be integrated to compensate for the weaknesses of deep neural networks such as opacity and data hungriness. Neurosymbolic offers a systematic amalgamation between learning and reasoning and is considered a potential pathway toward Artificial General Intelligence. In the first part of this talk, I will present the theory behind neurosymbolic computing and the methods to represent symbolic knowledge in neural networks. The second part of the talk will showcase the applications of neurosymbolic and discuss some interesting research challenges in this topic.

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