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                         AI Risk and Reasoning in Neurosymbolic AI
                         Artur d’Avila Garcez1
                         1
                             City, University of London, UK


                                        Abstract
                                        Current advances in Artificial Intelligence (AI) and Machine Learning (ML) have achieved unprecedented impact
                                        across research communities and industry. Nevertheless, serious concerns around trust, safety, interpretability
                                        and accountability in AI were raised by influential thinkers. Many identified the need for well-founded knowledge
                                        representation and reasoning to be integrated with ML systems. Neurosymbolic AI has been an active area
                                        of research for many years seeking to do just that, bringing together robust learning in neural networks with
                                        reasoning and explainability via symbolic representation and description. In this talk I will review the research
                                        in neurosymbolic AI and computation, and discuss how it can help shed light into the increasingly prominent
                                        role of safety, trust, interpretability and accountability in AI. AI has become the focus of large-scale research
                                        endeavours and has changed businesses. This led to an important debate about the impact of AI on education and
                                        society. It has been argued that the building of a rich AI system, semantically sound, explainable and ultimately
                                        trustworthy, will require a sound reasoning layer in combination with deep learning. Parallels have been drawn
                                        between Daniel Kahneman’s research on human reasoning and decision making and so-called AI systems 1 and
                                        2. I will revisit early theoretical results of fundamental relevance to shaping the latest research, such as the
                                        proof that recurrent neural networks compute the semantics of logic programming. I will also seek to identify
                                        bottlenecks and the most promising technical directions for the sound representation of learning and reasoning in
                                        neural networks. I will conclude by discussing the key ingredients for sustainable AI going forward, identifying
                                        directions and challenges for the next decade of research in the field.




                          CILC 2024: 39th Italian Conference on Computational Logic, June 26-28, 2024, Rome, Italy
                          $ a.garcez@city.ac.uk (A. d’Avila Garcez)
                           0000-0001-7375-9518 (A. d’Avila Garcez)
                                     © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).


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Workshop      ISSN 1613-0073
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