Ahmad, Muhammad AurangzebÖzönder, Şener2020-09-152020-09-152020Ahmad, M. A., & Özönder, Ş. (2020, August). Physics Inspired Models in Artificial Intelligence. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3535-3536).978-145037998-4https://doi.org/10.1145/3394486.3406464https://hdl.handle.net/20.500.12713/1019Abstract Ideas originating in physics have informed progress in artificial intelligence and machine learning for many decades. However the pedigree of many such ideas is oft neglected in the Computer Science community. The tutorial focuses on current and past ideas from physics that have helped in furthering AI and machine learning. Recent advances in physics inspired ideas in AI are also explored especially how insights from physics may hold the promise of opening the black box of deep learning. Lastly, current and future trends in this area and outlines of a research agenda on how physics-inspired models can benefit AI machine learning is given.eninfo:eu-repo/semantics/closedAccessAi and PhysicsArtificial IntelligenceMachine Learning and PhysicsPhysicsPhysics Inspired ModelsConference Object35353536WOS:0007495523030732-s2.0-85090425311N/A10.1145/3394486.3406464N/A