On the evening of November 29th at 7:30 pm, the School of Intelligent Science and Technology invited Stephen H. Muggleton, a member of the Royal Academy of Engineering, to give an academic lecture titled "Are AI's key challenges solved?".
Abstract:
Generative Pre-trained Transformer models (GPT) are a form of Large-Language model which has recently attracted wide-scale interest based on general open-ended query-answering. In this talk I will look at remaining challenges for the field of Artificial Intelligence. In particular, I will argue that although widely available systems, such as ChatGPT, partially address Alan Turing's Imitation Game [1950 paper in the Journal Mind] (now called the Turing Test) they fall short of the Supercriticality Challenge which Turing provided within the same paper. While Turing's paper initiated modern discussions on these topics, it was John McCarthy [1956] who named the field Artificial Intelligence. In a 2006 keynote speech at the Inductive Logic Programming conference, McCarthy introduced a Discovery Challenge which is closely related to Turing's Supercriticality. I will exemplify a range of human discoveries in Science, Engineering and Mathematics, and argue that neither Turing's nor McCarthy's challenges are addressed by existing GPT techniques. By contrast, to enable progress on Discovery Systems, further work is required on development of methods for identifying and explaining rare phenomena. Some initial work and directions for further studies will be described.
Presenter:
Stephen H. Muggleton, male, born in December 1959, is a professor, doctoral supervisor, Distinguished Professor at the School of Intelligent Science and Technology, academician of the Royal Academy of Engineering, chairman of machine learning research at the Royal Academy of Engineering, director of the Computational Bioinformatics Laboratory in the Department of Computer Science at Imperial College London, director of the Syngenta Systems Biology Innovation Research Center, member of the Management Committee of the Institute of Systems and Synthetic Biology at Imperial College London, and member of the Council of the International Society for Artificial Intelligence (MLS), Mainly engaged in research on machine learning and artificial intelligence, he is one of the leading figures in semiotic artificial intelligence and machine learning. He pioneered the field of inductive logic programming and successfully applied machine learning to systems and synthetic biology, achieving groundbreaking results. He has published more than 200 papers in top international journals and conferences such as Nature. According to Google Scholar's statistics, his work has been cited more than 19000 times, H-index 73, selected as a member of the International Association for Artificial Intelligence (AAAI), British Computer Society (BCS), International Society for Engineering and Technology (ET), Royal Society of Biology (RSB), and European Society for Artificial Intelligence (EurAl).
