Kazuki Irie received his Ph.D. in Computer Science from Aachen University, Germany in 2020, after completing his undergraduate and Master’s studies in Applied Mathematics at École Centrale Paris and ENS Cachan, France. From 2020 to 2023, he was a postdoctoral researcher at the Swiss AI Lab IDSIA and lecturer teaching deep learning at the University of Lugano, Switzerland. He is currently a postdoctoral fellow at the Department of Psychology, Harvard University, USA.
His current research investigates the computational principles of memory, learning, perception, self-reference, analogy & decision making, and problem solving & creation with the dual goals of advancing general-purpose artificial intelligence and developing tools to better understand our own intelligence. The scope of his research interest has expanded from language modelling during his Ph.D. to general sequence and program learning during his postdoc, and now to computational cognitive neuroscience in his current post-postdoctoral research.
Title and abstract of his presentation:
Memory-Centric Deep Learning: Advancing machine intelligence & informing cognitive neuroscience
Memory is the cornerstone of intelligence. State-of-the-art artificial intelligence (AI) systems based on artificial neural networks (NNs) are no exception. They are powered by two core "memory algorithms": (1) learning algorithms that translate past experience into model parameters/programs, and (2) sequence-processing neural architectures that maintain and manipulate task-relevant information while solving a problem at hand. Together with internet-scale data, advances in these memory systems have enabled major breakthroughs in real-world AI applications, including increasingly general-purpose chatbots. Despite this progress, today's best AI systems still lack several cognitive capabilities associated with truly general or superintelligent machines---I argue that these limitations stem largely from the shortcomings of the existing memory algorithms. In this talk, I will present "memory-centric deep learning", a research programme focused on developing advanced memory algorithms to address key frontier challenges in AI. I will further show how progress in machine learning algorithms and models can also contribute to advancing the study of natural intelligence by providing computational models and insights that are not available in the traditional toolbox of cognitive neuroscience. Overall, this research programme aims to advance an integrated science of intelligence---one that unites machine intelligence and natural cognition, and creates new opportunities for multidisciplinary collaboration.
Tuesday, March 10th, 2026 at 9:00 am // Währinger Straße 29, 1090 Wien, Hörsaal 2, OG02