When Models Meet the Real World: Lessons from Applied AI Research
Abstract: “In theory, there is no difference between theory and practice; in practice, there is.” While the authorship of this famous quote remains debated, it resonates deeply, particularly in the context of applied AI. When we move from the controlled environment of fundamental research to the complexities of real-world problems, especially with search and model-based AI approaches, the gap between expectation and reality can be vast. This talk will explore some of the challenges encountered when deploying models and search algorithms in practical settings. We’ll delve into the lessons learned from bridging this gap, discussing the trade-offs, unexpected pitfalls, and unique rewards of applied research.
Bio: Mauro Vallati is a UKRI Future Leaders Fellow and ACM Distinguished Speaker on Artificial Intelligence (AI) for the UK. He is a Professor of AI at the University of Huddersfield, where he leads the Autonomous Intelligent Systems research center and the AI for Urban Traffic Control research team. Mauro has extensive experience in real-world applications of AI methods and techniques, spanning from healthcare to train dispatching. In 2014, he started working on AI applied to the field of urban traffic control, a line of research that led to numerous high-impact academic publications, patents filed in the United Kingdom, China, and the United States, as well as the deployment of the resulting techniques in urban areas of the United Kingdom.