Join us to hear from a panel of experts in artificial intelligence and machine learning, and find out more about how machines have become intelligent without thinking.

The media reports almost daily on the latest developments in artificial intelligence (AI). Almost without exception, everything heralded as an 'AI breakthrough' is driven by a set of underlying models and algorithms, collectively known as machine learning.

The evening's talks will give you the opportunity to learn more about machine learning, from specialists at the University of Bath. They'll be followed by a discussion and the chance to put your questions to the panel. The evening's talks will feature:

• The Shortcut: how machines became intelligent without thinking in a human way, by Professor Nello Cristianini.
• Machine Learning and the Age of AI, by Professor Mike Tipping.
• How machines learn by interacting with the world, by Professor Özgür Şimşek.

This event is part of the AI UK Fringe 2024, a series of events exploring key topics around data science and AI, taking place between 4-29 March 2024.

AI UK is the UK’s showcase of data science and AI from the Alan Turing Institute.

Professor Nello Cristianini

Nello Cristianini is a Professor of Artificial Intelligence at the University of Bath, specialising in machine learning. Before that, he worked at the University of Bristol, and the University of California, Davis. He is the author of 'The Shortcut - why intelligent machines do not think like us', edited by CRC Press (2023).

Nello's talk will examine the origins of AI and how machines became intelligent without thinking in a human way. Current media coverage of AI assumes it has suddenly appeared out of the blue. Instead, the prevailing form of machine intelligence is the direct result of a decades-long series of decisions. These were shortcuts to address technical and business problems, and they are now behind many of the current concerns about the impact of this technology on society. Understanding how those technical shortcuts limit the options of regulators will be essential to safely co-exist with the present form of AI.

Professor Mike Tipping

Mike Tipping is Professor of Machine Learning in the Department of Computer Science at the University of Bath. Combining interests in both theory and application of AI, Mike is known for the introduction of the 'probabilistic PCA' data science framework, the invention of the 'relevance vector machine' predictive model, the origination of the 'Drivatar AI' concept behind Microsoft's Forza Motorsport' Xbox franchise, and the development of an award-winning machine learning-based payment fraud prevention system.

During his talk, Mike will explore machine learning and the age of AI. After decades of over-promising and under-delivering, it seems that artificial intelligence (AI) has finally come of age. AI is now ubiquitous across the media, society and throughout our daily lives. What has brought about this transformation, and why has it happened now? The talk will offer some answers to those questions in the context of five underlying driving forces.

Professor Özgür Şimşek

Özgür Şimşek is a Professor of Artificial Intelligence and Deputy Head of the Department of Computer Science at the University of Bath, where she leads the Artificial Intelligence and Machine Learning Research Group. Her research focuses on machine learning, drawing from a multi-disciplinary background that encompasses computer science, engineering, and psychology. Prof Şimşek is the Machine Learning Theme Lead of the UKRI Centre for Doctoral Training in Accountable, Responsible and Transparent Artificial Intelligence (ART-AI). She is a co-author of the book Classification in the Wild: The Science and Art of Transparent Decision Making, published by MIT Press.

Özgür will be addressing how machines learn by interacting with the world. How do AI systems learn to play difficult board games better than any human player? By using an elegant and intuitive approach to AI – called reinforcement learning – that allows an artificial agent to learn from its own experience as it interacts with the world. This talk will explore how reinforcement learning systems work, how they differ from other AI systems that are currently in use, and how they may shape our future. It will also discuss application areas, which include healthcare, autonomous vehicles, robotics, drug discovery, finance, traffic control, power systems, and natural language processing.