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Join us for this year’s Gödel Lecture with Nobel laureate and ‘Godfather of AI’ Geoffrey Hinton!

A pioneer in Neural Networks, Artificial Intelligence, and Machine Learning, Geoffrey Hinton’s research has profoundly shaped today’s AI systems. In 2024, he was awarded the Nobel Prize in Physics for foundational discoveries and inventions that enable machine learning with artificial neural networks.

• When: Friday, October 23, 17:00-18:30
• Where: TU Wien Kuppelsaal, Karlsplatz 13, 1040 Vienna

Further details about the lecture will be provided in due time, so stay tuned for updates!

Registration

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Attendance is free, but registration is required.

About Geoffrey Hinton

Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He did postdoctoral work at the University of Sussex and the University of California, San Diego, and spent five years as a faculty member in the Computer Science department at Carnegie Mellon University. He then became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto. He spent three years from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at University College London and then returned to the University of Toronto, where he is now an emeritus distinguished professor. From 2004 to 2013, he was the director of the program on “Neural Computation and Adaptive Perception,” funded by the Canadian Institute for Advanced Research. From 2013 to 2023, he worked half-time at Google, where he became a Vice President and Engineering Fellow.

Geoffrey Hinton is a fellow of the UK Royal Society, the Royal Society of Canada, the Association for the Advancement of Artificial Intelligence, and a former president of the Cognitive Science Society. He is an honorary foreign member of the American Academy of Arts and Sciences, the US National Academy of Engineering, and the US National Academy of Sciences. He has received honorary doctorates from the University of Edinburgh, the University of Sussex, the University of Sherbrooke, and the University of Toronto. His awards include the David E. Rumelhart prize, the IJCAI award for research excellence, the Killam prize for Engineering, the NSERC Herzberg Gold Medal, the IEEE Frank Rosenblatt medal, the IEEE James Clerk Maxwell Gold medal, the NEC C&C award, the BBVA award, the Honda Prize, the Princess of Asturias Award, the ACM Turing Award, and the Queen Elizabeth Prize for Engineering.

Geoffrey Hinton designs machine learning algorithms. His aim is to discover a learning procedure that is efficient at finding complex structure in large, high-dimensional datasets and to show that this is how the brain learns to see. He was one of the researchers who introduced the back-propagation algorithm and the first to use backpropagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts, and deep belief nets (deep learning). His research group in Toronto made major breakthroughs in deep learning that have revolutionized speech recognition and object classification.

About the Vienna Gödel Lectures

Named after the famous Austrian-American logician, mathematician, and philosopher Kurt Gödel (1906-1978) and introduced in 2013, the annual Vienna Gödel Lectures bring world-class scientists to Vienna. The lecture series illustrates computer science’s fundamental and disruptive contribution to our information society, and investigates how our discipline explains and shapes the world we live in—and thereby, our lives as such.

Photographs and/or video will be taken at this event. By attending, you grant TU Wien Informatics full rights to use the material (and any reproductions or adaptations) for fundraising, publicity, or other purposes. This may include (but is not limited to) the right to use in our print and online publicity, social media, press releases, and funding applications. If you wish that no photographs explicitly depicting you are used for these purposes, please send us an informal message. — Thank you!

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