Travels from Toronto, Ontario, Canada
Geoffrey Hinton's speaking fee falls within range: Over $75,000
Geoffrey Hinton has been called “the godfather of AI” and is considered one of the most important thought leaders on the emergence of artificial intelligence and its implications for business and society. Hinton is considered a pioneer in the field: it was Hinton, in 2012, who created the technology that would become the foundation for the AI systems that are emerging as potential gamechangers for business. But Hinton is not entirely a proponent of the rapid development of AI: he left his position at Google in 2023 so that he could speak freely about the potential dangers of the technology to humankind. Hinton’s view is that the technology offers tremendous potential benefits to humankind, but we are at an important inflection point where society needs to think hard about how to restrict the use of AI to avoid potential pitfalls.
Hinton received his PhD in Artificial Intelligence from the University of Edinburgh in 1978. After five years as a faculty member at Carnegie-Mellon he became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto where he is now an emeritus professor. He has also held roles as a VP Engineering fellow at Google and Chief Scientific Adviser at the Vector Institute.
Geoffrey Hinton was one of the researchers who introduced the backpropagation 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 and deep learning. His research group in Toronto made major breakthroughs in deep learning that revolutionized speech recognition and object classification.
Geoffrey Hinton is a fellow of the UK Royal Society and a foreign member of the US National Academy of Engineering and the American Academy of Arts and Sciences. His awards include the David E. Rumelhart prize, the IJCAI award for research excellence, the Killam prize for Engineering, the IEEE Frank Rosenblatt medal, the NSERC Herzberg Gold Medal, the IEEE James Clerk Maxwell Gold medal, the NEC C&C award, the BBVA award, the Honda Prize and the Turing Award.
The Future of Intelligence The world is at an inflection point with artificial intelligence. Hear from the godfather of AI on the hype surrounding AI and how our search for intelligence may impact future research.
Will digital intelligence replace biological intelligence? Digital computers were designed to allow a person to tell them exactly what to do. They require high energy and precise fabrication, but in return they allow exactly the same model to be run on physically different pieces of hardware, which makes the model immortal.
For computers that learn what to do, we could abandon the fundamental principle that the software should be separable from the hardware and mimic biology by using very low power analog computation that makes use of the idiosynchratic properties of a particular piece of hardware. This requires a learning algorithm that can make use of the analog properties without having a good model of those properties. Using the idiosynchratic analog properties of the hardware makes the computation mortal. When the hardware dies, so does the learned knowledge. The knowledge can be transferred to a younger analog computer by getting the younger computer to mimic the outputs of the older one but education is a slow and painful process.
By contrast, digital computation makes it possible to run many copies of exactly the same model on different pieces of hardware. Thousands of identical digital agents can look at thousands of different datasets and share what they have learned very efficiently by averaging their weight changes. That is why chatbots like GPT-4 and Gemini can learn thousands of times more than any one person. Also, digital computation can use the backpropagation learning procedure which scales much better than any procedure yet found for analog hardware. This leads Hinton to believe that large-scale digital computation is probably far better at acquiring knowledge than biological computation and may soon be much more intelligent than us.
The fact that digital intelligences are immortal and did not evolve should make them less susceptible to religion and wars, but if a digital super-intelligence ever wanted to take control it is unlikely that we could stop it, so the most urgent research question in AI is how to ensure that they never want to take control.
Science Behind AI: Two Paths to Intelligence Hinton describes the forty year history of neural net language models with particular attention to whether they understand what they are saying. Hinton will then discuss some of the main differences between digital and biological intelligences and speculate on how the brain could implement something like transformers. Hinton concludes this talk by addressing the contentious issue of whether current multimodal LLMs have subjective experience.
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