Two Paths to Intelligence

The Centre for the Study of Existential Risk is a multidisciplinary research centre within the University of Cambridge dedicated to the study and mitigation of existential risks that could lead to human extinction or civilisational collapse.

Centre for the Study of Existential Risk

Lecture by Geoffrey Hinton on two paths to intelligence, and the possibility of digital intelligence surpassing biological.

Dr Constance Tipper Lecture Theatre,  Department of Engineering, Trumpington St, Cambridge CB2 1PZ

The <a href="https://www.cser.ac.uk/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Centre for the Study of Existential Risk</a>, the<a href="http://lcfi.ac.uk/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"> Centre for the Future of Intelligence</a>, and the Department of Engineering are pleased to cohost a public lecture by <a href="https://www.cs.toronto.edu/~hinton/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Professor Geoffrey Hinton</a> of the University of Toronto. Abstract:Digital computers were designed to allow a person to tell them exactly what to do. They require high energy and precise fabrication, but they allow exactly the same computation to be run on physically different pieces of hardware. For computers that learn what to do, we could abandon the fundamental principle that the software should be separable from the hardware and use 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. I will briefly describe one such algorithm. 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 allows us to run many copies of exactly the same model on different pieces of hardware. All of these digital agents can look at different data and share what they have learned very efficiently by averaging their weight changes. Also, digital computation can use the backpropagation learning procedure which scales much better than any procedure yet found for analog hardware. This leads me 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 lecture will be held at the Dr Constance Tipper Lecture Theatre in the Department of Engineering, Trumpington Street, Cambridge (CB2 1PZ). To access the lecture theatre, please follow the signs from the road to Reception. Please note there are tickets available for ONLINE or IN PERSON. Seats in the Dr Constance Tipper Lecture Theatre are on a first come, first serve basis - anyone attending above the main hall capacity will have a seat in LR4. D﻿ue to high demand, if you are unable to attend the lecture, please return your ticket before the event through Eventbrite.

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