Artificial Intelligence: Teaching Machines to Think Like People

Geoff Hinton: Adapting Ideas from Neuroscience for AI

How a better understanding of neurons could lead to smart AI systems: An interview with Geoff Hinton, emeritus distinguished professor at the University of Toronto and an engineering fellow at Google.

A better understanding of the reasons why neurons spike could lead to smart AI systems that can store more information more efficiently, according to Geoff Hinton, who is often referred to as the “godfather” of deep learning.

Hinton is one of the pioneers of neural networks, and was part of the small group of academics that nursed the technology through a period of tepid interest, funding, and development.

Key Takeaways

• Large-scale analysis of the brain through research schemes like the Obama administration’s “Brain Initiative” have the promise to shed light on new aspects of the brain, giving AI designers new ideas.

• You can adapt ideas from neuroscience into ideas that are relevant to AI, though it takes some time. Hinton first thought, in 1973, about implementing a system with capabilities similar to those afforded by the fact synapses change on multiple timescales, yet it took until 2016 to publish a major paper on this area.

• It’s relatively easy to develop powerful perception systems, but we need new techniques to build systems capable of reasoning and language.

Jack: Why should we look at the brain when developing AI systems, and what aspects should we focus on?

Geoff: The main reason is that it’s the thing that ...