Geoffrey Hinton
Geoffrey E. Hinton is a British-Canadian computer scientist, psychologist, neuroscientist, and artificial intelligence researcher born on 6 December 1947 in Wimbledon.
Hinton holds citizenship in both the United Kingdom and Canada. He was educated at King's College and subsequently at the University of Edinburgh, institutions that shaped his formation across the several disciplines he has worked within throughout his career. Alongside his research activities, he has worked as a university teacher, a role that has run in parallel with his standing as an artificial intelligence researcher.
His recognition within the scientific community is considerable and documented across several distinct forms of distinction. He is a Fellow of the Royal Society, a long-standing mark of standing in British science. He received the Turing Award, the highest honour in computer science. He also received the Nobel Prize in Physics, a notable distinction for a researcher whose professional identity encompasses computer science, psychology, neuroscience, and artificial intelligence research.
The recurring disciplinary preoccupations evident across Hinton's career draw on psychology, neuroscience, and computer science in combination, reflecting a sustained engagement with questions that touch on both biological and artificial forms of intelligence. English is the language in which he has conducted and communicated his research. His career, marked by the Turing Award and the Nobel Prize in Physics, represents a sustained body of work recognised across the fields of computer science and artificial intelligence research.
Quotes by Geoffrey Hinton
All you need is lots and lots of data and lots of information about what the right answer is, and you'll be able to train a big neural net to do what you want.
The NSA is already bugging everything that everybody does. Each time there's a new revelation from Snowden, you realise the extent of it.
Everybody right now, they look at the current technology, and they think, 'OK, that's what artificial neural nets are.' And they don't realize how arbitrary it is. We just made it up! And there's no reason why we shouldn't make up something else.
I get very excited when we discover a way of making neural networks better - and when that's closely related to how the brain works.
The paradigm for intelligence was logical reasoning, and the idea of what an internal representation would look like was it would be some kind of symbolic structure. That has completely changed with these big neural nets.