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Geoffrey Hinton

44quotes

Geoffrey Hinton: A Pioneer in Artificial Intelligence


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Geoffrey Hinton is a renowned Canadian computer scientist and researcher in the field of artificial intelligence (AI). His groundbreaking contributions to deep learning have revolutionized the way machines learn from data, making him one of the most influential figures in AI.

Full Name and Common Aliases


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Full Name: Geoffrey Everest Hinton
 Common Aliases: Geoff Hinton

Birth and Death Dates


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Geoffrey Hinton was born on December 6, 1947. His current status is alive.

Nationality and Profession(s)


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Nationality: Canadian
 Profession(s): Computer Scientist, Researcher, Professor

Hinton's work has spanned multiple institutions, including the University of Toronto, where he currently serves as a professor emeritus. He has also held positions at the University of California, San Diego, and the University of Edinburgh.

Early Life and Background


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Growing up in London, England, Hinton showed an early interest in mathematics and physics. He pursued these interests at King's College Cambridge, graduating with a degree in Experimental Psychology. After completing his undergraduate studies, Hinton moved to the United States for graduate school, earning his Ph.D. in Artificial Intelligence from the University of Edinburgh.

Major Accomplishments


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Hinton's work has been instrumental in developing and popularizing deep learning techniques. Some of his notable contributions include:

Backpropagation: Hinton is credited with reinvigorating the backpropagation algorithm, which is a fundamental component of modern neural networks.
 Distributed Representations: His research on distributed representations has enabled computers to learn complex patterns in data.

Notable Works or Actions


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Some notable works and actions that have contributed to Hinton's influence include:

The Red Book: Alongside David Rumelhart and Yann LeCun, Hinton co-authored "Parallel Distributed Processing: Explorations in the Microstructure of Cognition" (1986), a seminal work on neural networks.
 Neural Information Processing Systems (NIPS) Founding: In 1987, Hinton helped establish NIPS as a premier conference for AI and machine learning research.

Impact and Legacy


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Hinton's impact on the field of AI is undeniable. His work has inspired new generations of researchers, leading to breakthroughs in computer vision, natural language processing, and many other areas. He has also received numerous awards for his contributions, including the 2018 A.M. Turing Award.

Why They Are Widely Quoted or Remembered


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Geoffrey Hinton's influence can be seen in various aspects of modern technology, from image recognition algorithms to voice assistants like Siri and Alexa. His work has democratized access to AI capabilities, making it possible for developers worldwide to build intelligent systems.

Through his groundbreaking research and dedication to the field, Hinton has left an indelible mark on AI. As a pioneer in deep learning, he continues to inspire new discoveries and innovations that shape our digital world.

Quotes by Geoffrey Hinton

Humans are still much better than computers at recognizing speech.
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Humans are still much better than computers at recognizing speech.
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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.
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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.
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The NSA is already bugging everything that everybody does. Each time there's a new revelation from Snowden, you realise the extent of it.
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The NSA is already bugging everything that everybody does. Each time there's a new revelation from Snowden, you realise the extent of it.
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My main interest is in trying to find radically different kinds of neural nets.
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My main interest is in trying to find radically different kinds of neural nets.
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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.
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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.
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I think the way we're doing computer vision is just wrong.
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I think the way we're doing computer vision is just wrong.
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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.
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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.
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I am betting on Google's team to be the epicenter of future breakthroughs.
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I am betting on Google's team to be the epicenter of future breakthroughs.
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I have a Reagan-like ability to believe in my own data.
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I have a Reagan-like ability to believe in my own data.
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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.
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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.
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