Source: Deep Learning on Medium
The Godfather of Deep Learning
Geoffrey Hinton, one of the geniuses who helped create modern AI
Deep Learning has become the hottest subject in the field of artificial intelligence, thanks in particular to breakthroughs in image and language recognition in recent years that have approached or surpassed human levels of comprehension. It is basically a class of machine learning algorithms that uses artificial neural network with multiple layers to progressively extract higher level features from the raw input.
But how did it start and who are the main figures behind it? There are several deep-learning heroes to mention , but in general the English Canadian Geofrey Hinton — together with Yoshua Bengio and Yann LeCun — are referred to as the “Godfathers of AI”. Those three researchers have won the 2018 Turing Award, known as the ‘Nobel Prize of computing,’ for conceptual and engineering breakthroughs in artificial intelligence (AI). In this particular article, I will focus only on Hinton.
Dr. Geoffrey Hinton is VP and Engineering Fellow of Google, Chief Scientific Adviser of The Vector Institute and a University Professor Emeritus at the University of Toronto. Hinton received a Bachelor’s degree in experimental psychology from Cambridge University and a Doctoral degree in artificial intelligence from the University of Edinburgh. He coauthors several relevant fields including applications of Backpropagation, Boltzmann machine, Deep learning, and most recently Capsule neural network. Another interesting fact about him: Hinton is the great-great-grandson of logician George Boole whose work eventually became one of the foundations of modern computer science.
The are some relevant interviews with him that I like and would like to share.
In this non-technical interview, Dr. Hinton provides an overview of how things have evolved over the years since his first ideas were published in the 1980s/1990s.
In this more technical (as well as my favorite) interview conducted by Dr. Andrew Ng, they discuss various internal topics in Deep Learning, not just restricted to Dr. Hinton’s work, but several other researchers work. Almost all the papers mentioned in this interview can be found here, thanks to my fellow student Darryl Wright.
This a must watch interview for any data scientist.
Google I/O 2019
In this stage interview at Google I / O 2019, Geoffrey exposes the conceptual and engineering breakthroughs that have made deep neural networks a critical element of computing.
At some point Nicholas Thompson, Dr. Hinton’s partner in the chat, challenged the idea that machines could learn to perform any and all human brain activities: “There is no emotion that couldn’t be recreated? There is nothing of humans that couldn’t be recreated by fully functional neural networks? And you are 100% confident on this?”
Dr. Hinton replied that he was “99.9% sure.”
“What about that 0.1%?”
“We might be in a big simulation,” joked Dr. Hinton.
Informal Conversation between Nick Bostrom and Geoffrey Hinton
This is a very interesting informal conversation between Geoffrey and AI risk researcher Nick Bostrom in November 2015, conducted by the New Yorker’s journalist Raffi Khatchadourian, about the future and risks of AI.
Nick Bostrom is Swedish-born philosopher and polymath with a background in theoretical physics, computational neuroscience, logic, and artificial intelligence, as well as philosophy. He is best know as the author of Superintelligence and Simulation Hypothesis.
Still very active, over the last years Dr. Hinton have co-authors several paper publications, among my favorites CapsNet, a new approach to Computer Vision and Pattern Recognition.