The Association for Computing Machinery, or ACM, has named Yoshua Bengio, Geoffrey Hinton, and Yann LeCun recipients of this year’s ACM A.M. Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing. For these reasons, the trio are known as the fathers of the deep learning revolution in artificial intelligence.
Bengio of course is a well known Professor at the University of Montreal and Scientific Director at Mila, Quebec’s Artificial Intelligence Institute. He is also a Co-Founder of Element AI and has his hand in several other Quebec based AI initiatives.
The award is named for the now famous Alan Turing, who’s pioneering work to use computers to break Nazi codes during WWII was documented in the 2014 Hollywood blockbuster Imitation Game. The A.M. Turing Award has been given since 1966, and past years recipients include some of the most brilliant minds in the history of computer science. Awarded by New York based ACM, the prize is referred to by many as the Nobel Prize of computing, and winners receive a $1 million (USD) purse.
“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society,” said ACM President Cherri M. Pancake. “The growth of and interest in AI is due, in no small part, to the recent advances in deep learning for which Bengio, Hinton and LeCun laid the foundation. These technologies are used by billions of people. Anyone who has a smartphone in their pocket can tangibly experience advances in natural language processing and computer vision that were not possible just 10 years ago. In addition to the products we use every day, new advances in deep learning have given scientists powerful new tools—in areas ranging from medicine, to astronomy, to materials science.”
As a group and as individuals, the fathers created the foundations for artificial intelligence which in turn led to technological advances that allowed the demonstration of the advantages of neural networks. Today this is being applied to language recognition, autonomous vehicles, robotics and natural language processing, technologies which are being adopted in industries from mining to insurance to home entertainment. Though it took decades and the scientific community was skeptical for much of that time, today their methodology has become the dominant approach in the field.
The ACM lists a few highlights of Bengio’s technical achievements, which include but are not limited to the following:
-Probabilistic models of sequences: In the 1990s, Bengio combined neural networks with probabilistic models of sequences, such as hidden Markov models. These ideas were incorporated into a system used by AT&T/NCR for reading handwritten checks, were considered a pinnacle of neural network research in the 1990s, and modern deep learning speech recognition systems are extending these concepts.
-High-dimensional word embeddings and attention: In 2000, Bengio authored the landmark paper, “A Neural Probabilistic Language Model,” that introduced high-dimension word embeddings as a representation of word meaning. Bengio’s insights had a huge and lasting impact on natural language processing tasks including language translation, question answering, and visual question answering. His group also introduced a form of attention mechanism which led to breakthroughs in machine translation and form a key component of sequential processing with deep learning.
-Generative adversarial networks: Since 2010, Bengio’s papers on generative deep learning, in particular the Generative Adversarial Networks (GANs) developed with Ian Goodfellow, have spawned a revolution in computer vision and computer graphics. In one fascinating application of this work, computers can actually create original images, reminiscent of the creativity that is considered a hallmark of human intelligence.
The trio will receive the award at a gala in San Francisco on June 15.
Photo courtesy Université de Montréal.