Facebook’s Yann LeCun, Mila’s Yoshua Bengio and Google’s Geoffrey Hinton share the 2018 Turing Award. (ACM Photos)
The three recipients of the Association for Computing Machinery’s 2018 Turing Award, known as the “Nobel Prize of computing,” are sharing the $1 million award for their pioneering work with artificial neural networks — but that’s not all they share.
Throughout their careers, the researchers’ career paths and spheres of influence in the field of artificial intelligence have crossed repeatedly.
Yann LeCun, vice president and chief AI scientist at Facebook, conducted postdoctoral research under the supervision of Geoffrey Hinton, who is now a vice president and engineering fellow at Google. LeCun also worked at Bell Labs in the early 1990s with Yoshua Bengio, who is now a professor at the University of Montreal, scientific director of Quebec’s Mila AI institute, and an adviser for Microsoft’s AI initiative.
All three also participate in the Learning in Machines and Brains program sponsored by CIFAR, previously known as the Canadian Institute for Advanced Research.
In Wednesday’s award announcement, ACM credited the trio with rekindling the AI community’s interest in deep neural networks — thus laying the groundwork for today’s rapid advances in machine learning.
“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 Pancake, a professor emeritus of computer science at Oregon State University. “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.”
And you don’t need to work in a lab to feel their impact.
“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,” Pancake said.
The current approach to machine learning, championed by Hinton starting in the early 1980s, shies away from telling a computer explicitly how to solve a given task, such as object classification. Instead, the software uses an algorithm to analyze the patterns in a data set, and then apply that algorithm to classify new data. Through repeated rounds of learning, the algorithm becomes increasingly accurate.
Hinton, LeCun and Bengio focused on developing neural networks to facilitate that learning. Such networks are composed of relatively simple software elements that are interconnected in ways inspired by the connections between neurons in the human brain.