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Yann LeCun is among those bringing a new level of artificial intelligence to popular internet services from the likes of Facebook, Google, and Microsoft. As the head of AI research at Facebook, LeCun oversees the creation of vast "neural networks" that can recognize photos and respond to everyday human language. And similar work is driving speech recognition on Google's Android phones, instant language translation on Microsoft's Skype service, and so many other online tools that can "learn" over time. Using vast networks of computer processors, these systems approximate the networks of neurons inside the human brain, and in some ways, they can outperform humans themselves. This week in the scientific journal Nature, LeCun--also a professor of computer science at New York University--details the current state of this "deep learning" technology in a paper penned alongside the two other academics most responsible for this movement: University of Toronto professor Geoff Hinton, who's now at Google, and the University of Montreal's Yoshua Bengio.
The four founders, two of whom have resigned their professorships to devote their full attention to TwentyBN, met each other during studies at the University of Bielefeld in Germany. Each of the founders has over 15 years of experience in machine learning and the relatively young deep learning discipline. Prof. Dr. Roland Memisevic, Chief Scientist, received his doctorate in Toronto, studying with Geoffrey Hinton, one of the founding fathers of deep learning. Prior to co-founding Twenty Billion Neurons, Memisevic was a member of the faculty at the renowned Machine Learning Institute of the University of Montréal led by Yoshua Bengio. The institute counts Google, Facebook, and IBM amongst its most active donors.
NIPS 2018 (Montreal, Canada), or NeurIPS, as it is called now, is over, and I would like to take the opportunity to dissect one of the papers that received the Best Paper Award at this prestigious conference. The name of the paper is Neural Ordinary Differential Equations (arXiv link) and its authors are affiliated to the famous Vector Institute at the University of Toronto. In this post, I will try to explain some of the main ideas of this paper as well as discuss their potential implications for the future of the field of Deep Learning. Since the paper is quite advanced and touches on concepts such as Ordinary Differential Equations (ODE), Recurrent Neural Networks (RNN) or Normalizing Flows (NF), I suggest that you read up on these terms if you are not familiar with them, since I will not go into details on these. However, I will try to explain the ideas of the paper as intuitively as possible, so that you may get the main concepts without going too much into the technical details.
The research lab will be led by Maluuba's CTO, Kaheer Suleman, and will be staffed by 13 deep learning research scientists. Maluuba has also partnered with reinforcement learning expert Richard Sutton, a principal investigator from the Alberta Innovates Centre for Machine Learning and an Association for the Advancement of Artificial Intelligence Fellow. "Maluuba is working with leading experts and the world's premiere academic centre for deep learning to design systems that can represent knowledge and answer questions in natural language. The potential applications of this research are staggering." The company counts LG as one of its customers.