Université de Montréal
Senior Editor, Business Reports
MIT Technology Review
Director of the Center for Data Science
University of Massachusetts Amherst
Fast Forward Labs
CEO & Founder
Senior Principal Research Scientist
Mitsubishi Electric Research Labs
VP of the Associative Computing
“Today’s models can be trained on huge quantities of data, but that’s not enough,” says Bengio, who together with LeCun and Google’s Geoffrey Hinton is one of the original musketeers of deep learning. “We need to discover learning algorithms that can take better advantage of all this unlabeled data that’s sitting out there.”Read More
“On our chip we bring the data as close as possible to the processing units, and move the data as little as possible,” Vivienne Sze. When run on an ordinary GPU, neural networks fetch the same image data multiple times. The MIT chip has 168 processing engines, each with its own dedicated memory nearby.Read More
"A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data". In this paper Hugo Larochelle explores how to successfully apply and extend DocNADE to multimodal data, such as simultaneous image classification and annotation.Read More
Meet Partpic: After being licensed by a manufacturing company, PartPic obtains 360 degree photos and metadata of every part in the company’s inventory. A user can then can takes a photo of a specific part and PartPic’s software automatically identifies the name and model number of that part.Read More
Learning Physical Intuition of Block Towers by Example - hear from Adam Lerer, Research Engineer at Facebook AI Research on exploring the ability of deep feedforward models to learn intuitive physics.Read More
Conservation Metrics goal is to improve conservation by providing powerful new tools to monitor wildlife status, distribution, and population trends. They provide rigorous data for impact assessments, cost-effective tools for measuring ecological changes after restoration and management actions or development projects.Read More
“The broad goal is to become the emotion layer of the Internet,” says Affectiva co-founder Rana el Kaliouby, a former MIT postdoc who invented the technology. “We believe there’s an opportunity to sit between any human-to-computer, or human-to-human interaction point, capture data, and use it to enrich the user experience.”Read More
Tony Jebara and colleagues at Columbia University will work with oceanographers to understand what has caused an unusual plankton-like species to rapidly invade the Arabian Sea food chain, threatening fisheries that sustain more than 100 million people living at the sea's edge.Read More
Nervana Systems wants to make deep learning more accessible by developing custom hardware built to process all that data. Hear their latest developments from CEO Naveen Rao in a panel session at the summit.Read More
"The easiest place for Amazon to bring its expertise to bear is in the warehouses, because that's where Amazon really excels," Hawkins said. "If they can reduce costs, they can show that on the store shelves and move Whole Foods away from the Whole Paycheck image."Read More
Discover advances in deep learning & smart artificial intelligence from the world's leading innovators. Learn from industry experts & academics in speech & image recognition, neural networks & big data. Explore how deep learning will impact communications, manufacturing, healthcare & transportation.
The summit will showcase the opportunities of advancing trends in deep learning and their impact on business & society. Will smart artificial intelligence finally rival human intelligence? Learn the latest technological advancements & industry trends from a global line-up of experts.
A unique opportunity to interact with business leaders, influential technologists, data scientists & entrepreneurs leading the deep learning revolution. Learn from & connect with 200+ industry innovators sharing best practices to advance the smart artificial intelligence revolution.