The smart artificial intelligence revolution. Explore software that can recognize patterns in digital representations of sounds, images, & data.

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Speakers

The full line up will be announced closer to the event. Previous speakers include:

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Yoshua Bengio

Full Professor

Université de Montréal

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Ian Goodfellow

Staff Research Scientist

Google Brain

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Sanja Fidler

Assistant Professor

University of Toronto

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Ira Kemelmacher-Shlizerman

Assistant Professor/Researcher

Allen School of Computer Science/Facebook

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Aaron Courville

Assistant Professor

University of Montreal

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Alex Acero

Senior Director of Siri

Apple

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Yann LeCun

Director of AI Research

Facebook

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Geoffrey Hinton

Professor

University of Toronto

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Brendan Frey

Co-Founder & CEO

Deep Genomics

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Raquel Urtasun

Head

Uber ATG/University of Toronto

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Hugo Larochelle

Research Scientist

Google Brain

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“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.”

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Ian Goodfellow is a Senior Research Scientist on the Google Brain team. He studies new methods for improving neural networks. Recent publications include: Adversarial Autoencoders; Net2Net: Accelerating Learning via Knowledge Transfer; and Explaining and Harnessing Adversarial Examples.

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Researchers have developed a machine learning algorithm that can turn audio clips into realistic, lip-synced videos. A video shows former US president Barack Obama apparently speaking on a number of subjects including terrorism, though the clips were artificially generated using existing video addresses.

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Yann LeCun once built an AI chip called ANNA. But he was 25 years ahead of his time. He and several other researchers designed this chip to run deep neural networks—complex mathematical systems that can learn tasks on their own by analyzing vast amounts of data—but ANNA never reached the mass market.

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"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.

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"To understand deep learning in the context of genetic disease, you need to understand shallow learning first. Shallow learning relates mutations to diseases by looking for mutations that commonly occur in patients with a disease. It’s a commonly used method", Brendan Frey, Deep Genomics.

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There are some estimates that five percent of all AI talent within the private sector are currently employed by Google. Perhaps no one among that rich talent pool has as deep a set of perspectives as Geoff Hinton. He has been involved in AI research since the early 1970s, which means he got involved before the field was really defined.

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Computer scientists from the University of Toronto have created a software program that helps users avoid fashion faux pas. Raquel Urtasun and Sanja Fidler, along with colleagues in Spain, designed an algorithm that analyzes a person’s photograph to determine whether the wearer’s outfit is stylish. It also suggests ways to improve the ensemble and the subject’s overall appeal.

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Why Attend

Speakers

Extraordinary Speakers

Discover advances in deep learning algorithms and methods from the world's leading innovators. Learn from industry experts in speech & pattern recognition, neural networks, image analysis and NLP. Explore how deep learning will impact healthcare, manufacturing, search & transportation.

Tech

Discover Emerging Trends

The summit will showcase the opportunities of advancing trends in deep learning and their impact and successful applications in business. Where do the challenges still lie in research and application? Learn the latest technological advancements & industry trends from a global line-up of experts.

Network

Expand Your Network

A unique opportunity to interact with industry leaders, influential technologists, data scientists & founders leading the deep learning revolution. Learn from & connect with 450+ industry innovators sharing best practices to advance the smart artificial intelligence revolution.

Who

Who Should Attend

  • Data Scientists
  • Data Engineers
  • Machine Learning Scientists
  • CTOs
  • Founders
  • Director of Engineering
  • CEOs
Speechbubbles

Join the discussion

  • 60 speakers
  • 600 leading technologists & innovators
  • Access to sessions in Track 1
  • Group brainstorming sessions
  • Interactive workshops
  • 7 + hours of networking
  • Access to all the filmed presentations
  • Discover technology shaping the future
Document

Downloads

View the summit brochure and all the information you need to convince your boss that attending the summit will help future-proof your business.

Topics we cover

Neural Networks
Neural Networks
NLP
NLP
Computer Vision
Computer Vision
Pattern Recognition
Pattern Recognition
Speech Recognition
Speech Recognition
AI Assistants
AI Assistants
Image Retrieval
Image Retrieval
Autonomous Vehicles
Autonomous Vehicles

Previous Attendees Include

Honda
CBC Radio Canada
Apple
Fujitsu
alpha sense
PwC
capital one
Microsoft
Intel
openai
uber
dell

Register

For enquiries about the event or to pay via invoice please contact the summit creator Ka Lai Brightley-hodges via kalai@re-work.co

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