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

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Speakers

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Ian Goodfellow

Staff Research Scientist

Google Brain

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Brendan Frey

Co-Founder & CEO

Deep Genomics

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Shivon Zilis

Partner

Bloomberg

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Andrej Karpathy

Director of Artificial Intelligence

Tesla

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Andrew Tulloch

Research Engineer

Facebook

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Ofir Nachum

Brain Resident

Google

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Stefano Ermon

Assistant Professor

Stanford University

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Toru Nishikawa

CEO

Preferred Networks

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Avidan Akerib

VP of the Associative Computing

GSI Technology

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Durk Kingma

Research Scientist

OpenAI

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Eli David

CTO

Deep Instinct

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Chelsea Finn

PhD Student

UC Berkeley

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Roland Memisevic

Chief Scientist

Twenty Billion Neurons

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Stacey Svetlichnaya

Software Development Engineer

Flickr

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Sergey Levine

Assistant Professor

UC Berkeley

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Chris Moody

Data Scientist

Stitch Fix

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Brad Folkens

Co-Founder & CTO

CloudSight

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Alex Dalyac

Co-Founder & CEO

Tractable

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Danny Lange

VP of AI and Machine Learning

Unity Technologies

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Augustin Marty

Co-Founder & CEO

Deepomatic

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Andres Rodriguez

Senior Technical Lead for Deep Learning

Intel

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Ilya Sutskever

Research Director

OpenAI

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Alexei Efros

Associate Professor

UC Berkeley

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Tony Jebara

Director of Machine Learning Research

Netflix

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Modar Alaoui

CEO & Founder

Eyeris

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Shubho Sengupta

AI Research

Facebook AI Research (FAIR)

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Inmar Givoni

Director of Machine Learning

Kindred

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Judy Hoffman

Postdoc Researcher

Stanford Computer Vision Group

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Will Jack

CEO

Remedy

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Bryan Catanzaro

VP of Applied Deep Learning Research

NVIDIA

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Rumman Chowdhury

Senior Manager

Accenture

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Nathan Benaich

Investor

Playfair Capital

More Speakers Coming Soon

Get to know the speakers

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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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"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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Despite the noisy hype, which sometimes distracts, machine intelligence is already being used in several valuable ways. Machine intelligence already helps us get the important business information we need more quickly, monitors critical systems, feeds our population more efficiently, reduces the cost of health care, detects disease earlier, and so on.

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Karpathy and colleagues have created artificial intelligence software capable of recognizing and describing the content of photographs and videos with far greater accuracy than ever before, sometimes even mimicking human levels of understanding.

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Ofir Nachum will be sharing his latest work from the recently published paper: 'Improving Policy Gradient by Exploring Under-appreciated Rewards' - presents a novel form of policy gradient for model-free reinforcement learning (RL) with improved exploration properties.

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Preferred Networks took part in the 2016 Amazon Picking Challenge and used Deep Learning algorithms & Chainer, a Python-based Open Source Deep Learning framework, on input data obtained from image and 3D location sensors for object detection and localization.

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They are a dream of researchers but perhaps a nightmare for highly skilled computer programmers: artificially intelligent machines that can build other artificially intelligent machines.

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Having parsed millions of images, Flickr’s deep learning algorithm has learned to recognize 1,000 different objects in images. It does this by passing them through a series of layers, each of which transforms the original image and performs progressively more and more complex computations on it.

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Sergey Levine, Assistant Professor at UC Berkeley has been listed as one of the 35 Innovators Under 35 by MIT Technology Review for his work creating a robot that supervises its own learning. “It’s reverse-engineering its own behavior,” Levine said.

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A Brief Overview of Deep Learning from Google's Ilya Sutskever. What’s so special about deep learning? Why does it work now, and how does it differ from neural networks of old? How will it impact your industry? Hear more from Ilya at the summit.

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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.

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Facebook recently published a paper detailing its personal approach to speeding up training for visual recognition models. The company says it has managed to reduce the training time of a ResNet-50 deep learning model on ImageNet from 29 hours to one.

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A new technology developed at Berkeley from a team including Alexei Efros, claims to be able to create photorealistic images from just a few simple digital brush strokes. It uses machine learning to edit images based on an understanding of what looks real and what doesn't.

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Eyeris is a world pioneer and leader of deep learning-based facial emotion recognition software. The company's flagship product EmoVu provides the most comprehensive suite of face analytics and is used in today's commercial applications such as automotive, robotics, and video analytics.

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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

  • 40 speakers
  • 450 leading technologists & innovators
  • Group brainstorming sessions
  • Interactive workshops
  • 7 + hours of networking
  • Access to all the filmed presentations
  • Discover technology shaping the future
Document

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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
Machine Learning
Machine Learning
Deep Learning Algorithms
Deep Learning Algorithms
Voice Recognition
Voice Recognition
NLP
NLP
Computer Vision
Computer Vision
Pattern Recognition
Pattern Recognition
Speech Recognition
Speech Recognition
Predictive Intelligence
Predictive Intelligence
AI Assistants
AI Assistants
Image Retrieval
Image Retrieval
Autonomous Vehicles
Autonomous Vehicles

Confirmed Attendees Include

Apple
hitachi
Fujitsu
alpha sense
PwC
capital one
Microsoft
Intel
Cogniance
openai
kholsa ventures
uber
tivo
arterys
glidewell
dell
NVIDIA
Autodesk1
American Express
Ricoh

Venue

A block from Market Street in the Downtown neighborhood of SoMa, the Park Central is a contemporary hotel, a 2-minute walk from Montgomery Street BART/Muni Metro station, and a 9-minute walk from Union Square.

View more information on hotels & things to do in San Francisco on our Pinterest page.

Park Central Hotel, 50 3rd St, San Francisco, CA 94103

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