Bridging the gap between the latest technological research advancements and real world applications in business and society

Speakers

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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Timnit Gebru, Research Scientist in the Ethical AI Team at Google, discusses the effects of bias in artificial intelligence. Timnit shares with Bloomberg that "we can try to mitigate bias and we can try to mitigate the effects of bias".

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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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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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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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Improving neural networks not through teaching, but through evolution—is revealing its potential. Five new papers from Uber in San Francisco, California, demonstrate the power of so-called neuroevolution to play video games, solve mazes, and even make a simulated robot walk.

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Stanford University Assistant Professor Dorsa Sadigh has ridden in self-driving cars. "These cars are OK driving in normal driving conditions on normal roads," Sadigh says. But "the moment you put them in situations they haven't seen, they don't really know how to deal with that."

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Find out more about Walmart's plans to create an Intelligence Retail Lab for developing artificial intelligence technology.

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Find out more on how AI4ALL is educating the next generation of AI technologists, thinkers, and leaders

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Computer scientists at the University of California San Diego have released a new version of a software system that processes images from the world's coral reefs anywhere between 10 to 100 times faster than processing the data by hand.

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Sridhar is currently Director of the Data Science Lab at Adobe Research. Adobe's Digital Experience Cloud processes trillions of consumer transactions on behalf of thousands of corporations, involving hundreds of petabytes of data. Understanding how to design machine learning algorithms that operate at such massive web scales is an exciting challenge that has drawn Sridhar to the Bay Area.

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Find out more about Mark Crowley's work on using machine learning to improve forest wildfire management

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Rushin Shah left his post as a senior machine learning manager on Apple’s virtual assistant to join Facebook’s Applied Machine Learning team, where he’ll be working on natural language and dialog understanding.

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This article takes you behind the scenes on how Dropbox built a state-of-the-art Optical Character Recognition (OCR) pipeline for their mobile document scanner.

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Hear from Catherine Lu, Principal at Spike Ventures, on the 3 major categories of AI companies. Catherine breaks down the three major categories as data science consulting firms, AI platform companies, and vertical AI companies. Read on to find out more.

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Vinod is a data scientist working with the Content Strategy and Enterprise teams where his work has recently focused on developing ways to measure the learning outcomes from taking Coursera classes, especially in the context of company sponsored training.

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A number of startups are now thinking about ways to minimize the amount of training needed for AI. Samsung Next is pushing this trend forward with the launch of a venture team called Q Fund. The team appears to be looking for companies that focus less on specific applications of AI (say, farming or algorithmic trading) and more on developing new ways of making decisions with computers.

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NASA's Robonaut project is designed to develop a humanoid robot that could one day take over menial or dangerous tasks in space. Julia Badger mentions: "The original thought and what we hold to now is that we want to create a robot that's able to do real work but with humans, in the vicinity of humans and being safe with humans"

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Northern Illinois University’s David Gunkel, in his recent book, asks provocative questions about the accountability and social standing of robots in the future.

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Karol's research interests lie in active state estimation, control generation and machine learning for robotics. Karol investigates interactive perception, by which robots use their manipulation capabilities to gain the most useful perceptual information to model the world and inform intelligent decision making.

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Aira was listed as one of TIME's Best Inventions in 2018. Read more here to discover how Aira uses AI to empower the blind community

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Modern bots try to trick users into interacting with them as if they’re human in other ways: by bantering and using humor, speaking (or writing) conversationally, and learning to parse free-form questions and answers. “This creates a perception that if you say anything to this bot, it should respond to it,” said Nikhil Mane, Autodesk.

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Conservation Metrics, a recipient of Microsoft’s AI for Earth grant program, is using algorithms to analyze a corpus from Cornell University Lab of Ornithology’s Elephant Listening Project, which collects data from acoustic sensors embedded throughout Nouabalé-Ndoki National Park and adjacent logging areas in the Republic of Congo.

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Chris Fregly is Founder at PipelineAI, a Real-Time Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production with Kubernetes and GPUs."

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Yuandong is a Research Scientist in Facebook AI Research (FAIR). Before joining Facebook, Yuandong was a Researcher/Software Engineer in Google X, Self-driving Car team in 2013-2014.

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SignalFire, a San Francisco, CA-based venture capital firm, is raising two new funds: a seed/small A fund and a later stage fund

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Vancouver-based Intuitive AI creates a smarter way to sort garbage. Read on to find out more about their technology.

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Eshika founded TakeKnowledGe, a 501(c)3 nonprofit focused on inspiring middle school students, girls in particular, to pursue STEM fields through fun activities like FIRST Lego League Robotics, app

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Ria attended the Stanford AI4ALL program in her freshman year of high school, where she coded a machine learning algorithm that classifies cancerous genes with 96% accuracy. Find out more about Stanford's AI Summer Program for young women here.

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Thomas Simonini dives deep into topics such as deep reinforcement learning and curiosity-driven learning through a series of free articles and video tutorials. Find out more here

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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 900+ 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
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Join the discussion

  • 100 speakers
  • 900 leading technologists & innovators
  • 10 Stages
  • 12 + hours of networking
  • Access to all the filmed presentations
  • Discover technology shaping the future
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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. View a PDF version of the schedule HERE.

Topics we cover

Neural Networks
Neural Networks
Machine Learning
Machine Learning
Deep Learning Algorithms
Deep Learning Algorithms
NLP
NLP
Computer Vision
Computer Vision
Speech Recognition
Speech Recognition
Image Retrieval
Image Retrieval
Autonomous Vehicles
Autonomous Vehicles

Confirmed Attendees Include

IBM
samsung
Google
BBC
dell
facebook
Uber
Target
University of Cambridge
Bank of America Merrill Lynch
Montreal AI Ethics Institute,
Adobe
PwC
Apple
Microsoft
Fujitsu
Pixar
Netflix
Toyota
Walmart
DeepMind
Stanford University
NVIDIA
salesforce
EY
Nike
Hitachi
Bank of America
Carnegie Mellon University
Chick-fil-A
Accenture
Jet.com
Morgan Stanley
Air BnB
Go Daddy
Mastercard

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