The advancements of reinforcement learning to achieve the goal of human-level intelligence

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Dawn Song, a professor in EECS and a leader in the cybersecurity field, is taking an entirely new approach to enable organizations to follow tight data security and privacy policies while enabling flexible data analysis, as well as machine learning for analysts.

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Jacob Andreas joins the Department of Electrical Engineering and Computer Science as an assistant professor in July 2019. His work is focused on using language as a scaffold for more efficient learning and as a probe for understanding model behavior.

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Find out more about Marc Bellemere's work at Google Brain and his previous work at DeepMind here...

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Google Researchers, including Ofir Nachum, propose a new framework to model how bias can arise in a dataset, on the assumption that there exists an unbiased ground truth.

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Behavioral psychologists have long used mazes to study memory and learning; their subjects, mostly rats and mice. Now researchers are beginning to use the same approach to test an entirely new kind of subject—the latest breed of artificial intelligence machine. Hear more from Junhyuk Oh, University of Michigan.

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Researchers Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn and Sergey Levine introduced an approach called Unsupervised Meta Learning which allows an AI agent to get a distribution of tasks. The agent can go on to do meta learning over these tasks.

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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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Why Attend

Speakers

Extraordinary Speakers

Discover advances in reinforcement learning algorithms & methods from the world's leading innovators, from research pioneers to industry leaders, deploying applications of reinforcement learning in the real world, e.g. robotics, recommendations & manufacturing.

Tech

Discover Emerging Trends

The summit will showcase academic advancements of RL as well as their impact in business & industry. Where do the challenges still lie in research? How can companies leverage progress? How can we apply the notion of learning from environment to process inputs & choose complex actions?

Network

Expand Your Network

A unique opportunity to interact with engineers, research scientists, professors, and startups leading the deep reinforcement learning progression. Learn from & connect with 400+ industry innovators & researchers sharing insights, trends, recent discoveries & best practices.

Who

Who Should Attend

  • Research Scientists
  • Engineers
    • RL Engineers
  • CTOs
  • Founders
  • ML Engineers
  • Software Engineers
  • Research Engineers
  • Data Scientists
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Join the discussion

  • 45 speakers
  • 400 leading technologists & innovators
  • Technical Talks & Practical Applications
  • Interactive workshops
  • 12 + hours of networking
  • Access to all the filmed presentations
  • Discover technology shaping the future
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Topics we cover

Neural Networks
Neural Networks
Reinforcement Learning
Reinforcement Learning
Swarm robotics
Swarm robotics
Imitation Learning
Imitation Learning
Deep Learning
Deep Learning
Meta-Learning
Meta-Learning
End-to-End Learning
End-to-End Learning
Multi-Task Learning
Multi-Task Learning

Previous Attendees include:

Medtronic
Plarium Games
Honeywell
Amazon
Google
VISA
Cisco
Mitsubishi
Ancestry
Slack
samsung
lyft
Uber
UC Berkeley
Soundcloud
Intel
NVIDIA
Microsoft
facebook
DeepMind
MIT
Kindred AI
Stanford University
QuantumBlack
openai

Previous Sponsors include:

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