Advancing Our Understanding of Deep Reinforcement Learning with Community-driven Insights
Who is this presentation for?Business leaders interested in understanding the real-world potential of training AI in simulated environments. AI industry professionals that are interested in hosting their own AI challenges.
Simulated environments have been essential to advancing the field of artificial intelligence, providing vast amounts of synthetic data that test novel approaches safely and efficiently. This has most often taken the form of games, ranging from simple board games to modern, multiplayer strategy games.
While these games have served as a good starting point, to push the state-of-the-art in AI research, Unity saw an opportunity to take this to the next level. In order to do so, they introduced the Obstacle Tower, a high-visual-fidelity, 3D, 3rd person, procedurally-generated game environment purpose-built to test a deep reinforcement learning-trained agent’s vision, control, planning, and generalization abilities. Over the past year, we invited researchers and developers to attempt to solve the tower, with the intention of sharing those insights with the broader community.
Attendees will learn:
*What was learned from hosting the challenge, and how other companies can engage the broader community in the same way to advance AI research.
*How participants fared as they attempted to solve the tower and what was gleaned from those results.
*What’s next for Obstacle Tower, and how projects like this will continue to evolve to push advances in deep reinforcement learning.
*How observing emergent behaviors of multiple AI agents in a simulated virtual environment can lead to the most optimal designs and real-world practices.
Prerequisite knowledgeIt is assumed that attendees have basic knowledge of the concepts of machine learning and AI.
What you'll learnHow simulated environments are helping to advance the state-of-the-art in AI research, and the practical lessons of hosting this type of AI contest.
Danny Lange is vice president of AI and machine learning at Unity Technologies, where he leads multiple initiatives around applied artificial intelligence. Previously, Danny was head of machine learning at Uber, where he led the efforts to build a highly scalable machine learning platform to support all parts of Uber’s business, from the Uber app to self-driving cars; general manager of Amazon Machine Learning, where he provided internal teams with access to machine intelligence and launched an AWS product that offers machine learning as a cloud service to the public; principal development manager at Microsoft, where he led a product team focused on large-scale machine learning for big data; CTO of General Magic, Inc.; and founder of his own company, Vocomo Software, where he worked on General Motor’s OnStar Virtual Advisor, one of the largest deployments of an intelligent personal assistant until Siri. Danny started his career as a computer scientist at IBM Research. He is a member of ACM and IEEE Computer Society and has numerous patents to his credit. Danny holds an MS and PhD in computer science from the Technical University of Denmark.
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