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 and AI industry professionals interested in hosting AI challenges
Simulated environments have been essential to advancing the field of artificial intelligence, providing vast amounts of synthetic data that tests novel approaches safely and efficiently. This has most often taken the form of games, ranging from simple board games to modern multiplayer strategy games.
These games served as a good starting point, but Danny Lange reveals an opportunity to push the state of the art in AI research to the next level. United introduced the Obstacle Tower, a high-visual-fidelity, 3-D, third-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, Unity invited researchers and developers to try to solve the tower with the intention of sharing those insights with the broader community.
- A basic knowledge of machine learning and AI
What you'll learn
- See how you can use what Unity learned from hosting the challenges to engage the broader community to advance AI research
- Find out how participants fared as they attempted to solve the tower, what that taught Unity, and what's next for Obstacle Tower as it continues pushing advances in deep reinforcement learning
- Learn how observing emergent behaviors of multiple AI agents in a simulated virtual environment can lead to the most optimal designs and real-world practices
Danny Lange is the vice president of AI and machine learning at Unity, where he leads multiple initiatives around applied artificial intelligence. Previously, Danny led the efforts to build a highly scalable machine learning platform to support all parts of Uber’s business from the app to self-driving cars as the head of machine learning, provided internal teams with access to machine intelligence and launched an AWS product that offers machine learning as a cloud service to the public as the general manager of Amazon Machine Learning, led a product team focused on large-scale machine learning for big data as principal development manager at Microsoft, was CTO of General Magic, Inc., worked on General Motor’s OnStar Virtual Advisor—one of the largest deployments of an intelligent personal assistant until Siri—as the founder of his own company Vocomo Software, and was a computer scientist at IBM Research. He’s 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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