Learning from Multi-Agent, Emergent Behaviors in a Simulated Environment
A revolution in reinforcement learning is happening, one that helps companies harness the more diverse, complex, virtual simulations available to accelerate the pace of innovation. Join this session to learn about particular environments already created that have yielded surprising advances in AI agents, and to better understand how emergent behaviors and open-endedness in multi-agent systems can lead to the most optimal designs and real-world practices.
Dr. Danny Lange is Senior Vice President of Artificial Intelligence and Machine Learning at Unity. As head of machine learning at Unity, Lange leads the company’s innovation around AI (Artificial Intelligence) and Machine Learning, focusing on bringing AI to simulation and gaming.
Prior to joining Unity, Lange was the head of machine learning at Uber, where he led efforts to build the world’s most versatile Machine Learning platform to support the company’s hyper-growth. Lange also served as General Manager of Amazon Machine Learning -- an AWS product that offers Machine Learning as a Cloud Service. Before that, he was Principal Development Manager at Microsoft where he led a product team focused on large-scale Machine Learning for Big Data.
Lange spent 8 years on Speech Recognition Systems, first as CTO of General Magic, Inc., then through his work 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 holds MS and Ph.D. degrees in Computer Science from the Technical University of Denmark. He is a member of the Association for Computer Machinery (ACM) and IEEE Computer Society, and has several patents to his credit.