Traditionally, determining the most efficient designs and practices requires vast amounts of data and human assessment, whether it is how store merchandise should be arranged or where people and machines should be laid out in a factory floor. These efficient designs can be the difference between a thriving company or a struggling one. Even then, most companies are still challenged by this.
Recent advancements in multi-agent reinforcement learning within virtual environments, such as DeepMind’s Capture the Flag or Open AI’s Learning to Compete and Cooperate, has led to a novel approach for tackling efficient design and practices. In this session, we’ll discuss how observing emergent behaviors of multiple AI agents in a simulated virtual environment can lead to the most optimal designs and real-world practices, all without introducing human bias or the need for vast amounts of data.
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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