Traditionally, determining the most efficient designs and practices—whether for determining how store merchandise should be arranged or where people and machines should be laid out in a factory floor—has required vast amounts of data and human assessment. These efficient designs can be the difference between a thriving company and a struggling one. Recent advancements in multiagent reinforcement learning within virtual environments, such as DeepMind’s Capture the Flag or Open AI’s Learning to Compete and Cooperate, have led to a novel approach for tackling efficient design and practices.
Danny Lange explains 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 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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