Interview with Josh Tobin, Research Scientist at OpenAI

Josh was interviewed by George Lawton from TechTarget.

• Give me an overview of your work at OpenAI
• How did you begin working in AI and more specifically robotics?
• What motivates you to keep working in the industry?
• How can social robots help improve society?
• What problems are you trying to solve with AI, and what are the main challenges you’re currently facing in your work?
• How do you see AI changing society in the coming years?
• Could you unpack the notion of improving robot performance through better simulation?
• What are the places where simulation breaks down or is limited compared to real world experience?
• How can scientists improve the kinds of feedback loops to allow the creation of more useful and accurate simulations for a particular task?
• What lessons might humans take from AI research around building better visualizations and feedback loops for learning tasks like improving a golf swing?
• What industries do you think will benefit from your current work, and where are you most excited to see the impact?
• Would you advise a career in AI, and what are the key skills that you think are needed for such roles?
• What’s next for you?
• Where can we find you? Do you have Twitter, or should we keep our eye out for any new work or publications?

Josh Tobin, Research Scientist at OpenAI

Josh Tobin is a Research Scientist at OpenAI and a PhD student in Computer Science at UC Berkeley working with Professor Pieter Abbeel. Josh's research focuses on applying deep learning to problems in robotic perception and control, with a particular concentration on deep reinforcement learning, domain adaptation, and generative models. Prior to Berkeley and OpenAI, Josh was a consultant at McKinsey & Co. in New York. Josh has a BA in Mathematics from Columbia University.

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