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 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.