This post has been republished via RSS; it originally appeared at: Channel 9.
Fielding AI solutions in the open world requires systems to grapple with incompleteness and uncertainty. This session will address several promising areas of research in open world AI, including enhancing robustness via leveraging algorithmic portfolios, learning from experiences in rich simulation environments, harnessing approaches to transfer learning, and learning and personalization from small training sets. In addition, this session will cover mechanisms for engaging people to identify and address uncertainties, failures, and blindspots in AI systems.
