Tim Scarfe

Host of Machine Learning Street Talk (MLST), an in-depth technical AI podcast. Scarfe holds a Ph.D. in machine learning and previously worked as a Principal Engineer at Microsoft and Chief Data Scientist at bp.

AI Needs Inference, Incentives, and Institutions Around the Model

Michael I. Jordan, the Berkeley statistician and computer scientist, argues that modern machine learning is being misdescribed when it is framed as a race toward AGI or disembodied intelligence. In this conversation, Jordan says the more important problem is designing collective economic systems around prediction models: incentives, markets, uncertainty, regulation, privacy, and institutions. His case is that prediction alone is not inference, and that useful AI will depend less on anthropomorphic claims about understanding than on system design that lets humans act, coordinate, and reduce uncertainty.

Machine Learning Street TalkMay 20, 202625 min read