Economic history and research can provide useful tools for thinking about the future of AI and the challenges and risks along the way.

Tom Bilston and Tony Murray of the Richmond Fed's bank supervision team discuss how banks use machine learning, chat bots and other forms of AI as well as what these technological innovations mean for bank examiners, both in terms of the risks they monitor and how they do their job.

Financial institutions have approached AI with cautious optimism, developing and implementing AI-based applications for things like customer service chatbots, detecting fraud, and evaluating creditworthiness. Bank supervisors are learning how to adapt.

MIT's Daron Acemoglu, the author of Why Nations Fail and Power and Progress, on Henry Ford, making AI worker-friendly, and how democracy improves economic growth.

Among the topics discussed were income growth volatility, AI's impact on productivity and how housing price changes affect young businesses.

University of Toronto economist on managing pandemics, allocating vaccines, and low-cost prediction with artificial intelligence.

Microsoft's former chief economist on conducting research at tech companies, entrepreneurship within large firms, and the effects of artificial intelligence on markets

Economists interested in AI have been puzzled by what some call a "productivity paradox," the gap between optimistic expectations about the economic effects of AI and the effects that appear in the data.

Whether it's a custom music playlist crafted for millions of users or interpreting X-rays and other scans, machine learning is having a substantial impact on a wide range of industries.