What does it take for large language models (LLMs) to dispense trusted advice to their human users? Three key features: (1) domain-specific expertise; (2) the ability to tailor that expertise to a user’s unique situation; and (3) trustworthiness and adherence to the user’s moral and ethical standards. These challenges apply to virtually all industries
and endeavors in which LLMs can be applied, such as medicine, law, accounting, education,
psychotherapy, marketing, and corporate strategy. In this talk, Prof. Lo focuses on the specific context of financial advice, which serves as an ideal test bed both for determining the possible shortcomings of current LLMs and for exploring ways to overcome them.
AI, Business, Finance, Money "Andrew W. Lo is the Charles E. and Susan T. Harris Professor at the MIT Sloan School of Management, the director of MIT’s Laboratory for Financial Engineering, a principal investigator at MIT’s Computer Science and Artificial Intelligence Lab, and an external professor at the Santa Fe Institute. His current research focuses on systemic risk in the financial system; evolutionary approaches to investor behavior, bounded rationality, and financial regulation; and applying financial engineering to develop new funding models for biomedical innovation and fusion energy. Lo has published extensively in academic journals (see [ Ссылка ]) and his most recent book is The Adaptive Markets Hypothesis: An Evolutionary Approach to Understanding Financial System Dynamics. His awards include Batterymarch, Guggenheim, and Sloan Fellowships; the Paul A. Samuelson Award; the Eugene Fama Prize; the IAFE-SunGard Financial Engineer of the Year; the Global Association of Risk Professionals Risk Manager of the Year; one of TIME’s “100 most influential people in the world”; and awards for teaching excellence from both Wharton and MIT. He received a B.A. in economics from Yale University and an A.M. and Ph.D. in economics from Harvard University.
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"Andrew W. Lo is the Charles E. and Susan T. Harris Professor at the MIT Sloan School of Management, the director of MIT’s Laboratory for Financial Engineering, a principal investigator at MIT’s Computer Science and Artificial Intelligence Lab, and an external professor at the Santa Fe Institute. His current research focuses on systemic risk in the financial system; evolutionary approaches to investor behavior, bounded rationality, and financial regulation; and applying financial engineering to develop new funding models for biomedical innovation and fusion energy. Lo has published extensively in academic journals (see [ Ссылка ]) and his most recent book is The Adaptive Markets Hypothesis: An Evolutionary Approach to Understanding Financial System Dynamics. His awards include Batterymarch, Guggenheim, and Sloan Fellowships; the Paul A. Samuelson Award; the Eugene Fama Prize; the IAFE-SunGard Financial Engineer of the Year; the Global Association of Risk Professionals Risk Manager of the Year; one of TIME’s “100 most influential people in the world”; and awards for teaching excellence from both Wharton and MIT. He received a B.A. in economics from Yale University and an A.M. and Ph.D. in economics from Harvard University.
" This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at [ Ссылка ]
