Blending Machine Learning and Microeconomics

The speaker discusses the importance of incorporating economic principles into machine learning systems to build large-scale, economically sound structures that benefit society.

• Rey is a platform that combines functional programming and object-oriented programming to create a model of distributed asynchronous computing.
• The speaker believes that AI and machine learning have been missing the boat by neglecting the importance of market intelligence.
• Building multi-way markets, exploring to learn preferences, and uncertainty quantification are some of the open academic problems in this field.
• Fairness, privacy, and social good are important considerations that have an economic side to them.
• The speaker provides an anecdote about UnitedMasters, a company that created a two-way market for musicians to stream their music to the NBA and get paid for it.
• The overall goal is to build large-scale systems that are healthy, happy, and economically efficient.

The speaker provides an anecdote about UnitedMasters, a company that created a two-way market for musicians to stream their music to the NBA and get paid for it. This market allows producers, consumers, and brands to be connected in a healthy, economically sound structure. The market is based on machine learning, which analyzes data and makes predictions to structure the market effectively. The result is that 16-year-old musicians are now receiving a salary based on the market created by UnitedMasters, rather than just famous musicians receiving royalties.

While the current era of machine learning is focused on recognizing patterns, the future will be all about making decisions, Michael I. Jordan, a Distinguished Professor at UC Berkeley, explains in this keynote. However, it's not easy to get there. Real-world decisions have consequences, are powered by context, are often interlaced with others’ decisions, have multiple levels of related decisions, and must be explainable. He talks about how today’s recommendation engines, for instance, can wind up sending hundreds of people to the same restaurant or movie, or along the same traffic route. Professor Jordan covers how the future of machine learning relies on economic principles. He discusses how decision-making is different from recommendations, how market forces factor into decision making, and will explain a framework for building a decision-making system. Professor Jordan’s research interests include machine learning, optimization, control theory, and computational biology. He is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences. He has received the Ulf Grenander Prize from the American Mathematical Society, the IEEE John von Neumann Medal, the IJCAI Research Excellence Award, and the ACM/AAAI Allen Newell Award.