On Learning-Aware Mechanism Design with Michael I. Jordan, PhD

This is a previously recorded interview from early 2023 with one of computer science's most influential pioneers that we are rereleasing on our podcast platform for a wider audience.

In this episode, the incredibly accomplished Michael I. Jordan joins us for a discussion on learning-aware mechanism design.

Michael, currently a Distinguished Professor at the University of California, Berkeley, has made significant contributions to the field of AI throughout his extensive career. In 2016, he was named the "most influential computer scientist" worldwide in Science magazine.

Michael is a member of many distinguished associations including the American Association for the Advancement of Science. He is also the recipient of many awards, including the Ulf Grenander Prize from the American Mathematical Society (2021) and the IEEE John von Neumann Medal (2020).

This episode will delve into learning-aware mechanism design, a subfield of mechanism design, a branch of economics that studies the design of rules and procedures for decision-making in strategic settings with the goal of creating mechanisms that are more efficient, fair, and robust by incorporating insights from machine learning.
Topics:
-  Guest’s professional background and journey to current position
-  Guest’s definition and understanding of machine learning
-  The history of machine learning 
-  How your thinking has evolved since then due to advance in the field of ML and the economic impact of COVID on the behavior of individuals and companies
-  The importance of two-sided marketplace for agents 
-  Limitations of the recommendation system
-  How do we solve data equality problems in market driven decision algorithms
-  Ways machine learning can improve decision-making under uncertainty
-  Market dynamic of scarcity when it comes to the designing system  
-  The role of government regulation for AI 
-  Why federated learning is a necessity
-  The possible development of Generative AI applications into  two sided markets or ad revenue-based business model
-  The models that work best
-  Advice about the future and direction of AI

Show Notes:
More about Michael I. Jordan, PhD:
⁠www.linkedin.com/in/michael-jorda…duqMAAAAJ&hl=en⁠

This episode was sponsored by:  
Ai+ Training ⁠aiplus.training/⁠ 
Home to hundreds of hours of on-demand, self-paced AI training, ODSC interviews, free webinars, and certifications in in-demand skills like LLMs and Prompt Engineering
And created in partnership with ODSC ⁠odsc.com/⁠ 
The Leading AI Training Conference, featuring expert-led, hands-on workshops, training sessions, and talks on cutting-edge AI topics and tools, from data science and machine learning to generative AI to LLMOps

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