Podcast: On Learning-Aware Mechanism Design with Michael I. Jordan

Learn about cutting-edge developments in AI and data science from the experts who know them best on ODSC’s Ai X Podcast. Each week, we release an interview with a leading expert, core contributor, experienced practitioner, or acclaimed instructor who is helping to shape the future of the AI industry through their work or research.

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

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

In this episode, Michael 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 his 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 a 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:

https://www.linkedin.com/in/michael-jordan-767032125/

https://www2.eecs.berkeley.edu/Faculty/Homepages/jordan.html

https://scholar.google.com/citations?user=yxUduqMAAAAJ&hl=en

Start listening now to get the full impact of Michael’s extensive knowledge of and expertise in learning-aware mechanism design and don’t forget to subscribe to ODSC’s Ai X Podcast to ensure you never miss an episode. Finally, like what you hear? Leave a review or share it with a friend! You can listen on Spotify, Apple, and SoundCloud.To take an even deeper dive into AI topics and tools, and their effects on society at large, join us at one of our upcoming conferences, ODSC Europe (September 5–6, Hybrid, or ODSC West (October 29–31, Hybrid).

Originally posted on OpenDataScience.com

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