#74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI

Michael I. Jordan is a professor at Berkeley, and one of the most
influential people in the history of machine learning,
statistics, and artificial intelligence. He has been cited over
170,000 times and has mentored many of the world-class
researchers defining the field of AI today, including Andrew Ng,
Zoubin Ghahramani, Ben Taskar, and Yoshua Bengio.


EPISODE LINKS:
(Blog post) Artificial Intelligence—The Revolution Hasn’t
Happened Yet


This conversation is part of the Artificial Intelligence
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Here’s the outline of the episode. On some podcast players you
should be able to click the timestamp to jump to that time.


OUTLINE:
00:00 – Introduction
03:02 – How far are we in development of AI?
08:25 – Neuralink and brain-computer interfaces
14:49 – The term “artificial intelligence”
19:00 – Does science progress by ideas or personalities?
19:55 – Disagreement with Yann LeCun
23:53 – Recommender systems and distributed decision-making at
scale
43:34 – Facebook, privacy, and trust
1:01:11 – Are human beings fundamentally good?
1:02:32 – Can a human life and society be modeled as an
optimization problem?
1:04:27 – Is the world deterministic?
1:04:59 – Role of optimization in multi-agent systems
1:09:52 – Optimization of neural networks
1:16:08 – Beautiful idea in optimization: Nesterov
acceleration
1:19:02 – What is statistics?
1:29:21 – What is intelligence?
1:37:01 – Advice for students
1:39:57 – Which language is more beautiful: English or French?