Judea Pearl: Causal Reasoning, Counterfactuals, Bayesian Networks, and the Path to AGI

Judea Pearl is a professor at UCLA and a winner of the Turing
Award, that's generally recognized as the Nobel Prize of computing.
He is one of the seminal figures in the field of artificial
intelligence, computer science, and statistics. He has developed
and championed probabilistic approaches to AI, including Bayesian
Networks and profound ideas in causality in general. These ideas
are important not just for AI, but to our understanding and
practice of science. But in the field of AI, the idea of causality,
cause and effect, to many, lies at the core of what is currently
missing and what must be developed in order to build truly
intelligent systems. For this reason, and many others, his work is
worth returning to often. This conversation is part of the
Artificial Intelligence podcast. If you would like to get more
information about this podcast go to https://lexfridman.com/ai or
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"LexPodcast".  Here's the outline of the episode. On some
podcast players you should be able to click the timestamp to jump
to that time. 00:00 - Introduction 03:18 - Descartes and analytic
geometry 06:25 - Good way to teach math 07:10 - From math to
engineering 09:14 - Does God play dice? 10:47 - Free will 11:59 -
Probability 22:21 - Machine learning 23:13 - Causal Networks 27:48
- Intelligent systems that reason with causation 29:29 - Do(x)
operator 36:57 - Counterfactuals 44:12 - Reasoning by Metaphor
51:15 - Machine learning and causal reasoning 53:28 - Temporal
aspect of causation 56:21 - Machine learning (continued) 59:15 -
Human-level artificial intelligence 1:04:08 - Consciousness 1:04:31
- Concerns about AGI 1:09:53 - Religion and robotics 1:12:07 -
Daniel Pearl 1:19:09 - Advice for students 1:21:00 - Legacy