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

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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 connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts or support it on Patreon.

This episode is presented by Cash App. Download it (App Store, Google Play), use code "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

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 <a href="https://lexfridman.com/ai">https://lexfridman.com/ai</a> or connect with @lexfridman on <a href="https://twitter.com/lexfridman">Twitter</a>, <a href="https://www.linkedin.com/in/lexfridman/">LinkedIn</a>, <a href="https://www.facebook.com/lexfridman">Facebook</a>, <a href="https://medium.com/@lexfridman">Medium</a>, or <a href="https://www.youtube.com/lexfridman">YouTube</a> where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on <a href="https://podcasts.apple.com/us/podcast/artificial-intelligence/id1434243584">Apple Podcasts</a> or support it on <a href="https://www.patreon.com/lexfridman">Patreon</a>.
This episode is presented by Cash App. Download it (<a href="https://apps.apple.com/us/app/cash-app/id711923939">App Store</a>, <a href="https://play.google.com/store/apps/details?id=com.squareup.cash&hl=en_US">Google Play</a>), use code “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

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