In this interview with AI expert Pedro Domingos, you’ll learn about self-driving cars, where knowledge comes from, and the 5 schools of machine learning.   Go Premium: Members get early access, ad-free episodes, hand-edited transcripts, searchable transcripts, member-only episodes, and more. Sign up at: https://fs.blog/membership/   Every Sunday our newsletter shares timeless insights and ideas that you can use at work and home. Add it to your inbox: https://fs.blog/newsletter/   Follow Shane on Twitter at: https://twitter.com/ShaneAParrish Learn more about your ad choices. Visit megaphone.fm/adchoices

Pedro Domingos, Professor Emeritus of Computer Science and Engineering at the University of Washington, is renowned for his research in machine learning, particularly for his work on Markov logic networks that allow for uncertain inference. He is also the author of the acclaimed book "The Master Algorithm". Panel: Dr. Tim Scarfe TOC: [00:00:00] Introduction [00:01:34] Galaxtica / misinformation / gatekeeping [00:12:31] Is there a master algorithm? [00:16:29] Limits of our understanding  [00:21:57] Intentionality, Agency, Creativity [00:27:56] Compositionality  [00:29:30] Digital Physics / It from bit / Wolfram  [00:35:17] Alignment / Utility functions [00:43:36] Meritocracy   [00:45:53] Game theory  [01:00:00] EA/consequentialism/Utility [01:11:09] Emergence / relationalism  [01:19:26] Markov logic  [01:25:38] Moving away from anthropocentrism  [01:28:57] Neurosymbolic / infinity / tensor algerbra [01:53:45] Abstraction [01:57:26] Symmetries / Geometric DL [02:02:46] Bias variance trade off  [02:05:49] What seen at neurips [02:12:58] Chalmers talk on LLMs [02:28:32] Definition of intelligence [02:32:40] LLMs  [02:35:14] On experts in different fields [02:40:15] Back to intelligence [02:41:37] Spline theory / extrapolation YT version:  https://www.youtube.com/watch?v=C9BH3F2c0vQ References; The Master Algorithm [Domingos] https://www.amazon.co.uk/s?k=master+algorithm&i=stripbooks&crid=3CJ67DCY96DE8&sprefix=master+algorith%2Cstripbooks%2C82&ref=nb_sb_noss_2 INFORMATION, PHYSICS, QUANTUM: THE SEARCH FOR LINKS [John Wheeler/It from Bit] https://philpapers.org/archive/WHEIPQ.pdf A New Kind Of Science [Wolfram] https://www.amazon.co.uk/New-Kind-Science-Stephen-Wolfram/dp/1579550088 The Rationalist's Guide to the Galaxy: Superintelligent AI and the Geeks Who Are Trying to Save Humanity's Future [Tom Chivers] https://www.amazon.co.uk/Does-Not-Hate-You-Superintelligence/dp/1474608795 The Status Game: On Social Position and How We Use It [Will Storr] https://www.goodreads.com/book/show/60598238-the-status-game Newcomb's paradox https://en.wikipedia.org/wiki/Newcomb%27s_paradox The Case for Strong Emergence [Sabine Hossenfelder] https://philpapers.org/rec/HOSTCF-3 Markov Logic: An Interface Layer for Artificial Intelligence [Domingos] https://www.morganclaypool.com/doi/abs/10.2200/S00206ED1V01Y200907AIM007 Note; Pedro discussed “Tensor Logic” - I was not able to find a reference Neural Networks and the Chomsky Hierarchy [Grégoire Delétang/DeepMind] https://arxiv.org/abs/2207.02098 Connectionism and Cognitive Architecture: A Critical Analysis [Jerry A. Fodor and Zenon W. Pylyshyn] https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/proseminars/Proseminar13/ConnectionistArchitecture.pdf Every Model Learned by Gradient Descent Is Approximately a Kernel Machine [Pedro Domingos] https://arxiv.org/abs/2012.00152 A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27 [LeCun] https://openreview.net/pdf?id=BZ5a1r-kVsf Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges [Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković] https://arxiv.org/abs/2104.13478 The Algebraic Mind: Integrating Connectionism and Cognitive Science [Gary Marcus] https://www.amazon.co.uk/Algebraic-Mind-Integrating-Connectionism-D

Pedro Domingos, a machine-learning pioneer and author, joins Martin Casado for an intriguing discussion on the quest for Artificial General Intelligence (AGI). They delve into the costs associated with scaling AI and the feasibility of reaching human-level intelligence without astronomical investments. The conversation also touches on the importance of embracing non-traditional perspectives to spark innovation, and Domingos shares insights from his satirical novel that critiques the intersection of AI and society, blending humor with deep philosophical questions.

Prof. Pedro Domingos, an influential AI researcher and computer science professor, shares his critical views on the current push for AI regulations, arguing they could hinder innovation. He discusses the limitations of existing AI technologies and emphasizes the need for new innovations, including his work on tensor logic that seeks to unify AI approaches. Domingos also offers insights into his satirical book, "2040," which humorously critiques tech culture and its impact on society, raising pressing questions about the future of AI and democratic governance.

Pedro Domingos, a distinguished AI researcher and author, dives into the fascinating world of machine learning. He contrasts Bayesian and Frequentist approaches, illuminating the historical divide in probability interpretation. Discussing Bayesian networks, he reveals their critical role in AI decision-making and success in fields like medical diagnosis. Pedro also critiques deep learning, suggesting it's merely a twist on nearest-neighbor learning. He questions conventional views on AI regulation and the challenges of scalability in these models.

Pedro Domingos, a leading AI researcher and author of 'The Master Algorithm', explores the Symbolist approach to AI. He discusses how Symbolic AI, dominant from the 1950s to the early 2000s, remains vital today. Domingos explains the Physical Symbol System Hypothesis and inverse deduction, showcasing its applications like decision trees and random forests that often outperform deep learning. He also delves into challenges like the knowledge acquisition bottleneck and the dynamic landscape of AI techniques, emphasizing the need for a diverse toolkit in AI development.

Pedro Domingos, a leading professor and author known for his works on machine learning, dives into transformative ideas about education and democracy in an AI-driven age. He discusses the shift in job structures due to AI and automation, highlighting the necessity for new skills. Domingos emphasizes the importance of human input alongside AI innovations and advocates for a collaborative approach to creativity. He also explores how crowdsourced intelligence can enhance democratic engagement, pushing for continuous citizen involvement beyond just voting.

Pedro Domingos, a distinguished professor of computer science and co-founder of the International Machine Learning Society, dives into the societal impact of AI. He discusses a future where AI may run for president, blending satire with profound questions about democracy. Dominating the conversation are concepts like 'digital twins' in dating and predictions for crime and elections. Domingos also highlights ethical dilemmas in AI decision-making and the evolving relationship between humans and technology, offering insights that challenge our understanding of intelligence and governance.

In this discussion, Pedro Domingos, a computer science professor and AI expert, shares insights on the nuanced impacts of AI on society. He presents his satirical novel, 2040, where an AI named Prezibot runs for president, sparking debates on democracy and the flaws in decision-making processes. Pedro examines the stark contrast between Silicon Valley's tech utopia and homelessness. He also addresses the evolving role of AI in decision-making, the challenges of AI governance, and stresses the importance of human engagement in leveraging AI’s potential responsibly.

Pedro Domingos, Professor Emeritus of Computer Science at the University of Washington and author of "The Master Algorithm," critiques the exaggerated views on AI's capabilities. He discusses his satirical novel '2040,' which humorously tackles big tech and American politics through an AI presidential candidate. The conversation delves into the complexities of portraying AI in storytelling, the ethical dilemmas of social media, and the intriguing yet contentious debate around machine consciousness.