Pedro Domingos: Tensor Logic Unifies AI Paradigms
Pedro Domingos, author of the bestselling book "The Master Algorithm," introduces his latest work: Tensor Logic - a new programming language he believes could become the fundamental language for artificial intelligence.
Think of it like this: Physics found its language in calculus. Circuit design found its language in Boolean logic. Pedro argues that AI has been missing its language - until now.
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Current AI is split between two worlds that don't play well together:
Deep Learning (neural networks, transformers, ChatGPT) - great at learning from data, terrible at logical reasoning
Symbolic AI (logic programming, expert systems) - great at logical reasoning, terrible at learning from messy real-world data
Tensor Logic unifies both. It's a single language where you can:
Write logical rules that the system can actually learn and modify
Do transparent, verifiable reasoning (no hallucinations)
Mix "fuzzy" analogical thinking with rock-solid deduction
INTERACTIVE TRANSCRIPT:
https://app.rescript.info/public/share/NP4vZQ-GTETeN_roB2vg64vbEcN7isjJtz4C86WSOhw
TOC:
00:00:00 - Introduction
00:04:41 - What is Tensor Logic?
00:09:59 - Tensor Logic vs PyTorch & Einsum
00:17:50 - The Master Algorithm Connection
00:20:41 - Predicate Invention & Learning New Concepts
00:31:22 - Symmetries in AI & Physics
00:35:30 - Computational Reducibility & The Universe
00:43:34 - Technical Details: RNN Implementation
00:45:35 - Turing Completeness Debate
00:56:45 - Transformers vs Turing Machines
01:02:32 - Reasoning in Embedding Space
01:11:46 - Solving Hallucination with Deductive Modes
01:16:17 - Adoption Strategy & Migration Path
01:21:50 - AI Education & Abstraction
01:24:50 - The Trillion-Dollar Waste
REFS
Tensor Logic: The Language of AI [Pedro Domingos]
https://arxiv.org/abs/2510.12269
The Master Algorithm [Pedro Domingos]
https://www.amazon.co.uk/Master-Algorithm-Ultimate-Learning-Machine/dp/0241004543
Einsum is All you Need (TIM ROCKTÄSCHEL)
https://rockt.ai/2018/04/30/einsum
https://www.youtube.com/watch?v=6DrCq8Ry2cw
Autoregressive Large Language Models are Computationally Universal (Dale Schuurmans et al - GDM)
https://arxiv.org/abs/2410.03170
Memory Augmented Large Language Models are Computationally Universal [Dale Schuurmans]
https://arxiv.org/pdf/2301.04589
On the computational power of NNs [95/Siegelmann]
https://binds.cs.umass.edu/papers/1995_Siegelmann_JComSysSci.pdf
Sebastian Bubeck
https://www.reddit.com/r/OpenAI/comments/1oacp38/openai_researcher_sebastian_bubeck_falsely_claims/
I am a strange loop - Hofstadter
https://www.amazon.co.uk/Am-Strange-Loop-Douglas-Hofstadter/dp/0465030793
Stephen Wolfram
https://www.youtube.com/watch?v=dkpDjd2nHgo
The Complex World: An Introduction to the Foundations of Complexity Science [David C. Krakauer]
https://www.amazon.co.uk/Complex-World-Introduction-Foundations-Complexity/dp/1947864629
Geometric Deep Learning
https://www.youtube.com/watch?v=bIZB1hIJ4u8
Andrew Wilson (NYU)
https://www.youtube.com/watch?v=M-jTeBCEGHc
Yi Ma
https://www.patreon.com/posts/yi-ma-scientific-141953348
Roger Penrose - road to reality
https://www.amazon.co.uk/Road-Reality-Complete-Guide-Universe/dp/0099440687
Artificial Intelligence: A Modern Approach [Russel and Norvig]
https://www.amazon.co.uk/Artificial-Intelligence-Modern-Approach-Global/dp/1292153962
#250 Pedro Domingos on the Real Path to AGI
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Can AI Ever Reach AGI? Pedro Domingos Explains the Missing Link
In this episode of Eye on AI, renowned computer scientist and author of The Master Algorithm, Pedro Domingos, breaks down what's still missing in our race toward Artificial General Intelligence (AGI) — and why the path forward requires a radical unification of AI's five foundational paradigms: Symbolists, Connectionists, Bayesians, Evolutionaries, and Analogizers.
Topics covered:
Why deep learning alone won't achieve AGI
How reasoning by analogy could unlock true machine creativity
The role of evolutionary algorithms in building intelligent systems
Why transformers like GPT-4 are impressive—but incomplete
The danger of hype from tech leaders vs. the real science behind AGI
What the Master Algorithm truly means — and why we haven't found it yet
Pedro argues that creativity is easy, reliability is hard, and that reasoning by analogy — not just scaling LLMs — may be the key to Einstein-level breakthroughs in AI.
Whether you're an AI researcher, machine learning engineer, or just curious about the future of artificial intelligence, this is one of the most important conversations on how to actually reach AGI.
📚 About Pedro Domingos: Pedro is a professor at the University of Washington and author of the bestselling book The Master Algorithm, which explores how the unification of AI's "five tribes" could produce the ultimate learning algorithm.
Stay Updated:
Craig Smith on X:https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI
(00:00) The Five Tribes of AI Explained
(02:23) The Origins of The Master Algorithm
(08:22) Designing with Bit Strings: Radios, Robots & More
(10:46) Fitness Functions vs Reward Functions in AI
(15:51) What Is Reasoning by Analogy in AI?
(18:38) Kernel Machines and Support Vector Machines Explained
(22:23) Case-Based Reasoning and Real-World Use Cases
(27:38) Are AI Tribes Still Siloed or Finally Collaborating?
(32:42) Why AI Needs a Deeply Unified Master Algorithm
(36:40) Creativity vs Reliability in AI
(39:14) Can AI Achieve Scientific Breakthroughs?
(41:26) Why Reasoning by Analogy Is AI's Missing Link
(45:10) Evolutionaries: The Most Distant Tribe in AI
(48:41) Will Quantum Computing Help AI Reach AGI?
(53:15) Are We Close to the Master Algorithm?
(57:44) Tech Leaders, Hype & the Reality of AGI
(01:04:06) The AGI Spectrum: Where We Are & What's Missing
(01:06:18) Pedro's Research Focus
#248 Pedro Domingos: How Connectionism Is Reshaping the Future of Machine Learning
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In this episode, renowned AI researcher Pedro Domingos, author of The Master Algorithm, takes us deep into the world of Connectionism—the AI tribe behind neural networks and the deep learning revolution.
From the birth of neural networks in the 1940s to the explosive rise of transformers and ChatGPT, Pedro unpacks the history, breakthroughs, and limitations of connectionist AI. Along the way, he explores how supervised learning continues to quietly power today's most impressive AI systems—and why reinforcement learning and unsupervised learning are still lagging behind.
We also dive into:
The tribal war between Connectionists and Symbolists
The surprising origins of Backpropagation
How transformers redefined machine translation
Why GANs and generative models exploded (and then faded)
The myth of modern reinforcement learning (DeepSeek, RLHF, etc.)
The danger of AI research narrowing too soon around one dominant approach
Whether you're an AI enthusiast, a machine learning practitioner, or just curious about where intelligence is headed, this episode offers a rare deep dive into the ideological foundations of AI—and what's coming next.
Don't forget to subscribe for more episodes on AI, data, and the future of tech.
Stay Updated:
Craig Smith on X:https://x.com/craigss
Eye on A.I. on X: https://x.com/EyeOn_AI
(00:00) What Are Generative Models?
(03:02) AI Progress and the Local Optimum Trap
(06:30) The Five Tribes of AI and Why They Matter
(09:07) The Rise of Connectionism
(11:14) Rosenblatt's Perceptron and the First AI Hype Cycle
(13:35) Backpropagation: The Algorithm That Changed Everything
(19:39) How Backpropagation Actually Works
(21:22) AlexNet and the Deep Learning Boom
(23:22) Why the Vision Community Resisted Neural Nets
(25:39) The Expansion of Deep Learning
(28:48) NetTalk and the Baby Steps of Neural Speech
(31:24) How Transformers (and Attention) Transformed AI
(34:36) Why Attention Solved the Bottleneck in Translation
(35:24) The Untold Story of Transformer Invention
(38:35) LSTMs vs. Attention: Solving the Vanishing Gradient Problem
(42:29) GANs: The Evolutionary Arms Race in AI
(48:53) Reinforcement Learning Explained
(52:46) Why RL Is Mostly Just Supervised Learning in Disguise
(54:35) Where AI Research Should Go Next
#237 Pedro Domingos Breaks Down The Symbolist Approach to AI
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In this episode of the Eye on AI podcast, Pedro Domingos—renowned AI researcher and author of The Master Algorithm—joins Craig Smith to break down the Symbolist approach to artificial intelligence, one of the Five Tribes of Machine Learning.
Pedro explains how Symbolic AI dominated the field for decades, from the 1950s to the early 2000s, and why it's still playing a crucial role in modern AI. He dives into the Physical Symbol System Hypothesis, the idea that intelligence can emerge purely from symbol manipulation, and how AI pioneers like Marvin Minsky and John McCarthy built the foundation for rule-based AI systems.
The conversation unpacks inverse deduction—the Symbolists' "Master Algorithm"—and how it allows AI to infer general rules from specific examples. Pedro also explores how decision trees, random forests, and boosting methods remain some of the most powerful AI techniques today, often outperforming deep learning in real-world applications.
We also discuss why expert systems failed, the knowledge acquisition bottleneck, and how machine learning helped solve Symbolic AI's biggest challenges. Pedro shares insights on the heated debate between Symbolists and Connectionists, the ongoing battle between logic-based reasoning and neural networks, and why the future of AI lies in combining these paradigms.
From AlphaGo's hybrid approach to modern AI models integrating logic and reasoning, this episode is a deep dive into the past, present, and future of Symbolic AI—and why it might be making a comeback.
Don't forget to like, subscribe, and hit the notification bell for more expert discussions on AI, technology, and the future of intelligence!
Stay Updated:
Craig Smith Twitter: https://twitter.com/craigss
Eye on A.I. Twitter: https://twitter.com/EyeOn_AI
(00:00) Pedro Domingos onThe Five Tribes of Machine Learning
(02:23) What is Symbolic AI?
(04:46) The Physical Symbol System Hypothesis Explained
(07:05) Understanding Symbols in AI
(11:51) What is Inverse Deduction?
(15:10) Symbolic AI in Medical Diagnosis
(17:35) The Knowledge Acquisition Bottleneck
(19:05) Why Symbolic AI Struggled with Uncertainty
(20:40) Machine Learning in Symbolic AI – More Than Just Connectionism
(24:08) Decision Trees & Their Role in Symbolic Learning
(26:55) The Myth of Feature Engineering in Deep Learning
(30:18) How Symbolic AI Invents Its Own Rules
(31:54) The Rise and Fall of Expert Systems – The CYCL Project
(38:53) Symbolic AI vs. Connectionism
(41:53) Is Symbolic AI Still Relevant Today?
(43:29) How AlphaGo Combined Symbolic AI & Neural Networks
(45:07) What Symbolic AI is Best At – System 2 Thinking
(47:18) Is GPT-4o Using Symbolic AI?
#236 Pedro Domingo's on Bayesians and Analogical Learning in AI
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In this episode of the Eye on AI podcast, Pedro Domingos, renowned AI researcher and author of The Master Algorithm, joins Craig Smith to explore the evolution of machine learning, the resurgence of Bayesian AI, and the future of artificial intelligence.
Pedro unpacks the ongoing battle between Bayesian and Frequentist approaches, explaining why probability is one of the most misunderstood concepts in AI. He delves into Bayesian networks, their role in AI decision-making, and how they powered Google's ad system before deep learning. We also discuss how Bayesian learning is still outperforming humans in medical diagnosis, search & rescue, and predictive modeling, despite its computational challenges.
The conversation shifts to deep learning's limitations, with Pedro revealing how neural networks might be just a disguised form of nearest-neighbor learning. He challenges conventional wisdom on AGI, AI regulation, and the scalability of deep learning, offering insights into why Bayesian reasoning and analogical learning might be the future of AI.
We also dive into analogical learning—a field championed by Douglas Hofstadter—exploring its impact on pattern recognition, case-based reasoning, and support vector machines (SVMs). Pedro highlights how AI has cycled through different paradigms, from symbolic AI in the '80s to SVMs in the 2000s, and why the next big breakthrough may not come from neural networks at all.
From theoretical AI debates to real-world applications, this episode offers a deep dive into the science behind AI learning methods, their limitations, and what's next for machine intelligence.
Don't forget to like, subscribe, and hit the notification bell for more expert discussions on AI, technology, and the future of innovation!
Stay Updated:
Craig Smith Twitter: https://twitter.com/craigss
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(00:00) Introduction
(02:55) The Five Tribes of Machine Learning Explained
(06:34) Bayesian vs. Frequentist: The Probability Debate
(08:27) What is Bayes' Theorem & How AI Uses It
(12:46) The Power & Limitations of Bayesian Networks
(16:43) How Bayesian Inference Works in AI
(18:56) The Rise & Fall of Bayesian Machine Learning
(20:31) Bayesian AI in Medical Diagnosis & Search and Rescue
(25:07) How Google Used Bayesian Networks for Ads
(28:56) The Role of Uncertainty in AI Decision-Making
(30:34) Why Bayesian Learning is Computationally Hard
(34:18) Analogical Learning – The Overlooked AI Paradigm
(38:09) Support Vector Machines vs. Neural Networks
(41:29) How SVMs Once Dominated Machine Learning
(45:30) The Future of AI – Bayesian, Neural, or Hybrid?
(50:38) Where AI is Heading Next
#210 Pedro Domingos: Exploring AI's Impact on Politics and Society
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In this episode of the Eye on AI podcast, we sit down with Pedro Domingos, professor of computer science and author of The Master Algorithm and 2040, to dive deep into the future of artificial intelligence, machine learning, and AI governance.
Pedro shares his expertise in AI, offering a unique perspective on the real dangers and potential of AI, far from the apocalyptic fears of superintelligence taking over. We explore his satirical novel, 2040, where an AI candidate for president—Prezibot—raises questions about control, democracy, and the flaws in both AI systems and human decision-makers.
Throughout the episode, Pedro sheds light on Silicon Valley's utopian dreams clashing with its dystopian realities, highlighting the contrast between tech innovation and societal challenges like homelessness. He discusses how AI has already integrated into our daily lives, from recommendation systems to decision-making tools, and what this means for the future.
We also unpack the ongoing debate around AI safety, the limits of current AI models like ChatGPT, and why he believes AI is more of a tool to amplify human intelligence rather than an existential threat. Pedro offers his insights into the future of AI development, focusing on how symbolic AI and neural networks could pave the way for more reliable and intelligent systems.
Don't forget to like, subscribe, and hit the notification bell to stay updated on the latest insights into AI, machine learning, and tech culture.
Stay Updated:
Craig Smith Twitter: https://twitter.com/craigss
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(00:00) Preview and Introduction
(01:06) Pedro's Background and Contributions to AI
(03:36) The Satirical Take on AI in '2040'
(05:42) AI Safety Debate: Geoffrey Hinton vs. Yann LeCun
(08:06) Debunking AI's Real Risks
(12:45) Satirical Elements in '2040': HappyNet and Prezibot
(17:57) AI as a Decision-Making Tool: Potential and Risks
(22:55) The Limits of AI as an Arbiter of Truth
(27:35) Crowdsourced AI: PreziBot 2.0 and Real-Time Decision Making
(29:54) AI Governance and the Kill Switch Debate
(37:42) Integrating AI into Society: Challenges and Optimism
(47:11) Pedro's Current Research and Future of AI
(55:17) Scaling AI and the Future of Reinforcement Learning
"AI should NOT be regulated at all!" - Prof. Pedro Domingos
Professor Pedro Domingos, is an AI researcher and professor of computer science. He expresses skepticism about current AI regulation efforts and argues for faster AI development rather than slowing it down. He also discusses the need for new innovations to fulfil the promises of current AI techniques.
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Show notes:
* Domingos' views on AI regulation and why he believes it's misguided
* His thoughts on the current state of AI technology and its limitations
* Discussion of his novel "2040", a satirical take on AI and tech culture
* Explanation of his work on "tensor logic", which aims to unify neural networks and symbolic AI
* Critiques of other approaches in AI, including those of OpenAI and Gary Marcus
* Thoughts on the AI "bubble" and potential future developments in the field
Prof. Pedro Domingos:
https://x.com/pmddomingos
2040: A Silicon Valley Satire [Pedro's new book]
https://amzn.to/3T51ISd
TOC:
00:00:00 Intro
00:06:31 Bio
00:08:40 Filmmaking skit
00:10:35 AI and the wisdom of crowds
00:19:49 Social Media
00:27:48 Master algorithm
00:30:48 Neurosymbolic AI / abstraction
00:39:01 Language
00:45:38 Chomsky
01:00:49 2040 Book
01:18:03 Satire as a shield for criticism?
01:29:12 AI Regulation
01:35:15 Gary Marcus
01:52:37 Copyright
01:56:11 Stochastic parrots come home to roost
02:00:03 Privacy
02:01:55 LLM ecosystem
02:05:06 Tensor logic
Refs:
The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World [Pedro Domingos]
https://amzn.to/3MiWs9B
Rebooting AI: Building Artificial Intelligence We Can Trust [Gary Marcus]
https://amzn.to/3AAywvL
Flash Boys [Michael Lewis]
https://amzn.to/4dUGm1M
#96 Prof. PEDRO DOMINGOS - There are no infinities, utility functions, neurosymbolic
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
#65 Prof. PEDRO DOMINGOS [Unplugged]
Note: there are no politics discussed in this show and please do not interpret this show as any kind of a political statement from us. We have decided not to discuss politics on MLST anymore due to its divisive nature.
Patreon: https://www.patreon.com/mlst
Discord: https://discord.gg/HNnAwSduud
[00:00:00] Intro
[00:01:36] What we all need to understand about machine learning
[00:06:05] The Master Algorithm Target Audience
[00:09:50] Deeply Connected Algorithms seen from Divergent Frames of Reference
[00:12:49] There is a Master Algorithm; and it's mine!
[00:14:59] The Tribe of Evolution
[00:17:17] Biological Inspirations and Predictive Coding
[00:22:09] Shoe-Horning Gradient Descent
[00:27:12] Sparsity at Training Time vs Prediction Time
[00:30:00] World Models and Predictive Coding
[00:33:24] The Cartoons of System 1 and System 2
[00:40:37] AlphaGo Searching vs Learning
[00:45:56] Discriminative Models evolve into Generative Models
[00:50:36] Generative Models, Predictive Coding, GFlowNets
[00:55:50] Sympathy for a Thousand Brains
[00:59:05] A Spectrum of Tribes
[01:04:29] Causal Structure and Modelling
[01:09:39] Entropy and The Duality of Past vs Future, Knowledge vs Control
[01:16:14] A Discrete Universe?
[01:19:49] And yet continuous models work so well
[01:23:31] Finding a Discretised Theory of Everything
#042 - Pedro Domingos - Ethics and Cancel Culture
Today we have professor Pedro Domingos and we are going to talk about activism in machine learning, cancel culture, AI ethics and kernels. In Pedro's book the master algorithm, he segmented the AI community into 5 distinct tribes with 5 unique identities (and before you ask, no the irony of an anti-identitarian doing do was not lost on us!). Pedro recently published an article in Quillette called Beating Back Cancel Culture: A Case Study from the Field of Artificial Intelligence. Domingos has railed against political activism in the machine learning community and cancel culture. Recently Pedro was involved in a controversy where he asserted the NeurIPS broader impact statements are an ideological filter mechanism.
Important Disclaimer: All views expressed are personal opinions.
00:00:00 Caveating
00:04:08 Main intro
00:07:44 Cancelling culture is a culture and intellectual weakness
00:12:26 Is cancel culture a post-modern religion?
00:24:46 Should we have gateways and gatekeepers?
00:29:30 Does everything require broader impact statements?
00:33:55 We are stifling diversity (of thought) not promoting it.
00:39:09 What is fair and how to do fair?
00:45:11 Models can introduce biases by compressing away minority data
00:48:36 Accurate but unequal soap dispensers
00:53:55 Agendas are not even self-consistent
00:56:42 Is vs Ought: all variables should be used for Is
01:00:38 Fighting back cancellation with cancellation?
01:10:01 Intent and degree matter in right vs wrong.
01:11:08 Limiting principles matter
01:15:10 Gradient descent and kernels
01:20:16 Training Journey matter more than Destination
01:24:36 Can training paths teach us about symmetry?
01:28:37 What is the most promising path to AGI?
01:31:29 Intelligence will lose its mystery
Episode 10 - Pedro Domingos
In this week's episode, I talk to Pedro Domingos, author of the bestselling book, The Master Algorithm, which is about the ongoing effort to unify machine-learning paradigms in a single model. But the conversation was much broader than that. Pedro believes strongly that the great powers are engaged in an AI arms race with America's Defense Advanced Research Projects Agency, or Darpa, pitted against China's military and industrial dynamo. We also talked about the future of democracy and authoritarianism in an AI-driven world.
