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Pedro Domingos: Tensor Logic Unifies AI Paradigms

Dec 8, 2025

Guest

Pedro Domingos

Pedro Domingos, a leading computer science professor at the University of Washington and author of The Master Algorithm, unveils his groundbreaking concept, TensorLogic. He discusses how this innovative programming language could unify the fragmented worlds of Deep Learning and Symbolic AI. Pedro reveals TensorLogic's capabilities in logical reasoning and learning from data, emphasizing its potential to prevent AI hallucinations. He also shares insights on how TensorLogic can express complex systems and improve AI education, paving the way for a more integrated future in artificial intelligence.

01:27:48

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insights

INSIGHT

One Construct Bridges Learning And Logic

TensorLogic unifies tensor algebra and logic programming by treating einsum-like tensor equations as rules over real or Boolean types.
A single tensor equation construct can express learning, reasoning, and scalable GPU computation in one language.

insights

INSIGHT

Databases, Probabilities And NNs As Tensors

TensorLogic generalizes joins and projections to numeric tensors so Boolean Datalog becomes a numeric special case.
This lets the same operations express databases, probabilistic graphical models, kernels, and neural computations.

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ADVICE

Optimize Einsum With A CUDA Back End

Implement the tensor-equation abstraction efficiently on CUDA to make einsum-style programs practical and fast.
Optimize a single high-level construct and you unlock performance and developer adoption over clunky einsum syntax.

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Why TensorLogic and the Need for an AI Language

03:22 • 39sec

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Sponsors, Human Data, and Episode Context

TensorLogic: Language Requirements and Goals

04:59 • 2min

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Einsum, Tensors, and Logic: The Core Insight

06:50 • 4min

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Practical Benefits Over PyTorch Einsum

10:31 • 2min

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Concrete Example: Logic OR as Einsum+Heaviside

12:58 • 4min

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TensorLogic and The Master Algorithm Vision

17:23 • 3min

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Structure Learning and Predicate Invention

20:40 • 4min

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Encoding Networks, Architecture Discovery

24:41 • 4min

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Balancing Generality and Inductive Bias

28:36 • 3min

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Symmetries, Geometric Deep Learning, and Regularities

31:20 • 4min

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Complex Systems, Spontaneous Symmetry Breaking, and Prediction

35:31 • 5min

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TensorLogic as a Meta-Representation for Multiple Levels

40:31 • 3min

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Learning the Logic and RNN Notation

43:30 • 3min

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Universality, Turing Completeness, and Memory

46:45 • 7min

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Practical Iterator Semantics and Unbounded Computation

53:31 • 3min

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Transformers, Context Size, and Generalization Limits

56:20 • 5min

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Universal Induction vs. Universal Deduction

01:00:51 • 3min

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Reasoning in Embedding Space: Sound Deduction and Analogy

01:04:15 • 4min

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Structure Mapping, Case-Based Reasoning, and Analogy Power

01:07:47 • 5min

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Hallucination, Soundness, and Non-Hallucinating Modes

01:12:22 • 4min

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Adoption Strategy, Education, and Migration Path

01:16:18 • 9min

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Abstractions, Efficiency, and Teaching AI

• Mentioned in 2 episodes

The Complex World

An Introduction to the Fundamentals of Complexity Science

David Krakauer

#4202

• Mentioned in 9 episodes

The Master Algorithm

How the Quest for the Ultimate Learning Machine Will Remake Our World

Pedro Domingos

#3696

• Mentioned in 10 episodes

Artificial Intelligence, a Modern Approach

Peter Norvig

Blaise Aguera y Arcas

Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig, is a comprehensive textbook covering various aspects of artificial intelligence. It provides a broad overview of the field, encompassing search algorithms, knowledge representation, reasoning, machine learning, and natural language processing. The book is known for its clear explanations, numerous examples, and extensive coverage of both classical and modern AI techniques. It serves as a valuable resource for students and researchers alike, offering a solid foundation in the principles and applications of AI. Its wide adoption makes it a standard reference in the field.

#2832

• Mentioned in 13 episodes

The Road to Reality

A Complete Guide to the Laws of the Universe

Roger Penrose

#2355

• Mentioned in 16 episodes

I Am a Strange Loop

Douglas Hofstadter

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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**END**

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

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