Subvolts take Understand AI's core math and cognitive science links with Princeton's Tom Griffiths. Jan 19, 2026 - Developers
- Learn about the theoretical frameworks that underpin modern AI development and how they integrate.
- Business
- Understand the cognitive science principles behind AI and how they inform business applications.
- Everyday users
- Grasp the fundamental differences between human and machine intelligence and what humans still do better.
A solid interview with a university professor discussing foundational AI concepts and their relation to current models.
Connecting Language and (Artificial) Intelligence: Princeton’s Tom Griffiths
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From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
In this bonus episode of Me, Myself, and AI, Princeton professor Tom Griffiths discusses his new book, The Laws of Thought. Griffiths explores how mathematics has historically informed our understanding of human and machine intelligence. He details three core frameworks in AI: rules and symbols, neural networks, and probability, explaining their integration in modern systems. The conversation links cognitive science, large language models, and the distinct capabilities of human versus artificial intelligence.
This episode explains how math helps us understand thinking, for both people and computers. It covers different ways AI works, like using rules or brain-like networks, and how they fit together. You'll learn what makes human intelligence different from AI, like our judgment.
This episode offers a foundational understanding of AI's theoretical underpinnings, connecting cognitive science with current large language models. It's for anyone seeking to grasp how AI systems are built and how they relate to human thought processes. Griffiths' explanation of integrating different AI frameworks provides context for the rapid advancements seen across frontier labs like Google Gemini and OpenAI, highlighting the ongoing research into the limits and potential of machine intelligence.
- HostSam Ransbotham
- HostShervin Khodabandeh
- GuestTom Griffithsprofessor, Princeton University
- Guest: Tom Griffiths, Princeton University professor
- Book: The Laws of Thought
- Show: Me, Myself, and AI
- Length: 43 minutes
- AI Frameworks Discussed: Rules and symbols, neural networks, and probability
In this bonus episode, Princeton University professor and artificial intelligence researcher Tom Griffiths joins Sam to unpack The Laws of Thought, his new book exploring how math has been used for centuries to understand how minds — human and machine — actually work.
Tom walks through three main frameworks shaping intelligence today — rules and symbols, neural networks, and probability — and he explains why modern AI only makes sense when you see how those pieces fit together.
The conversation connects cognitive science, large language models, and the limits of human versus machine intelligence. Along the way, Tom and Sam dig into language, learning, and what humans still do better — like judgment, curation, and metacognition. Read the episode transcript here.
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- neural networks
- Tom Griffiths
- AI systems inspired by the structure of the human brain.
- large language models
- Tom Griffiths
- AI that understands and generates human-like text.
- metacognition
- Tom Griffiths
- Thinking about your own thinking; self awareness of cognitive processes.
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Questions people ask
- What is the main topic of the episode?
- The episode unpacks "The Laws of Thought," exploring how mathematics has been used to understand human and machine intelligence, and how different AI frameworks fit together.
- What are the three main frameworks shaping intelligence discussed?
- The three frameworks are rules and symbols, neural networks, and probability. The episode explains why modern AI makes sense when these pieces are seen as integrated.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to MIT Sloan Management Review.