In this episode, we welcome David Wolpert, a Professor at the Santa Fe Institute renowned for his groundbreaking work across multiple disciplines—from physics and computer science to game theory and complexity.
* Note: If you enjoy our podcast conversations, please join us for the Artificiality Summit on October 23-25 in Bend, Oregon for many more in person conversations like these! Learn more about the Summit at www.artificiality.world/summit.
We reached out to David to explore the mathematics of meaning—a concept that's becoming crucial as we live more deeply with artificial intelligences. If machines can hold their own mathematical understanding of meaning, how does that reshape our interactions, our shared reality, and even what it means to be human?
David takes us on a journey through his paper "Semantic Information, Autonomous Agency and Non-Equilibrium Statistical Physics," co-authored with Artemy Kolchinsky. While mathematically rigorous in its foundation, our conversation explores these complex ideas in accessible terms.
At the core of our discussion is a novel framework for understanding meaning itself—not just as a philosophical concept, but as something that can be mathematically formalized. David explains how we can move beyond Claude Shannon's syntactic information theory (which focuses on the transmission of bits) to a deeper understanding of semantic information (what those bits actually mean to an agent).
Drawing from Judea Pearl's work on causality, Schrödinger's insights on life, and stochastic thermodynamics, David presents a unified framework where meaning emerges naturally from an agent's drive to persist into the future. This approach provides a mathematical basis for understanding what makes certain information meaningful to living systems—from humans to single cells.
Our conversation ventures into:
- How AI might help us understand meaning in ways we cannot perceive ourselves
- What a mathematically rigorous definition of meaning could mean for AI alignment
- How contexts shape our understanding of what's meaningful
- The distinction between causal information and mere correlation
We finish by talking about David's current work on a potentially concerning horizon: how distributed AI systems interacting through smart contracts could create scenarios beyond our mathematical ability to predict—a "distributed singularity" that might emerge in as little as five years. We wrote about this work here.
For anyone interested in artificial intelligence, complexity science, or the fundamental nature of meaning itself, this conversation offers rich insights from one of today's most innovative interdisciplinary thinkers.
About David Wolpert:
David Wolpert is a Professor at the Santa Fe Institute and one of the modern era's true polymaths. He received his PhD in physics from UC Santa Barbara but has made seminal contributions across numerous fields. His research spans machine learning (where he formulated the "No Free Lunch" theorems), statistical physics, game theory, distributed intelligence, and the foundations of inference and computation. Before joining SFI, Wolpert held positions at NASA, Stanford, and the Santa Fe Institute as a professor. His work consistently bridges disciplinary boundaries to address fundamental questions about complex systems, computation, and the nature of intelligence.
Thanks again to Jonathan Coulton for our music.
