
The Jim Rutt Show EP 249 Seth Lloyd on Measuring Complexity
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Aug 6, 2024 Seth Lloyd, a professor of mechanical engineering at MIT and an expert in measuring complexity, dives into the challenges of quantifying complexity across various scientific fields. He discusses Kolmogorov complexity and Shannon entropy, unraveling their implications for understanding biological systems. Exploring cellular automata and effective complexity, he demonstrates how simple rules can generate complex behaviors. Lloyd also examines bacterial metabolism, network complexity, and the environmental concerns of large language models, revealing the intricate dance between order and chaos in our universe.
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No Single Universal Complexity Measure
- Complexity resists a single universal definition because it applies to many qualitatively different systems.
- Different fields develop measures that fit their specific kinds of complexity.
Randomness Inflates Algorithmic Complexity
- Algorithmic (Kolmogorov) complexity rates random strings as highly complex because they're hard to compress.
- But random high-complexity strings lack the structured, functional complexity we intuitively value.
Complexity Via Computational Time
- Logical depth measures complexity by how long a short program takes to produce an output.
- Deep objects (like many digits of pi) require little description but long computation to generate.

