N N Taleb's Probability Questions (UNOFFICIAL) STEPHEN WOLFRAM VISITS RWRI 19, SUMMER SCHOOL 2024
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Nov 11, 2025 
Stephen Wolfram, a pioneering computer scientist and the mind behind Mathematica and Wolfram|Alpha, dives deep into computational irreducibility and its significance. He explores how cellular automata can model biological evolution and how randomness aids adaptive searches. The discussion extends to the interplay between neural networks and irreducibility, touching upon challenges in extrapolation. Wolfram links these concepts to climate modeling and policy, advocating for conservative approaches amid uncertainty. His insights weave together AI, ethics, and the nature of scientific discovery.
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Why Machine Learning 'Just Works'
- Machine learning often finds working solutions without human-understandable narratives because training explores irreducible computational space.
- When training succeeds it typically yields complex, non-engineered representations rather than simple explanations.
Match ML Tools To What Training Needs
- Recognize that many ML infrastructures (continuous calculus, real-number heavy math) may be unnecessary for effective learned behavior.
- Focus engineering on what the training process actually needs rather than assumed mathematical complexity.
Observers Shape Observed Laws
- Our limited computational perspective interacting with irreducible processes helps produce the effective laws we observe.
- Observers' boundedness selects pockets of reducibility where science can form narratives.