
36 - Adam Shai and Paul Riechers on Computational Mechanics
AXRP - the AI X-risk Research Podcast
Predictions and Probabilities: The Mechanics of Future Insights
This chapter examines the use of computational mechanics and Bayesian statistics in predicting future outcomes through innovative modeling techniques. It discusses the significance of hidden Markov models and epsilon machines, along with the necessity of efficient data representation. The exploration extends to the relationship between stochastic processes and predictive modeling, emphasizing the nuances of inference and generation while considering their implications in neural network applications.
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