Large Language Models (LLMs) function as massive matrices that perform Bayesian updating to predict the next token, rather than possessing consciousness or an inner monologue. This mathematical framework, validated through "Bayesian wind tunnel" experiments, demonstrates that transformer architectures achieve near-perfect Bayesian posterior accuracy. While these models excel at correlation—effectively navigating existing data manifolds—they remain limited by their frozen weights and lack of causal reasoning. Achieving Artificial General Intelligence (AGI) requires moving beyond Shannon entropy-based predictions toward Kolmogorov complexity, where systems can generate new representations of the world through causal simulation and continual learning. Current models are restricted by "data gravity," which forces them to treat anomalous evidence as noise, preventing the kind of paradigm-shifting breakthroughs seen in human scientific discovery, such as Einstein’s theory of relativity.
Sign in to continue reading, translating and more.
Continue
