Institute Output
Human Language and Machine Language
The explosion of large language models means that we are all suddenly awash in machine-generated language, spoken, written and even sung. There are many advantages to the artificial intelligence produced like this, and many disadvantages too: it remains to be seen what the eventual outcome will be. But there is one particular idea that keeps bubbling up to the surface: that AI is going to decide to kill us all. This, I suggest, is based on a misunderstanding of the nature of language.
Kolmogorov Complexity vs. Computational Irreducibility: Understanding the Distinction
James K. Wiles
Kolmogorov complexity and computational irreducibility describe two kinds of limits on simplification, but they apply in different ways. Kolmogorov complexity measures the shortest possible description of an object, such as a string. Computational irreducibility refers to processes that cannot be predicted or accelerated. This paper introduces each concept, explains their theoretical distinction, and illustrates the difference using simple examples.
Observer Theory and the Ruliad: An Extension to the Wolfram Model
Sam A. Senchal
This paper introduces a rigorous category-theoretic extension to Observer Theory within Wolfram's Ruliad framework, demonstrating how observers and observes like us sample and integrate information across hierarchical domains, addressing consciousness, causation, and the transition from discrete computational processes to continuous perceived reality.