Transparent machine learning is introduced as an alternative form of machine learning, where both the model and the learning system are represented in source code form. The goal of this project is to enable direct human understanding of machine learning models, giving us the ability to learn, verify, and refine them as programs. If solved, this technology could represent a best-case scenario for the safety and security of AI systems going forward.

EXINT is a byte-aligned universal code with complete support for the integers. It is byte-order agnostic and has O(1) time performance when bounded by the system datapath, integer, or memory width.

The control problem is a question posed by Nick Bostrom on how to limit advanced artificial intelligence while still benefiting from its use. I propose an extension to the original control problem that separates it into a local and global version. I then provide proofs that the global version has no solution.

A book on the technical and societal impacts of advanced artificial intelligence.