Unlocking the Library of AI


Formal AI will enable human and AI programmers to collaborate on a massive scale. This will exponentially accelerate progress in AI.

The Reflexivity of Sentience


Sentience is reflexive. This implies that it is impossible for a subject to be conscious without senses, as sentience itself is a modality of experience.

On The Information Theoretic Properties of Sentience


An analysis of the subjectivity of experience and sentience under an information theoretic approach. This essay contains significant updates to my core beliefs about consciousness.

Beyond Program Synthesis


Generating source code from natural language is not formal artificial intelligence, even though it sounds similar to some of the things I have described about it before.

A Tale of Two Neumanns


A brief look into the history of the stored-program computer and its parallels to formal artificial intelligence.

Position Paper: Towards Transparent Machine Learning


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.

Will AI ever be human compatible?


This is a review of “Human Compatible” by computer scientist, Stewart Russell. The thesis of this book is that we need to change the way we develop AI if we want it to remain beneficial to us in the future. Russell discusses a different kind of machine learning approach to help solve the problem.

Extensible Integer Coding (EXINT)


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.

Saving The Control Problem


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.