Position Paper: Towards Transparent Machine Learning

2019.11.01

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.

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Will AI ever be human compatible?

2019.10.08

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.

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Extensible Integer Coding (EXINT)

2016.12.28

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.

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Saving The Control Problem

2016.12.18

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.

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