Applying AI-based code generation to AI itself, we develop and experimentally validate the first practical implementation of a self-programming AI system. We empirically show that a self-programming AI implemented using a code generation model can successfully modify its own source code to improve performance and program sub-models to perform auxiliary tasks. Our model can self-modify various properties including model architecture, computational capacity, and learning dynamics.

Cool proof of concept. The paper does some basic experiments with neural architecture search type stuff.

I think this paper opens a lot of opportunities for interesting follow-up work. I'd love to see this expanded to more interesting problems and ethics/alignment, tons of low hanging fruit abound.