For the past few days I’ve been testing an impressive open-weight AI coding model called Flint. It suggests that fully recursive self-improvement, or RSI—an exciting and controversial approach that uses AI to build AI—is getting closer.

Flint is the first offering from Recursive, a company that aims to use RSI to advance both AI itself and other areas of science using AI. “Nearly every aspect of the entire model pipeline was actually done and improved upon by AI,” Recursive CEO and cofounder Richard Socher told me.

Socher says his company used its recursive AI system, dubbed the Eureka Machine, to automate most elements of the model’s development. This enabled a company with a handful of researchers to build a world-class model in just a few months, he says.

Socher told me that the Eureka Machine devised a number of innovations, including a new way of training the base model, new model architectures, and code optimized for specific silicon chips. Recursive shared benchmark scores that suggest Flint outperforms other models of similar size and architecture on coding tasks.

AI is already widely used to help build, test, and improve AI thanks to its amazing coding skills. This can speed up the process of refining a model and testing new ideas without the need for as much input from humans.

It is also possible to create a loop through which AI decides what improvements to make and then begins another round of improvement. For the moment this technique is limited, but if AI can contribute to every aspect of model development, then the process could theoretically continue indefinitely.

Source link