AFAICT the "AI" didn't "pay attention to the work" either. They built a representation of a set of possible experiments, defined an objective function quantifyi

AFAICT the "AI" didn't "pay attention to the work" either. They built a representation of a set of possible experiments, defined an objective function quantifying what they wanted to optimise and used gradient descent to find the best experiment according to that objective function. If I've understood it right, calling this AI is a stretch and arguably even misleading. Gradient descent is the primary tool of machine learning, but this isn't really using it the way machine learning uses it. It's more just an application of gradient descent to an optimisation problem. The article and headline make it sound like they asked an LLM to make an experiment and it used some obscure Russian technique to make a really cool one. That isn't true at all. The algorithm they used had no awareness of the Russian research, or of language, or experimental design. It wasn't "trained" in any sense. It was just a gradient descent program. It's the researchers that recognised the Russian technique when analyzing the experiment the optimiser chose.