One way AI can improve on human work

Computer scientists at UC Berkeley say that AI models show promise as a way to discover and optimize algorithms.

In a preprint paper titled "Barbarians at the Gate: How AI is Upending Systems Research," 17 UC Berkeley researchers describe how they employed OpenEvolve, an open source implementation of Google DeepMind's AlphaEvolve, to improve a load balancing algorithm so that it significantly outperforms prior human designs.

Specifically, the authors claim to have used OpenEvolve to achieve a 5x speedup for an Expert Parallelism Load Balancer (EPLB) algorithm, which is used in large language models to route tokens to specialized expert modules – an efficiency mechanism that reduces the number of processed parameters.

The authors say that AI-Driven Research for Systems (ADRS), through which an AI model iteratively generates, evaluates, and refines solutions, promises to transform systems research.

"As AI assumes a central role in algorithm design, we argue that human researchers ...