""We didn't do any LLMs. There is significant interest in that. There are lots of people trying those ideas out, but I think they're still in the exploratory phase," Desai told El Reg. As it turned out, the researchers didn't need them. "We used a simpler model called a variational auto encoder (VAE). This model was established in 2013. It's one of the early generative models," Desai said."
"The experiment builds on a 2023 paper in which Iyer and his team demonstrated a method for steering LED light that has applications in everything from autonomous vehicles to holographic projectors. The trick was finding the right combination of parameters to steer the light in the desired manner, a process researchers expected to take years. To speed this process up, Iyer enlisted the help of his colleague Saaketh Desai to develop a series of artificially intelligent lab assistants."
Sandia National Laboratories deployed three domain-specific AI lab assistants to optimize steering of LED light. The agents ran more than 300 experimental tests in five hours and discovered a novel steering approach that outperformed previous human-developed methods by fourfold. The effort built on a 2023 technique with applications in autonomous vehicles and holographic projectors, where finding parameter combinations previously could take years. The team avoided large language models and third-party APIs, instead using mature machine-learning architectures like variational autoencoders (VAE). The approach demonstrates how autonomous experimental systems can accelerate hardware optimization and design.
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