If modern artificial intelligence has a founding document, a sacred text, it is Google’s 2017 research paper “Attention Is All You Need.” This paper introduced a new deep learning architecture known as the transformer, which has gone on to revolutionize the field of AI over the past half-decade.

The generative AI mania currently taking the world by storm can be traced directly to the invention of the transformer. Every major AI model and product in the headlines today—ChatGPT, GPT-4, Midjourney, Stable Diffusion, GitHub Copilot, and so on—is built using transformers.

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Transformers are remarkably general-purpose: while they were initially developed for language translation specifically, they are now advancing the state of the art in domains ranging from computer vision to robotics to computational biology.

Rice University scientists are starting small as they begin to figure out how to build an artificial brain from the bottom up.

Electrical and computer engineer Jacob Robinson of Rice’s Brown School of Engineering and Celina Juliano, an assistant professor of molecular and cellular biology at the University of California, Davis, have won a $1 million Keck Foundation grant to advance the team’s synthetic neurobiology effort to define the connections between neurons and muscles that drive programmed behaviors in living animals.

To begin with, Robinson and his colleagues are putting their faith in a very small animal, the freshwater cnidarian Hydra vulgaris, a tiny tentacled creature that has long been a focus of study in the Robinson and Juliano labs. Because they are small, squishy and transparent, they’re easy to manipulate and measure through Robinson’s custom microfluidic platforms.