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AI can have a black box problem: we can see what goes in them and what comes out, but the deep learning systems that power these models are so complex that even their creators do not understand exactly what happens inside them.

The National Engineering Laboratory for Deep Learning and other state-backed initiatives have helped train thousands of AI specialists, according to Ms Zhang.

"Data-driven deep learning models are on the verge of transforming global weather and climate modeling," the researchers from the University of California San Diego and the Allen Institute for AI, write.

In the long run, the photonic processor could lead to faster and more energy-efficient deep learning for computationally demanding applications like lidar, scientific research in astronomy and particle physics, or high-speed telecommunications.

VAME's deep learning platform is unlike conventional behavioral tests in mice, which often center around preconceived tasks that the animals are challenged to complete.