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How Reverse-Engineering the Brain Could Help Machines Learn

Artist's impression of what goes on in the human brain.

Thew U.S. Intelligence Advanced Research Projects Activity has launched a new research and development program to reverse-engineer the algorithms human brains use.

Credit: agsandrew/

The research arm of the U.S. intelligence community is interested in improving the way supercomputers and high-end machines learn.

The U.S. Intelligence Advanced Research Projects Activity (IARPA) has announced a new research and development program called Machine Intelligence from Cortical Networks (MICrONS), which seeks to reverse-engineer algorithms brains use. The aim is to "achieve a quantum leap in machine learning that uses neutrally-inspired architectures and mathematical abstractions of the representations, transformations, and learning rules employed by the brain," according to IARPA. The goal is human-like proficiency in processing tasks such as one-shot learning, unsupervised clustering, and scene parsing.

IARPA envisions an algorithm-driven machine that could potentially collect data sets relevant to its mission on its own volition, and the group will accept proposals from companies for the five-year program. Companies are expected to use neuroscience data to improve cortical computation and refine algorithms for smarter machines.

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