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New “Super-Turing” AI Chip Mimics the Human Brain to Learn in Real Time — Using Just Nanowatts of Power
A new Super-Turing AI chip mimics the human brain to learn in real time, adapt on the fly, and use ultra-low power in dynamic environments.
Developed by a team of engineers from UCLA, Texas A&M, and several collaborating institutions, the device represents a revolutionary “Super-Turing” AI model, which breaks free from the constraints of conventional computing by mimicking the brain’s ability to adapt on the fly. The research, published in Science Advances, shows that this Super-Turing architecture can outperform traditional artificial neural networks (ANNs) in adaptability and energy efficiency while consuming just 158 nanowatts — a power level seven orders of magnitude lower than typical AI systems. “Traditional AI models rely heavily on backpropagation — a method used to adjust neural networks during training,” co-author and assistant professor of electrical and computer engineering at Texas A&M, Dr. Suin Yi, said in a press release.
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