IBM Research in Zurich has created the world’s first artificial nanoscale stochastic phase-change neurons to store and process data. This demonstration marks a significant step forward in the development of energy-efficient, ultra-dense integrated neuromorphic technologies for applications in cognitive computing.
Neuromorphic chips attempt to model in silicon the massively parallel way the brain processes information as billions of neurons and trillions of synapses respond to sensory inputs such as visual and auditory stimuli. Those neurons also change how they connect with each other in response to changing images, sounds, and the like. That is the process we call learning. The chips, which incorporate brain-inspired models called neural networks, do the same.
Microprocessors configured more like brains than traditional chips could soon make computers far more astute about what’s going on around them.
Recently, Qualcomm announced its new Zeroth machine learning platform and neural processing engine SDK First to accelerate deep learning on the Snapdragon 820 series processor. Zeroth acts a bit like a smart administrator that knows your preferences before you do, and can leverage its own measurements and capabilities to take better photos; switch intelligently between WiFi, Bluetooth, and cellular data depending on which signals are the strongest. Zeroth is also supposedly capable of recognising gestures, expressions, and faces, and intelligently sensing its own surroundings.
The new Samsung Galaxy Note 7 comes with this processor to handle better sensory data and tasks such as image recognition and iris scan.
The Economist writes about how this technology is narrowing the gap between biological brains and electronic ones.
“The next step is to experiment with linking such neurons into networks. Small versions of these networks could be attached to sensors and tuned to detect anything from, say, unusual temperatures in factory machinery, to worrying electrical rhythms in a patient’s heart, to specific types of trade in financial markets. Bigger versions could be baked onto standard computer chips, offering a fast, frugal co-processor designed to excel at pattern-recognition tasks—like speech- or face-recognition—now performed by slower, less efficient software running on standard circuitry.”
The conceptual gap between artificial brains and real ones is shrinking a little further.
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