“Evolutionary Leap?” AI is Mimicing the Human Brain –“But Several Orders of Magnitude Faster and More Efficiently”


"It seems plausible that with technology we can, in the fairly near future create (or become) creatures who surpass humans in every intellectual and creative dimension. Events beyond such an event — such a singularity — are as unimaginable to us as opera is to a flatworm,"wrote Vernor Vinge -one of science fiction's greats.

“There must be a better way to do this, because nature has figured out a better way to do this,” says Michael Schneider, a physicist at the US National Institute of Standards and Technology (NIST) in Boulder, Colorado.

Superconducting computing chips modelled after neurons can process information faster and more efficiently than the human brain. The synapses can fire up to one billion times per second — several orders of magnitude faster than human neurons — and use one ten-thousandth of the amount of energy used by a biological synapse.

That achievement is a key benchmark in the development of advanced computing devices designed to mimic biological systems, reports the journal Nature. And it could open the door to more natural machine-learning software, although many hurdles remain before it could be used commercially.


Artificial intelligence software has increasingly begun to imitate the brain. Algorithms such as Google’s automatic image-classification and language-learning programs use networks of artificial neurons to perform complex tasks. But because conventional computer hardware was not designed to run brain-like algorithms, these machine-learning tasks require orders of magnitude more computing power than the human brain does.

NIST is one of a handful of groups trying to develop ‘neuromorphic’ hardware that mimics the human brain in the hope that it will run brain-like software more efficiently. In conventional electronic systems, transistors process information at regular intervals and in precise amounts — either 1 or 0 bits. But neuromorphic devices can accumulate small amounts of information from multiple sources, alter it to produce a different type of signal and fire a burst of electricity only when needed — just as biological neurons do. As a result, neuromorphic devices require less energy to run.

Yet these devices are still inefficient, especially when they transmit information across the gap, or synapse, between transistors. So Schneider’s team created neuron-like electrodes out of niobium superconductors, which conduct electricity without resistance. They filled the gaps between the superconductors with thousands of nanoclusters of magnetic manganese.

By varying the amount of magnetic field in the synapse, the nanoclusters can be aligned to point in different directions. This allows the system to encode information in both the level of electricity and in the direction of magnetism, granting it far greater computing power than other neuromorphic systems without taking up additional physical space.

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