Everyday objects can run artificial intelligence programs

https://www.science.org/content/article/everyday-objects-can-run-artificial-intelligence-programs

by stichers • 4 years ago

30 4 4 years ago

https://www.nature.com/articles/s41586-021-04223-6

Abstract Deep-learning models have become pervasive tools in science and engineering. However, their energy requirements now increasingly limit their scalability1. Deep-learning accelerators2,3,4,5,6,7,8,9 aim to perform deep learning energy-efficiently, usually targeting the inference phase and often by exploiting physical substrates beyond conventional electronics.

Approaches so far10,11,12,13,14,15,16,17,18,19,20,21,22 have been unable to apply the backpropagation algorithm to train unconventional novel hardware in situ. The advantages of backpropagation have made it the de facto training method for large-scale neural networks, so this deficiency constitutes a major impediment.

Here we introduce a hybrid in situ–in silico algorithm, called physics-aware training, that applies backpropagation to train controllable physical systems. Just as deep learning realizes computations with deep neural networks made from layers of mathematical functions, our approach allows us to train deep physical neural networks made from layers of controllable physical systems, even when the physical layers lack any mathematical isomorphism to conventional artificial neural network layers.

To demonstrate the universality of our approach, we train diverse physical neural networks based on optics, mechanics and electronics to experimentally perform audio and image classification tasks. Physics-aware training combines the scalability of backpropagation with the automatic mitigation of imperfections and noise achievable with in situ algorithms.

Physical neural networks have the potential to perform machine learning faster and more energy-efficiently than conventional electronic processors and, more broadly, can endow physical systems with automatically designed physical functionalities, for example, for robotics23,24,25,26, materials27,28,29 and smart sensors30,31,32.

Flashtoo• 4 years ago

In the context of this paper it is interesting to remember that Rosenblatt's perceptron in the 60s was implemented in hardware with potentiometers for weights, tuned by stepper motors.

https://en.wikipedia.org/wiki/Perceptron#History

malka• 4 years ago

After the internet of shit, the internet of dumb shit.

Awesome.

ovi256• 4 years ago

One day we'll develop some advanced technology like this one and then we'll freak out when we notice it's deployed around us already. A neat SF story idea. The deploying villains that the heroes then have to fight against could be, at your pick, aliens, intelligence agencies (domestic or foreign), secret conspiracies, mega-corporations.

Everyday objects can run artificial intelligence programs

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