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William A. Borders, Ahmed Z. Pervaiz, Shunsuke Fukami, Kerem Y. Camsari, Hideo Ohno & Supriyo Datta https://doi.org/10.1038/s41586-019-1557-9
These folks built a spintronics-based proof of concept for a (classical! room-temperature!) probabilistic computer that efficiently runs a modified adiabatic quantum algorithm for integer factorization! They used a network of 8 probabilistic “p-bits” to factorize numbers up to 945.
Machines that see the world more like humans do | MIT News
Machines that see the world more like humans do | MIT News
Computer vision systems sometimes make inferences about a scene that fly in the face of common sense. For example, if a robot were processing a scene of a dinner table, it might completely ignore a bowl that is visible to any human observer, estimate that a plate is floating above the table, or misperceive a fork to be penetrating a bowl rather than leaning against it. Move that computer vision…
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In the quest to improve the power efficiency for the processor chips in drones etc, DARPA is looking to resurrect the analogue computer in the shape of probabilistic computers.
Probabilistic computing has two basic promises: one is to open the door to low-power, high-performance computing, especially in areas where the answer doesn’t have to be completely perfect — image rendering for example.
That’s what researchers at Rice university hit at earlier this year when they designed a low-power chip that uses probabilistic computing techniques to do energy-efficient, if occasionally inexact, calculations.
Another promise is to build new types of chips that can solve some of the complex data analysis problems that are on the cutting edge of today’s computer science.
“We’re using a few percent of the U.S.’s electricity bill on server farms and we can only do very basic machine-learning,” says Vigoda. “We’re just doing really really simple stuff because we don’t have the compute power to do it. One of the ways to fix this is to design chips that do machine-learning.”
