It would be unfeasible to count the number of solar panels by hand in the US, and that makes it hard to measure just how far the tech has actually expanded. Stanford scientists have a solution: tell the AI do the heavy jobs. They have crafted DeepSolar, a deep learning system, which mapped each noticeable solar panel in the US—almost 1.47 Million. The neural network-powered method turns satellite pictures into tiles, segments each pixel inside those tiles, and merges those pixels to verify if there are solar panels in a particular region, if they are individual rooftop installations or large solar farms.
This approach is precise, needs only essential oversight, and (most prominently) is quick. It just took weeks to map the nation where a traditional method may take so long that the info can be years old by the time it is ready. This can assist governments track solar adoption rates, decide on renewable energy plans, or even pinpoint financial differences on the basis of number of panels in a particular neighborhood.
On a related note, researchers at IBM are designing a new computer framework, better integrated to manage elevated data loads from AI (artificial intelligence). The designs, posted in the Journal of Applied Physics, are based on concepts from the human brain and considerably outperform traditional computers in proportional studies.
Today’s devices are developed on the von Neumann framework, designed in the 1940s. Such computing systems sport a central chip that executes a memory unit, logic and arithmetic, input & output devices, and storage. Unlike the stovepipe elements in traditional devices, the scientists claim that brain-inspired devices can have coexisting memory and processing units.
Researcher in the U.S. at IBM (International Business Machines), Abu Sebastian, clarified that executing particular computational errands in the computer’s memory might save energy and elevate the system’s efficiency.
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