Los Alamos National Laboratory announced a novel artificial intelligence (AI) methodology capable of reconstructing extensive datasets, like overall ocean temperature, using a limited number of field-deployable sensors through low-powered "edge" computing. This innovation, with applications spanning industry, science, and medicine, broadens the potential for effective data utilization, according to a press release by Los Alamos National Laboratory.
Researcher Javier Santos led the development of an innovative AI technique, Senseiver, which reconstructs vast data fields like ocean temperatures from limited sensor data. The model showcases effectiveness on real-world sparse datasets said the Los Alamos National Laboratory in a press release.
“We developed a neural network that allows us to represent a large system in a very compact way,” said Santos. “That compactness means it requires fewer computing resources compared to state-of-the-art convolutional neural network architectures making it well-suited to field deployment on drones, sensor arrays and other edge-computing applications that put computation closer to its end use."
The work published in Nature Machine Intelligence introduces Senseiver—an AI model that efficiently reconstructs extensive data fields from limited sensor information. The model's practical application on a NOAA sea-surface-temperature dataset demonstrates its utility in areas like global climate modeling said the Los Alamos National Laboratory in another press release.
“Los Alamos has a wide range of remote sensing capabilities but it’s not easy to use AI because models are too big and don’t fit on devices in the field which leads us to edge computing,” said Hari Viswanathan. “Our work brings the benefits of AI to drones networks of field-based sensors and other applications currently beyond the reach of cutting-edge AI technology."