Tech Briefs Magazine - June 2024 - 34

Innovative AI Approach Makes the Most of Sparse
Sensor Data
The method uses natural-language models to open AI applications for edge computing.
Los Alamos National Lab, Los Alamos, NM
A
n innovative approach to artificial
intelligence
(AI)
enables
reconstructing
a broad field of data, such as
overall ocean temperature, from a small
number of field-deployable sensors using
low-powered edge computing, with
broad applications across industry, science,
and medicine.
" We developed a neural network that
allows us to represent a large system in
a very compact way, " said Javier Santos,
a Los Alamos National Laboratory researcher.
" 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. "
Santos is first author of a paper published
by a team of Los Alamos researchers
in Nature Machine Intelligence on the novel
AI technique, which they dubbed Senseiver.
The work, which builds on an AI model
called Perceiver IO developed by Google,
applies the techniques of natural-language
models such as ChatGPT to the problem of
reconstructing information about a broad
area - such as the ocean - from relatively
few measurements.
The team realized the model would
have broad application because of its
efficiency. " Using fewer parameters and
less memory requires fewer central processing
unit cycles on the computer, so it
runs faster on smaller computers, " said
Co-Author Dan O'Malley.
In a first in the published literature,
Santos and his Los Alamos colleagues
validated the model by demonstrating its
effectiveness on real-world sets of sparse
data - meaning information taken from
sensors that cover only a tiny portion of
the field of interest - and on complex
three-dimensional-fluids datasets.
In a demonstration of the real-world
utility of the Senseiver, the team applied
the model to a National Oceanic and Atmospheric
Administration sea-surface-tem34
An
innovation in using natural language models brings artificial intelligence to field deployable
sensors, including drones. Los Alamos National Laboratory is exploring the AI technology for locating
and characterizing orphaned oil and gas wells that emit climate-warming methane. (Image:
discover.lanl.gov)
perature dataset. The model was able to
integrate a multitude of measurements
taken over decades from satellites and
sensors on ships. From these sparse point
measurements, the model forecast temperatures
across the entire body of the
ocean, which provides information useful
to global climate models.
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The Senseiver is well-suited to a variety
of projects and research areas of interest
to Los Alamos.
" 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 Co-Author Hari
Tech Briefs, June 2024
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Tech Briefs Magazine - June 2024

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