There’s no joke here.
Just some compelling science that will surely develop in the coming years — the wave of the future, perhaps.
For those of you with an aptitude for that kind of stuff —I’m looking at you, FrankJ.
Argonne [National Laboratory] Explores How Ants, Bees, and Fruit Flies Can Be the Next Big Buzz in Artificial Intelligence
Argonne National Laboratory | 12 Sept 2019 | Dave Bukey and Mary Fitzpatrick
Space. The final frontier.
And on Nov. 2, 2018, NASA’s Voyager 2 spacecraft crossed into the vastness of interstellar space, following Voyager 1, which made the leap six years earlier. Since their launch in 1977, the two probes have traveled more than 11 billion miles across the solar system, lasting much longer than scientists anticipated.
Powered by plutonium and drawing 400 watts of power each to run their electronics and heat, the probes still snap photos and send them back to NASA. After 42 years, though, only six of Voyager 2’s 10 instruments still work, and NASA scientists expect the probe will go dark in 2025, well before it leaves our Solar system.
But what if Voyager 2 needed only a couple of watts of power? Could it survive long enough to continue its explorations far into the future?
These are the types of questions that scientists are asking at the U.S. Department of Energy’s (DOE) Argonne National Laboratory. Here, Angel Yanguas-Gil, principal materials scientist in the Applied Materials division, is leading an interdisciplinary team that is rethinking the design of computer chips to not only perform and adapt better, but to do so using a minuscule amount of power — around one watt.
For inspiration, the team is looking to the brains of insects, such as ants, bees, and fruit flies — which offer a new frontier in a type of artificial intelligence known as neuromorphic computing. What they have found could turn artificial intelligence on its artificial head.
Inspired by biology, the team’s newly designed computer chips, which rely on new blueprints and materials, can bypass the “cloud” to learn on the fly, radically conserve power and adapt to extreme environments, such as deep space and radioactive areas — all while delivering reliable, accurate results.
The soft underbelly of artificial intelligence
. . .
How is artificial intelligence inflexible? The answer lies in how a popular form of AI, called a neural network, spots meaningful arrangements in data. Most neural networks, which uncover patterns and relationships in data without explicit programming, are static, designed for a specific task, such as recognizing images. Once a network learns that task, it can’t switch gears and start driving a car.
“The scene changes, the distribution of data is slightly different than before, and what you learned no longer applies,” explained Sandeep Madireddy, a computer scientist in Argonne’s Mathematics and Computer Science (MCS) division, who has joined Yanguas-Gil’s team.
Insects, on the other hand, are versatile and can solve problems in different ways, said Yanguas-Gil.
“In a biological system, the network can learn by itself and offers a much higher degree of flexibility,” he said. “Evolutionary pressure on insects produces very efficient, adaptive computing machines. Bees, for instance, exhibit half the number of distinct cognitive behaviors of dolphins, just in a much smaller volume.”
Accurate under pressure
To prove this point, Yanguas-Gil and Argonne chemists Jeff Elam and Anil Mane designed and simulated a new neuromorphic chip inspired by the tiny brain structure of bees, fruit flies and ants. The team created a network from scratch that contains two pivotal discoveries:
• Dynamic filters and weights that change the strength of various neural connections, depending on what the system finds important in real time.
• Tungsten‐aluminum oxide, an award-winning nanocomposite material created by Elam and Mane, which would allow the chip to operate at power levels far below one watt. (By contrast, graphics processing units [GPUs], based on conventional silicon semiconductor processing, can consume 100 watts or more per chip.)
Testing of the new chip design revealed that it was as accurate as the standard design, but it learned much more quickly and retained its accuracy — even under 60 percent error rates in its internal operation.
“With neural networks, error rates of 20 percent erode the system’s accuracy,” said Yanguas-Gil. “Our system can tolerate much higher error rates and sustain the same accuracy as a perfect system. This makes it a good candidate for machines that spend 30 years in space.”
