Google’s DeepMind has trained an artificial intelligence to control a Nuclear Fusion Reactor which is now able to create and maintain a wide range of plasma shapes and advanced configurations.
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Artificial intelligence has been trained how to manage a nuclear fusion reactor by DeepMind, a UK-based division of Alphabet, Google’s parent organization.
DeepMind’s long ambition is to “solve intelligence, developing more general and capable problem-solving systems, known as artificial general intelligence (AGI)” Google bought it in 2014, after it was founded in 2010.
On the endeavor, the scientific discovery firm worked with the Swiss Plasma Center at École Polytechnique Fédérale de Lausanne, a nuclear fusion research center.
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They collaborated to “develop a new magnetic control method for plasmas based on deep reinforcement learning,” which they tested on a real-world plasma for the very first time in the SPC’s TCV tokamak research facility.
TCV is among the few scientific institutions across the globe with a tokamak.
A tokamak is a doughnut-shaped container in which magnetic fields are used to contain and compress plasma in order to initiate the fusion reaction. They are strong contenders for generating long-term electric power.
The first tokamak, known as T-1, was installed in Russia in 1958.
The tokamak’s magnetic coils are high-voltage, which implies they must always be properly managed or the plasma will collide with the vessel walls, causing it to deteriorate.
Scientists at the SPC verify their control system setups on a simulator prior to deploying them in the tokamak to make sure that won’t happen.
“Our simulator is based on more than 20 years of research and is updated continuously,” said Federico Felici, an SPC scientist and co-author of the study. “But even so, lengthy calculations are still needed to determine the right value for each variable in the control system. That’s where our joint research project with DeepMind comes in.”
Researchers used AI or a “controller design that autonomously learns to command the full set of control coils” to address the issues of creating and sustaining high-temperature plasma inside the tokamak container, which would be hotter than the sun’s surface.
DeepMind created an AI algorithm that can build and sustain particular plasma configurations based on the device’s form and location.
The AI system was subsequently taught on the SPC’s simulator, which required it to experiment with a variety of control tactics in order to gain expertise.
“Based on the collected experience, the algorithm generated a control strategy to produce the requested plasma configuration,” researchers explained.
“After being trained, the AI-based system was able to create and maintain a wide range of plasma shapes and advanced configurations, including one where two separate plasmas are maintained simultaneously in the vessel. Finally, the research team tested their new system directly on the tokamak to see how it would perform under real-world conditions.”
The AI-based technology was capable of creating and controlling a wide range of plasma configurations, incorporating elongated, traditional shapes and also sophisticated combinations, like “snowflake” forms.
“Our architecture constitutes an important step forward in terms of generality, in which a single framework is used to solve a broad variety of fusion-control challenges, satisfying several of the key promises of machine learning and artificial intelligence for fusion,” researchers wrote.
Nuclear fusion with magnetic containment, particularly in the tokamak structure, is thought to provide a possible path toward sustainable energy, according to scientists.
Nonetheless, it is unclear whether fusion energy would be commercially viable, though TAE Technologies, a California-based business producing aneutronic fusion power, has stated that commercialization will occur by 2030.