Ambient office = 87 nanosieverts per hour

Ambient outside = 120 nanosieverts per hour

Soil exposed to rain water = 120 nanosieverts per hour

Red bell pepper from Central Market = 109 nanosieverts per hour

Tap water = 100 nanosieverts per hour

Filter water = 83 nanosieverts per hour

Ambient office = 102 nanosieverts per hour

Ambient outside = 108 nanosieverts per hour

Soil exposed to rain water = 107 nanosieverts per hour

Pineapple from Central Market = 98 nanosieverts per hour

Tap water = 93 nanosieverts per hour

Filter water = 80 nanosieverts per hour

Ambient office = 96 nanosieverts per hour

Ambient outside = 103 nanosieverts per hour

Soil exposed to rain water = 107 nanosieverts per hour

Iceberg lettuce from Central Market = 112 nanosieverts per hour

Tap water = 95 nanosieverts per hour

Filter water = 84 nanosieverts per hour

Dover sole = 101 nanosieverts per hour

Part 2 of 2 Parts (Please read Part 1 first)
     The DeepMind AI was able to autonomously learn how to create these plasma shapes by manipulating the magnetic coils both in the simulation and in the real tokamak. Fasoli says that this represents a “significant step.” It could influence the design of future tokamaks or even speed up the journey to viable fusion reactors. Yasmin Andrew is a fusion specialist at Imperial College London who is not involved in the DeepMind project. He said, “It will be interesting to see if they can transfer the technology to a larger tokamak.”
      The study of nuclear fusion offered a particular challenge to DeepMind’s scientists because the process is both complex and continuous. Unlike turn-based games like Go and Chess, the state of a plasma changes constantly. To make things even more difficult, plasmas cannot be continuously measured. This is called an “under-observed system.”
      Jonas Buchli is a research scientist at DeepMind. He said, “Sometimes algorithms which are good at these discrete problems struggle with such continuous problems. This was a really big step forward for our algorithm, because we could show that this is doable. And we think this is definitely a very, very complex problem to be solved. It is a different kind of complexity than what you have in games.”
     This is not the first time that researchers have tried to control nuclear fusion with artificial intelligence. Since 2014, Google has been collaborating with fusion company TAE Technologies to apply machine learning to a different type of fusion reactor in order to speed up the analysis of experimental data. Researchers at the Joint European Torus (JET) fusion project in the U.K. have used AI to try to predict the behavior of plasma.
     All in all, the collaboration of the Swiss Plasma Center with DeepMind could prove critical as fusion reactors get bigger. Physicists have a good grasp on how to control the plasma in smaller-scale tokamaks with conventional methods. The challenges will only increase as scientists try to make power-plant-sized versions viable. Progress has been slow but steady. Last week, the JET project made a breakthrough. It set a new record for the amount of energy extracted from a fusion project. Work continues on the ITER project in France. It will be the world’s largest experimental fusion reactor when it goes operational in 2025.
     Dmitri Orlov is an associate research scientist at the Center for Energy Research in San Diego. He said, “The more complex and high performance the tokamak, the greater the need to control more quantities with higher and higher reliability and accuracy. An AI-controlled tokamak could be optimized to control the transfer of heat out of the reaction to the walls of the containment vessel and prevent damaging “plasma instability”. The reactors could be redesigned to take advantage of the tighter control offered by the reinforcement learning.
     Fasoli says that the collaboration with DeepMind could allow fusion researchers to push the boundaries and accelerate the long journey towards fusion power. He said, “AI would enable us to explore things that we wouldn’t explore otherwise, because we can take risks with this kind of control system we wouldn’t dare take otherwise. If we are sure that we have a control system that can take us close to the limit but not beyond the limit, we can actually explore possibilities that wouldn’t otherwise be there for exploring.”

Ambient office = 108 nanosieverts per hour

Ambient outside = 122 nanosieverts per hour

Soil exposed to rain water = 121 nanosieverts per hour

English cucumber from Central Market = 100 nanosieverts per hour

Tap water = 96 nanosieverts per hour

Filter water = 87 nanosieverts per hour

Part 1 of 2 Parts
     Tokamaks are donut-shaped vessels designed to contain a nuclear fusion reaction. They are popular experimental reactors used in many laboratories for nuclear fusion research. Hydrogen atoms are smashed together at enormous temperatures which create a whirling, chaotic plasma that is hotter than the surface of the sun. Many think that fusion power will be the clean energy of future but smart ways to control and confine the plasma will be the key to fusion power.
      Currently, the scientific understanding of fusion is sound so what remains to be done is an engineering challenge. Ambrogio Fasoli is the director of the Swiss Plasma Center at the École Polytechnique Fédérale de Lausanne in Switzerland. He said, “We need to be able to heat this matter up and hold it together for long enough for us to take energy out of it.”
      DeepMind is an artificial intelligence company. It is a subsidiary of Alphabet, the parent company of Google. DeepMind has previously worked on video games and protein folding. It is now working on a joint research project with the Swiss Plasma Center to develop an AI for controlling a nuclear fusion reaction.
     Stars are power by fusion processes. The sheer gravitational mass of stars is sufficient to bring hydrogen atoms together and overcome the repulsion of their positively charged nuclei. On Earth, scientist have to use extremely powerful magnetic coils to confine the nuclear fusion reaction, nudging it into the desired position and shaping it like a potter working clay on a wheel. The coils have to be carefully controlled to prevent plasma from touching the sides of the containment vessel. If the plasma touches the vessel, it can damage the walls and slow down the fusion reaction. There is little risk of an explosion because the fusion reaction cannot survive without magnetic confinement.
     When researchers want to change the configuration of the plasma and experiment with different shapes that may generate more power or a cleaner plasma, a great deal of engineering and design work must be done. Conventional systems are computer-controlled based on models and careful simulation. Fasoli says that such simulations are “complex and not necessarily optimized.”
     DeepMind has developed an AI that can control the shape of the plasma autonomously. A paper recently published in the journal Nature describes how researchers from the two groups taught a deep reinforcement learning system how to control the nineteen magnetic coils inside the TCV which is the variable configuration tokamak at the Swill Plasma Center. This tokamak is used to carry out research that will inform the design of bigger fusion reactors in the future. Martin Riedmiller is the control team lead at DeepMind. He said, “AI, and specifically reinforcement learning, is particularly well suited to the complex problems presented by controlling plasma in a tokamak.”
      Neural networks are a type of AI system designed to imitate the architecture of the human brain. The plasma control AI was initially trained in a simulation. The training started with the AI system observing how changing the settings on each of the nineteen coils affected the shape of plasma inside the vessel. In the next phase, the AI system was given different shapes to try to re-create in the plasma. These shapes included a D-shaped cross section close to what will be used inside ITER (formally known as the International Thermonuclear Experimental Reactor). ITER is the large-scale experimental tokamak under construction in France by a consortium of countries. Also included in the research project was a snowflake configuration that could help dissipate the intense heat of the reaction more evenly around the vessel.
Please read Part 2 next