Google has lent the firm its expertise in the "machine learning" technique in order to help accelerate the timeline for fusion. Nuclear fusion promises a supply of low-carbon energy, using a similar process that powers the Sun. Existing nuclear power is based on nuclear fission, where a heavy chemical element is split to provide a lighter one. Nuclear fusion works by connecting two light elements to make a heavier one. For fusion to become economically feasible, it must first generate more energy than the amount being put in. But none has yet reached this point, despite an eight-decade struggle to "build a star on Earth". The challenges are too much, but some in the fusion community hope that new and innovative thinking and disruptive technologies could help shatter this paradigm. Controlling plasma at tens of millions of degrees requires a finely-tuned system and architecture. Google's expertise in machine learning - where system algorithms improved that have been used for "optimizing" TAE's fusion process. Optimization, or tuning for performance, is carried out, This process once took around two months, but with machine learning, so that we can now optimize in fractions of an afternoon, and the way the rate of learning has accelerated is incredible and efficient and allows us to make changes much faster, and multiple strands of data can be pulled together for a brief understanding of the process, By using a hot and electrically-charged gas called plasma, fast-moving particles can fuse with each other, releasing energy, TAE is doing something quite different from what everyone else is doing in the project. Rather than relying on the heat of the plasma to generate and activate fast-moving particles for the fusion process, the system uses external particle beams which are fired into the hot gas, similar to what happens in a particle accelerator. "That's your fusion source, that was explained, Fusion efforts will use fuel consisting of deuterium and tritium - two heavy versions of the element hydrogen, and this produces energy from fusion at tens of millions of degrees Celsius, which is still at a lower heat than some other options. However, there are downsides: tritium is radioactive, wears down the insides of fusion reactors, and has a finite supply and energy, Although neutrons do produce some radioactivity in the tokamak structure model, it explains, it's also their energy that we capture in "conventional" deuterium-tritium fusion, "If we could do fusion, that was achievable, that would be very interesting, Because they're not performing this through heat, but through particle acceleration, that disadvantage of hydrogen-boron fuel drops away from the system, the net gain through realizing something as a commercial fusion plant is potentially huge and rigid because a vast fraction of the cost of a deuterium-tritium plant is handling the radioactive products for the process, and approach and model is less susceptible to turbulence which hampers the ability and capacity to control plasmas in tokamaks and to energy leaking from the machine during the process, In fact, the leak rate goes down as the heat in the system goes up, As you get more energetic, the behavior gets more manageable, more predictable, more reliable and efficient,
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