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For all the problems that cause society, one of its greatest advantages is in the scientific world.
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In a new study, which focused on the dynamics of chaotic dust plasma, it was found that properly trained, in fact, it can actually discover new physics.
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By presenting the most detailed description of this type of material, AI corrected long -term theoretical beliefs about how particles behave in dusty plasma.
Artificial intelligence makes the world even more difficult. The generative AI now causes many quantities of “slopes”, and in classes Ai gradually destroys critical thinking skills that are … you know … critical; It does not even mention the role of an unhappy AI, as a ten -to -environment and work destroyer.
Fortunately, some models of artificial intelligence and machine learning (ML) have great ambitions than to tear off Beloved animators and mass -producing essays at the eighth grade reading level. Let’s take, for example, a new ML model designed by a team of researchers at the University of Emory. Usually machine learning algorithms are used as a tool to help scientists sift through a huge amount of data or optimize experiments, but this particular ML model has actually discovered a new physicist self-associated with dusty plasma.
You are probably familiar with plasma – the fourth state of matter that actually makes up 99.9% of all common things in the universe; Dust plasma is just the same mixture of ionized gas, but with charged dust particles. This type of plasma can be found both in space and in the terrestrial environment. E.g. In this new study – published in the magazine Publications of the National Academy of Sciences (PNA)-The team of studies describes how their trained ML model has successfully provided the most detailed description of dusty plasma physics, creating accurate forecasts for unrealistic forces.
“Our AI method is not a black box: we understand how and why it works,” said Justin Burton, co-author of Emory’s investigation. “The system it provides is also versatile. It can be adapted to other body systems to open new discovery routes.”
Simply put, the unrealistic force (as their name shows) occurs when the forces that act between two particles in plasma are not the same. The authors describe this phenomenon as two boats, which are influenced by others – the relaxation position can affect attractive or repulsive particles.
“In dusty plasma, we have described how the main particle attracts the rear particle, but the back particle always repels leading,” said Ily Nemenman, another co-author of Emory’s investigation in a press release. “Some were expecting this phenomenon, but now we have an accurate approximate of it that was not before.”
The ml algorithm also managed to correct some theoretical false thoughts about dusty plasma. For example, scientists thought the particle load was proportional to its size, but the model confirms that although a larger particle has a greater load, it is not proportional because it can also have density and temperature. They also found that the load between the particles not only affects the distance between two particles, but also the particle sizes.
According to the authors, one of the most complex parts of this project was primarily designed by ML algorithm. Usually Ai acquires his ability to feed (or train) data sets – provides a million millions of monkey photos, and it will gradually become a monkey when he sees it. However, when it comes to discovering New Physics, there is not much data on training. So the team had to create a structure that allowed it to work with data did Still give her a latitude to explore unknown physics.
“I am thinking of it as the motto of Star Trek, boldly go where no one has been before,” said Burton. “Properly used, Ai can open the door to brand new spheres to explore.”
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