Artificial Intelligence Literally Taught Itself How To Do An Experiment, From Start To Finish
Everywhere you turn these days there are more and more automated processes appearing all the time. From automatic vacuum cleaners to self-order counters at restaurants, to cars that automatically park themselves, robots are all around us in one way or another and physics is no different.
Everywhere you turn these days there are more and more automated processes appearing all the time. From automatic vacuum cleaners to self-order counters at restaurants, to cars that automatically park themselves, robots are all around us in one way or another and physics is no different. In using the latest artificial intelligence to do the same tasks as people, we are not only saving time and money but saving on resources too.
A recent physics experiment developed by physicists from The Australian National University (ANU) and the University of New South Wales at the Australian Defence Force Academy (UNSW ADFA) was shown to be completed by artificial intelligence (AI) just as a human would. The test was to create a replica of “Laser Beam” experiment that won the 2001 Nobel Prize and produced an extremely cold gas trapped in a laser beam (known as Bose-Einstein condensate) and the incredible AI literally taught itself how to do the experiment, from start to finish, in under one hour!
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With the success of the experiment, more are set to follow as scientists look to explore the endless possibilities of using AI and the complex conclusions to experiments they can uncover. Because Bose-Einstein condensates are of such a low temperature, artificial intelligence holds the advantage in not being affected by the cold and can even be used to cool the trapped gas down. They are also great navigators and explorers as are sensitive to their surroundings and with the ability to just set themselves up and learn new tasks, it is much more cost efficient than hiring a team of qualified people.
Story Via; Nature group journal Scientific Reports / Animation explains the Bose-Einstein condensate. Produced by the research group Physics Reimagined with the support of labex PALM.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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