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AI Copernicus 'discovers' that Earth orbits the Sun

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Physicists have designed artificial intelligence that thinks like the astronomer Nicolaus Copernicus by realizing the Sun must be at the centre of the Solar System.Credit: NASA/JPL/SPL Astronomers took centuries to figure it out. But now, a machine-learning algorithm inspired by the brain has worked out that it should place the Sun at the centre of the Solar System, based on how movements of the Sun and Mars appear from Earth. The feat is one the first tests of a technique that researchers hope they can use to discover new laws of physics, and perhaps to reformulate quantum mechanics, by finding patterns in large data sets. The results are due to appear in Physical Review Letters1. Physicist Renato Renner at the Swiss Federal Institute of Technology (ETH) in Zurich and his collaborators wanted to design an algorithm that could distill large data sets down into a few basic formulae, mimicking the way that physicists come up with concise equations like E mc2.


AI Copernicus 'discovers' that Earth orbits the Sun

#artificialintelligence

Astronomers took centuries to figure it out. But now, a machine-learning algorithm inspired by the brain has worked out that it should place the Sun at the centre of the Solar System, based on how movements of the Sun and Mars appear from Earth. The feat is one the first tests of a technique that researchers hope they can use to discover new laws of physics, and perhaps to reformulate quantum mechanics, by finding patterns in large data sets. The results are due to appear in Physical Review Letters 1. Physicist Renato Renner at the Swiss Federal Institute of Technology (ETH) in Zurich and his collaborators wanted to design an algorithm that could distill large data sets down into a few basic formulae, mimicking the way that physicists come up with concise equations like E mc 2. To do this, the researchers had to design a new type of neural network, a machine-learning system inspired by the structure of the brain. Conventional neural networks learn to recognize objects -- such as images or sounds -- by training on huge data sets.


Viewpoint: Physics Insights from Neural Networks

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Machine-learning models based on neural networks are behind many recent technological advances, including high-accuracy translations of text and self-driving cars. They are also increasingly used by researchers to help solve physics problems [1]. Neural networks have identified new phases of matter (see Q&A: A Condensed Matter Theorist Embraces AI) [2], detected interesting outliers in data from high-energy physics experiments [3], and found astronomical objects known as gravitational lenses in maps of the night sky (see Q&A: Paving A Path for AI in Physics Research) [4]. But, while the results obtained by neural networks proliferate, the inner workings of this tool remain elusive, and it is often unclear exactly how the network processes information in order to solve a problem. Now a team at the Swiss Federal Institute of Technology (ETH) in Zurich has demonstrated a way to find this information [5].


When AI Helps With Research, Do AI's Limits Compromise It? - The Wire Science

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Starfleet's star android, Lt. Commander Data, has been enlisted by his renegade android "brother" Lore to join a rebellion against humankind – much to the consternation of Jean-Luc Picard, captain of the USS Enterprise. "The reign of biological life-forms is coming to an end," Lore tells Picard. "You, Picard, and those like you, are obsolete." In real life, the era of smart machines has already arrived. They haven't completely taken over the world yet, but they're off to a good start.


A machine-learning revolution – Physics World

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The groundwork for machine learning was laid down in the middle of last century. When your bank calls to ask about a suspiciously large purchase made on your credit card at a strange time, it's unlikely that a kindly member of staff has personally been combing through your account. Instead, it's more likely that a machine has learned what sort of behaviours to associate with criminal activity – and that it's spotted something unexpected on your statement. Silently and efficiently, the bank's computer has been using algorithms to watch over your account for signs of theft. Monitoring credit cards in this way is an example of "machine learning" – the process by which a computer system, trained on a given set of examples, develops the ability to perform a task flexibly and autonomously.