AI Trained on Images from Cosmological Simulations Surprisingly Successful at Classifying Real Galaxies in Hubble Images (ucsc.edu) 20
A machine learning method which has been widely used in face recognition and other image- and speech-recognition applications, has shown promise in helping astronomers analyze images of galaxies and understand how they form and evolve. From a report: In a new study, accepted for publication in Astrophysical Journal and available online [PDF], researchers used computer simulations of galaxy formation to train a deep learning algorithm, which then proved surprisingly good at analyzing images of galaxies from the Hubble Space Telescope. The researchers used output from the simulations to generate mock images of simulated galaxies as they would look in observations by the Hubble Space Telescope. The mock images were used to train the deep learning system to recognize three key phases of galaxy evolution previously identified in the simulations. The researchers then gave the system a large set of actual Hubble images to classify.
The results showed a remarkable level of consistency in the neural network's classifications of simulated and real galaxies. "We were not expecting it to be all that successful. I'm amazed at how powerful this is," said coauthor Joel Primack, professor emeritus of physics and a member of the Santa Cruz Institute for Particle Physics (SCIPP) at UC Santa Cruz. "We know the simulations have limitations, so we don't want to make too strong a claim. But we don't think this is just a lucky fluke."
The results showed a remarkable level of consistency in the neural network's classifications of simulated and real galaxies. "We were not expecting it to be all that successful. I'm amazed at how powerful this is," said coauthor Joel Primack, professor emeritus of physics and a member of the Santa Cruz Institute for Particle Physics (SCIPP) at UC Santa Cruz. "We know the simulations have limitations, so we don't want to make too strong a claim. But we don't think this is just a lucky fluke."
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Then you should make use of artificial intelligence to sort out what AI cannot excel and work on it.
Nerd-collar jobs killer (Score:1)
Now what are astrophysics interns gonna put on their resume?
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"Worked with AI to more accurately identify galaxy formations from Hubble space data."
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"...and made my three competitor colleagues obsolete."
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Probably something a heck of a lot more valuable than analyzing images and avoiding bias using near impossible criteria.
It isn't surprising a machine could beat a human at a totally mechanistic and inhuman task.
Now those same students can better use their time to analyze large datasets produced by these algorithms and come up with results the algorithms never could... until we find a way to create such an algorithm. Which is something those students can also work on.
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There goes galaxy zoo. (Score:2)
Still, they run a lot of other projects and some of those are almost as much fun.
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Well, you only need to crowdsource the galaxies that can't be solved by AI. (If four out of five slightly different AIs all reach the same conclusion about a galaxy, then they're probably right. You'd need that many to cover all the cases this one can't cope with.)
However, the pool of unsolvable galaxies will be much smaller and much noisier, otherwise the AI would have solved them.
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Well, you only need to crowdsource the galaxies that can't be solved by AI.
Or you can use Boosting [wikipedia.org].
Boosting: Train two or three neural nets to solve the same problem. Then train another NN only on inputs where the others disagree, and use it as a tie-breaker.
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That looks a lot like my screen saver.
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No, that's much worse than that -or else, a tautology in a quite different meaning :
they trained an AI onto artificial images of what they THINK should be what they will see, and then the AI is confirming their bias -of course.
To me this is the apogee of biased training, to date.
(of course if it has been someone training the same NN onto artificially simulated jewish greediness, then showing that actual jews presented to it are found greedy, it'd have been an entirely different thing, isn't it?)
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Wow. My first 'moderated flamebait' in years, for this... I should have replaced 'jews' with 'muslims', I'd have been moderated 'fashion' maybe ;-)
But, back to serious : I really believe what I said : trained with artificial simulations introduces a bias.
Good way to train AI (Score:1)