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Artificial intelligence systems based on deep learning are changing the electronic devices that surround us.
The results of this deep learning is something seen each time a computer understands our speech, we search for a picture of a friend or we see an appropriately placed ad. But the deep learning itself requires enormous clusters of computers and weeklong runs.
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"Methods developed by our international team will reduce this burden," said Jeffrey Mark Siskind, professor of electrical and computer engineering in Purdue's College of Engineering. "Our methods allow individuals with more modest computers to do the kinds of deep learning that used to require multimillion dollar clusters, and allow programmers to write programs in hours which used to require months."
Deep learning uses a particular kind of calculus at its heart: a clever technique, called automatic differentiation (AD) in the reverse accumulation mode, for efficiently calculating how adjustments to a large number of controls will affect a result.
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"Sophisticated software systems and gigantic computer clusters have been built to perform this particular calculation," said Barak Pearlmutter, professor of computer science at Maynooth University in Ireland, and the other principal of this collaboration. "These systems underlie much of the AI in society: speech recognition, internet search, image understanding, face recognition, machine translation and the placement of advertisements."
One major limitation on these deep learning systems is that they support this particular AD calculation very rigidly.
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"These systems only work on very restricted kinds of computer programs: ones that consume numbers on their input, perform the same numeric operations on them regardless of their values, and output the resulting numbers," Siskind said.
The researchers said another limitation is that the AD operation requires a great deal of computer memory. These restrictions limit the size and sophistication of the deep learning systems that can be built. For example, they make it...
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