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Google Buys Machine Learning Startup

Acquisition of DNNresearch brings University of Toronto computer scientist Geoffrey Hinton and others to Google.

Google I/O: 10 Awesome Visions
Google I/O: 10 Awesome Visions
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Google has acquired DNNresearch, a machine learning startup founded last year by University of Toronto professor Geoffrey Hinton and two of his graduate students.

Hinton on Tuesday said in a Google+ post that he and his colleagues plan to join Google. "Last summer, I spent several months working with Google's Knowledge team in Mountain View, working with Jeff Dean and an incredible group of scientists and engineers who have a real shot at making spectacular progress in machine learning," he wrote. "Together with two of my recent graduate students, Ilya Sutskever and Alex Krizhevsky (who won the 2012 ImageNet competition), I am betting on Google's team to be the epicenter of future breakthroughs."

No price was disclosed. Google confirmed the acquisition but declined to comment beyond that.

According to the University of Toronto, Hinton will divide his time between his academic research and his work for Google, some of which will be out of Google's Toronto office and some of which will take him to Google's headquarters in Mountain View, Calif.

[ Will he get to meet the zombies? Read Google Vs. Zombies -- And Worse. ]

Google previously awarded a $600,000 grant to Hinton's research group. The company does a significant amount of research on machine learning.

Hinton is widely known for his work developing neural networks, which are used by computers to recognize and understand speech and images, among other tasks.

Online education website Coursera presently offers a course taught by Hinton called Neural Networks for Machine Learning.

In his video introduction to the course, Hinton explains that computers have now gotten fast enough to realize the potential of neural networks, unfulfilled since the initial algorithms were developed several decades ago, in areas like speech and object recognition.

Google and its competitors have become intensely interested in improving ways to employ computers to understand speech and identify images. The shift toward mobile computation applications demands forms of input other than text in many contexts. Google's forthcoming Project Glass, for example, depends on voice input and is designed around image-oriented applications. And its work on products like Google Now underscores the company's need for computational intelligence.

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