Harnessing this AI technology still requires a certain expertise—that’s why the giants of the web are buying up all the talent—and thanks to their massive data centers and deep pockets, the Googles of the world can take this technology to places others can’t. But many data scientists are now using single machines—ordinary consumer machines built for gaming—to solve their own problems via deep learning algorithms.
At Kaggle, a site where data scientists compete to solve problems on behalf of other businesses and organizations, deep learning has become one of the tools of choice, and according to Kaggle chief scientist Ben Hamner, single machines have been used to tackle everything from analyzing images and speech recognition to chemoinformatics.
For Richard Socher, a Stanford University researcher who has made extensive use of deep learning in systems that recognize natural language, this is another sign that these AI techniques can trickle down to smaller companies. “It’s very easy to deploy these kinds of models,” Socher says. “Anyone can buy a GPU machine.”
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At Kaggle, a site where data scientists compete to solve problems on behalf of other businesses and organizations, deep learning has become one of the tools of choice, and according to Kaggle chief scientist Ben Hamner, single machines have been used to tackle everything from analyzing images and speech recognition to chemoinformatics.
For Richard Socher, a Stanford University researcher who has made extensive use of deep learning in systems that recognize natural language, this is another sign that these AI techniques can trickle down to smaller companies. “It’s very easy to deploy these kinds of models,” Socher says. “Anyone can buy a GPU machine.”
- More Here
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