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01 outubro 2016

Deep Learning: passado, presente e futuro

Excelente reportagem da Fortune sobre o Deep Learning.

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But what most people don’t realize is that all these breakthroughs are, in essence, the same breakthrough. They’ve all been made possible by a family of artificial intelligence (AI) techniques popularly known as deep learning, though most scientists still prefer to call them by their original academic designation: deep neural networks.

The most remarkable thing about neural nets is that no human being has programmed a computer to perform any of the stunts described above. In fact, no human could. Programmers have, rather, fed the computer a learning algorithm, exposed it to terabytes of data—hundreds of thousands of images or years’ worth of speech samples—to train it, and have then allowed the computer to figure out for itself how to recognize the desired objects, words, or sentences.

In short, such computers can now teach themselves. “You essentially have software writing software,” says Jen-Hsun Huang, CEO of graphics processing leader Nvidia NVDA 1.66% , which began placing a massive bet on deep learning about five years ago. (For more, read Fortune’s interview with Nvidia CEO Jen-Hsun Huang.)

Neural nets aren’t new. The concept dates back to the 1950s, and many of the key algorithmic breakthroughs occurred in the 1980s and 1990s. What’s changed is that today computer scientists have finally harnessed both the vast computational power and the enormous storehouses of data—images, video, audio, and text files strewn across the Internet—that, it turns out, are essential to making neural nets work well. “This is deep learning’s Cambrian explosion,” says Frank Chen, a partner at the Andreessen Horowitz venture capital firm, alluding to the geological era when most higher animal species suddenly burst onto the scene.





That dramatic progress has sparked a burst of activity. Equity funding of AI-focused startups reached an all-time high last quarter of more than $1 billion, according to theCB Insights research firm. There were 121 funding rounds for such startups in the second quarter of 2016, compared with 21 in the equivalent quarter of 2011, that group says. More than $7.5 billion in total investments have been made during that stretch—with more than $6 billion of that coming since 2014. (In late September, five corporate AI leaders—Amazon, Facebook, Google, IBM, and Microsoft—formed the nonprofitPartnership on AI to advance public understanding of the subject and conduct research on ethics and best practices.)

Google had two deep-learning projects underway in 2012. Today it is pursuing more than 1,000, according to a spokesperson, in all its major product sectors, including search, Android, Gmail, translation, maps, YouTube, and self-driving cars. IBM’s IBM 0.47% Watson system used AI, but not deep learning, when it beat two Jeopardy champions in 2011. Now, though, almost all of Watson’s 30 component services have been augmented by deep learning, according to Watson CTO Rob High.

Venture capitalists, who didn’t even know what deep learning was five years ago, today are wary of startups that don’t have it. “We’re now living in an age,” Chen observes, “where it’s going to be mandatory for people building sophisticated software applications.” People will soon demand, he says, “ ‘Where’s your natural-language processing version?’ ‘How do I talk to your app? Because I don’t want to have to click through menus.’ ”

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