What is the meaning of Deep Learning?
Think of deep learning as a subset of a subset. “Artificial intelligence” encompasses a vast range of technologies—like traditional logic- and rules based systems—that enable computers and robots to solve problems in ways that at least superficially resemble thinking.
Deep learning is a "family" of artificial-intelligence techniques. Most scientists still prefer to call them by their original academic designation: deep neural networks, or nets. The most remarkable thing about neural nets is that no human being has programmed a computer to perform any of the stunts it performs. 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( those that possess the deep learning algorithm) can now teach themselves. “You essentially have software writing software,” says Jen-Hsun Huang, the CEO of graphics processing leader Nvidia, which began placing a big bet on deep learning about five years ago. Neural nets aren’t new. The concept dates back to the 1950s, and many of the key algorithmic breakthroughs occurred in the 1980s and ’90s.
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. That dramatic progress has sparked a burst of activity. In the first quarter of 2016, there were 27 acquisitions or funding rounds of A.I. startups, compared with four in the equivalent quarter in 2011, according to the research firm CB Insights. More than $1 billion in investments were made during that stretch, with $600 million coming in the past 18 months.
Google had two deep-learning projects under way 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. In 2011, Microsoft introduced deep-learning technology into its commercial speech-recognition products, according to Lee. Google followed suit in August 2012.