Deep Learning

A.I. Artificial Intelligence (album)
A.I. Artificial Intelligence (album) (Photo credit: Wikipedia)
"..... deep learning, a relatively new field of artificial intelligence research that aims to achieve tasks like recognizing faces in video or words in human speech ..... "

Is Google Cornering the Market on Deep Learning?
Companies like Google expect deep learning to help them create new types of products that can understand and learn from the images, text, and video clogging the Web..... Not everyone is happy about the arrival of the proverbial Google Bus in one of academia’s rarefied precincts..... a cultural “boundary between academia and Silicon Valley” had been crossed ..... deep learning experts were in such demand that they command the same types of seven-figure salaries as some first-year NFL quarterbacks..... Of the three computer scientists considered among the originators of deep-learning—Hinton, LeCun, and Bengio—only Bengio has so far stayed put in the ivory tower. “I just didn’t think earning 10 times more will make me happier,” he says. “As an academic I can choose what to work on and consider very long-term goals.” ..... in December, DeepMind published a paper showing that its software could do that by learning how to play seven Atari2600 games using as inputs only the information visible on a video screen, such as the score. For three of the games, the classics Breakout, Enduro, and Pong, the computer ended up playing better than an expert human. ..... might be particularly useful in helping robots learn to navigate the human world
Have you sometimes wondered, especially if you are someone who takes, uploads and publicly shares a ton of photos, that maybe noone else is seeing all those photos? What if your thing is video not photo? Then definitely even less people are watching the videos. What if there are important nuggets in them? What if it is a problem that no one is watching your videos?

Deep Learning
Deep-learning software attempts to mimic the activity in layers of neurons in the neocortex, the wrinkly 80 percent of the brain where thinking occurs. The software learns, in a very real sense, to recognize patterns in digital representations of sounds, images, and other data. ..... computer scientists can now model many more layers of virtual neurons than ever before ..... remarkable advances in speech and image recognition. ..... Last June, a Google deep-learning system that had been shown 10 million images from YouTube videos proved almost twice as good as any previous image recognition effort at identifying objects such as cats. Google also used the technology to cut the error rate on speech recognition in its latest Android mobile software. ...... a demonstration of speech software that transcribed his spoken words into English text with an error rate of 7 percent, translated them into Chinese-language text, and then simulated his own voice uttering them in Mandarin. ...... image recognition, search, and natural-language understanding ...... machine intelligence is starting to transform everything from communications and computing to medicine, manufacturing, and transportation. .... “deep learning has reignited some of the grand challenges in artificial intelligence.” ..... software that is familiar with the attributes of, say, an edge or a sound ...... This is much the same way a child learns what a dog is by noticing the details of head shape, behavior, and the like in furry, barking animals that other people call dogs. ...... In 2006, Hinton developed a more efficient way to teach individual layers of neurons. The first layer learns primitive features, like an edge in an image or the tiniest unit of speech sound. It does this by finding combinations of digitized pixels or sound waves that occur more often than they should by chance. Once that layer accurately recognizes those features, they’re fed to the next layer, which trains itself to recognize more complex features, like a corner or a combination of speech sounds. The process is repeated in successive layers until the system can reliably recognize phonemes or objects. ...... At least 80 percent of the recent advances in AI can be attributed to the availability of more computer power ...... Until last year, Google’s Android software used a method that misunderstood many words. But in preparation for a new release of Android last July, Dean and his team helped replace part of the speech system with one based on deep learning. Because the multiple layers of neurons allow for more precise training on the many variants of a sound, the system can recognize scraps of sound more reliably, especially in noisy environments such as subway platforms. Since it’s likelier to understand what was actually uttered, the result it returns is likelier to be accurate as well. Almost overnight, the number of errors fell by up to 25 percent—results so good that many reviewers now deem Android’s voice search smarter than Apple’s more famous Siri voice assistant. ..... Some critics say deep learning and AI in general ignore too much of the brain’s biology in favor of brute-force computing. ....... deep learning fails to account for the concept of time .... human learning depends on our ability to recall sequences of patterns: when you watch a video of a cat doing something funny, it’s the motion that matters, not a series of still images like those Google used in its experiment. “Google’s attitude is: lots of data makes up for everything” ...... deep-learning models can use phoneme data from English to more quickly train systems to recognize the spoken sounds in other languages ....... more sophisticated image recognition could make Google’s self-driving cars much better ....... Kurzweil will tap into the Knowledge Graph, Google’s catalogue of some 700 million topics, locations, people, and more, plus billions of relationships among them. It was introduced last year as a way to provide searchers with answers to their queries, not just links. ....... apply deep-learning algorithms to help computers deal with the “soft boundaries and ambiguities in language.” ..... sensors throughout a city might feed deep-learning systems that could, for instance, predict where traffic jams might occur.
It is possible to imagine a city that has zero traffic jams. If all cars are smart, driverless cars, and all traffic is machine coordinated, it is possible to get rid of traffic jams.
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