The script was written by Leo Dorfmanwith art by Curt Swan.
Artificial intelligence is finally getting smart. He told Page, who had read an early draft, that he wanted to start a company to develop his ideas about how to build a truly intelligent computer: It quickly became obvious that such an effort would require nothing less than Google-scale data and computing power.
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. But because of improvements in mathematical formulas and increasingly powerful computers, computer scientists can now model many more layers of virtual neurons than ever before.
With this greater depth, they are producing 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. In October, Microsoft chief research officer Rick Rashid wowed attendees at a lecture in China with 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.
That same month, a team of three graduate students and two professors won a contest held by Merck to identify molecules that could lead to new drugs.
The group used deep learning to zero in on the molecules most likely to bind to their targets. Google in particular has become a magnet for deep learning and related AI talent.
In March the company bought a startup cofounded by Geoffrey Hinton, a University of Toronto computer science professor who was part of the team that won the Merck contest. All this has normally cautious AI researchers hopeful that intelligent machines may finally escape the pages of science fiction.
Indeed, machine intelligence is starting to transform everything from communications and computing to medicine, manufacturing, and transportation. Microsoft has deployed deep learning in its Windows Phone and Bing voice search.
Extending deep learning into applications beyond speech and image recognition will require more conceptual and software breakthroughs, not to mention many more advances in processing power.
One has been to feed computers with information and rules about the world, which required programmers to laboriously write software that is familiar with the attributes of, say, an edge or a sound. That took lots of time and still left the systems unable to deal with ambiguous data; they were limited to narrow, controlled applications such as phone menu systems that ask you to make queries by saying specific words.
Neural networks, developed in the s not long after the dawn of AI research, looked promising because they attempted to simulate the way the brain worked, though in greatly simplified form.
These weights determine how each simulated neuron responds—with a mathematical output between 0 and 1—to a digitized feature such as an edge or a shade of blue in an image, or a particular energy level at one frequency in a phoneme, the individual unit of sound in spoken syllables.
Programmers would train a neural network to detect an object or phoneme by blitzing the network with digitized versions of images containing those objects or sound waves containing those phonemes.
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. But early neural networks could simulate only a very limited number of neurons at once, so they could not recognize patterns of great complexity.
They languished through the s. But the technique still required heavy human involvement: And complex speech or image recognition required more computer power than was then available.
InHinton 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. The process is repeated in successive layers until the system can reliably recognize phonemes or objects.
Last June, Google demonstrated one of the largest neural networks yet, with more than a billion connections.
One simulated neuron in the software model fixated on images of cats. Others focused on human faces, yellow flowers, and other objects. And thanks to the power of deep learning, the system identified these discrete objects even though no humans had ever defined or labeled them. What stunned some AI experts, though, was the magnitude of improvement in image recognition.
That might not sound impressive, but it was 70 percent better than previous methods. And, Dean notes, there were 22, categories to choose from; correctly slotting objects into some of them required, for example, distinguishing between two similar varieties of skate fish.With the help of Mathematical methods of algebraic topology, scientists have fond structures and multidimensional geometric spaces in human brain networks.
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Deep Learning. With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Artificial intelligence is finally getting smart. Propagating electromagnetic waves, showing their inter-digitated electrical (red) and magnetic (blue) components.
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