๐Ÿ”ฅ Explorer Classroom | National Geographic Society

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It is leading to significant advances in areas as diverse as machine vision and perception, speech recognition and language translation. This week the researchers will present the results of their work at a conference in Edinburgh, Scotland.

There Google scientists created one of the largest neural networks for machine learning by connecting 16, computer youtube ng, which they turned loose on the Internet to learn on youtube ng own.

Ng said. The neural network taught itself to recognize cats, which is actually no frivolous activity. What millions of humans do with YouTube: looked for cats. Despite being dwarfed by the immense scale of biological brains, here Google research provides new evidence that existing machine learning algorithms improve greatly as the machines are given access to large pools of data.

However, the research is also striking. The videos were selected randomly and that in itself is an interesting comment on what interests humans in the Internet age. While the scientists https://almarehotel.ru/blackjack/y8-football-league-mod.html struck by the parallel emergence of the cat images, as well as human faces and body parts in specific memory regions of their computer model, Dr.

Dean, who originally helped Google design the software that lets it easily youtube ng programs into many tasks that can be computed simultaneously.

Last year Microsoft scientists presented research showing that the techniques could be applied equally well to build computer systems to understand human speech.

And then, of course, there are the cats. That is because the continue reading neural network created by the researchers appeared to closely mirror theories developed by biologists that suggest individual neurons are trained inside the brain to detect significant objects.

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He noted that one difference was that despite the immense computing capacity that the scientists used, it was still dwarfed by the number of connections found in the brain. The research is representative of a new generation of computer science that is exploiting the falling cost of computing and the availability of huge clusters of computers in giant data centers. Ng said he was cautious about drawing parallels between his software system and biological life. The Google brain assembled a dreamlike digital image of a cat by employing a hierarchy of memory locations to successively cull out general features after being exposed to millions of images. It performed far better than any previous effort by roughly doubling its accuracy in recognizing objects in a challenging list of 20, distinct items. In the Google research, the machine was given no help in identifying features. Bader, executive director of high-performance computing at the Georgia Tech College of Computing. Although some of the computer science ideas that the researchers are using are not new, the sheer scale of the software simulations is leading to learning systems that were not previously possible. Ng and the Google fellow Jeff Dean, used an array of 16, processors to create a neural network with more than one billion connections. Potential applications include improvements to image search, speech recognition and machine language translation. They then fed it random thumbnails of images, one each extracted from 10 million YouTube videos. We probably have other ones that are side views of cats. The Google scientists and programmers will note that while it is hardly news that the Internet is full of cat videos, the simulation nevertheless surprised them. Despite their success, the Google researchers remained cautious about whether they had hit upon the holy grail of machines that can teach themselves.