AI-On-A-Chip Quickly Will Make Telephones, Drones And Extra A Lot Smarter

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The department of synthetic intelligence known as deep studying has given us new wonders similar to self-driving automobiles and immediate language translation on our telephones. Now it's about to injects smarts into each different object possible. That's as a result of makers of silicon processors from giants similar to Intel INTC 0.50% Corp. and Qualcomm QCOM -0.45% Applied sciences Inc. in addition to a raft of smaller corporations are beginning to embed deep studying software program into their chips, significantly for cellular imaginative and prescient purposes. In pretty quick order, that's prone to result in a lot smarter telephones, drones, robots, cameras, wearables and extra. "Customers will likely be genuinely amazed on the capabilities of those units," says Cormac Brick, vice chairman of machine studying for Movidius Ltd., a maker of imaginative and prescient processor chips in San Mateo, Calif.


Machine Learning is Going Mobile

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An emerging trend promises to bring the power of machine learning to mobile devices, opening the door to a plethora of valuable new applications. Machine learning--the process by which computers can get better at performing tasks through exposure to data, rather than through explicit programming--requires massive computational power, the kind usually found in clusters of energy-guzzling, cloud-based computer servers outfitted with specialized processors. But recent developments may enable machine learning to be embedded into mobile devices, thus greatly expanding applications for its use and providing new opportunities for marketers. Neural networks--computer models designed to mimic aspects of the human brain's structure and function, with elements representing neurons and their interconnections--are an increasingly popular way of implementing machine learning. They are particularly well suited for performing perceptual tasks such as computer vision and speech recognition.


Machine Learning is Going Mobile - Deloitte CIO - WSJ

#artificialintelligence

An emerging trend promises to bring the power of machine learning to mobile devices, opening the door to a plethora of valuable new applications. Machine learning--the process by which computers can get better at performing tasks through exposure to data, rather than through explicit programming--requires massive computational power, the kind usually found in clusters of energy-guzzling, cloud-based computer servers outfitted with specialized processors. But recent developments may enable machine learning to be embedded into mobile devices, thus greatly expanding applications for its use. Neural networks--computer models designed to mimic aspects of the human brain's structure and function, with elements representing neurons and their interconnections--are an increasingly popular way of implementing machine learning. They are particularly well suited for performing perceptual tasks such as computer vision and speech recognition.


Qualcomm brings big brains to mobile devices with deep-learning tool

PCWorld

Qualcomm has talked about putting "silicon brains" in mobile devices and is now providing tools to train smartphones to recognize people, objects, gestures, and even emotions. Phones like Samsung's Galaxy S7 and LG's G5 that use Qualcomm's Snapdragon 820 chips will get deep-learning capabilities with the Snapdragon Neural Processing Engine software development kit, announced on Monday. The SDK will include a run-time that will exploit chip features so smartphones can accomplish deep-learning tasks like tracking objects and recognizing sounds. The kit could also be used in self-driving cars and autonomous drones and robots. Computers can already recognize people in images, as seen on Facebook.


Google Partners With Chip Startup To Take Machine Learning Out Of The Cloud And Into Your Pocket

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Machine learning has become important to many products -- such as Google photos search and speech recognition. It's a kind of artificial intelligence that that gives computers the ability to learn, make predictions and find patterns. But most of all that complex computing typically needs to take place in the cloud, where the algorithms are being processed through power-intensive clusters of graphics processing units. Now Google wants to break those machine learning capabilities out of the data center and put them directly into devices. On Wednesday, the machine learning group at Google (now a division of Alphabet) announced it would start licensing processors from chip startup Movidius, which makes low-power chips it calls vision processing units (or VPUs).