{"id":32924,"date":"2019-10-23T03:53:00","date_gmt":"2019-10-23T07:53:00","guid":{"rendered":"http:\/\/blog.cybercon1.com\/?p=32924"},"modified":"2019-10-23T03:53:00","modified_gmt":"2019-10-23T07:53:00","slug":"expanding-the-use-of-ai-on-internet-of-things-hardware","status":"publish","type":"post","link":"https:\/\/blog.cyberconservices.com\/index.php\/2019\/10\/23\/expanding-the-use-of-ai-on-internet-of-things-hardware\/","title":{"rendered":"Expanding the use of AI on internet-of-things hardware"},"content":{"rendered":"\n

If you have a smart phone with facial recognition, you may have wondered: How does your device learn to recognize your face as opposed to, say, your spouse\u2019s face?<\/p>\n\n\n\n

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Credit a neural network, a form of artificial intelligence<\/strong> increasingly used in everyday devices. Neural networks are algorithms trained to recognize patterns and continuously improve their ability to do so \u2014 just as the human brain does.<\/p>\n\n\n\n

In order to be so smart, neural networks require a lot of power, which so far has limited their usefulness in small, battery-powered devices. Have you ever wondered why your smartphone supports face ID but your smartwatch does not? Simply because the watch does not have sufficient power to support it. With a new grant from the National Science Foundation (NSF), University of Delaware Associate Professor of Electrical and Computer Engineering<\/a> Chengmo Yang is researching ways to support neural networks in low-power embedded systems by using emerging memory devices that can retrieve information even when powered off, and furthermore minimize errors in these emerging devices. Read On: <\/a><\/p>\n\n\n\n