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The Faculty of Engineering at McMaster University in Hamilton, Ont., Canada is aiming to build on its ranking as one of the world’s top engineering schools by expanding its recruitment of both tenure-track and teaching track positions across multiple departments. This broad initiative is expected to continue the growth of McMaster as a leading destination for innovative teaching and research.

To support this growth and further develop McMaster Engineering’s longstanding strengths in research, innovation and graduate training, the positions being offered will include two Tier II Canada Research Chair (CRC) and tenure-track positions, with specialization in the fields of micro-nano technology, smart systems, and bio-innovation.

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Image: Audio Analytic

Smartphones for several years now have had the ability to listen non-stop for wake words, like “Hey Siri” and “OK Google,” without excessive battery usage. These wake-up systems run in special, low-power processors embedded within a phone’s larger chip set. They rely on algorithms trained on a neural network to recognize a broad spectrum of voices, accents, and speech patterns. But they only recognize their wake words; more generalized speech recognition algorithms require the involvement of a phone’s more powerful processors.

Today, Qualcomm announced that Snapdragon 8885G, its latest chipset for mobile devices, will be incorporating an extra piece of software in that bit of semiconductor real estate that houses the wake word recognition engine. Created by Cambridge, U.K. startup Audio Analytic, the ai3-nano will use the Snapdragon’s low-power AI processor to listen for sounds beyond speech. Depending on the applications made available by smartphone manufacturers, the phones will be able to react to such sounds as a doorbell, water boiling, a baby’s cry, and fingers tapping on a keyboard—a library of some 50 sounds that is expected to grow to 150 to 200 in the near future.

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