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Bringing Artificial Intelligence to the Edge of the IoT


IMEC, Sept. 04, 2018 – 

Today AI is successfully used to recognize faces and traffic signs, diagnose diseases and advance remedies, predict rainfall and consumer preferences, and much more. But doing so requires powerful, energy-guzzling processors. These sit at the heart of the cloud and require sensor data to be transferred all the way from where they are generated. Which is often prohibitive. The solution? Bring AI to the sensors at the edge of the Internet of Things (IoT). To do so, explains Rudi Cartuyvels, Executive VP Smart Electonics and Applications & Business Development at imec, our researchers are working on a pipeline of innovative hardware that instead of hundreds of watts consume less than a watt, or even mere milliwatts.

Machine learning – a successful spinoff of AI

Artificial intelligence (AI) is a term that has kindled the imagination of computer scientists for the last six decades. It was their moniker for the dream to build applications that resembled or even surpassed human intelligence. The field started in the late 1950s and has since gone through several cycles of optimism and doom. Each time the ambition and enthusiasm rose to great heights, to eventually clash with the complexity of the task and the available computational power.

The last years have seen a new upturn. Lately, we've seen computers beat champions of chess and even Go, said to be the most complex game played by humans. We've seen computers dialogue, discuss, and drive cars. So this time around, AI seems to be successful. The reason? The available computational power has risen exponentially to where we are today. And the ambition of general AI has been curbed sufficiently to match that power: computers now learn to recognize patterns in huge, seemingly random datasets. It's called machine learning (ML) and it is no mean feat.

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