Researchers show a machine learning network for connected devices

Gigaom

Researchers at Ohio State University have developed a method for building a machine learning algorithm from data gathered from a variety of connected devices. There are two cool things about their model worth noting. The first is that the model is distributed and second, it can keep data private.

The researchers call their model Crowd-ML and the idea is pretty basic. Each device runs a version of a necessary app, much like one might run a version of SETI@home or other distributed computing application, and grabs samples of data to send to a central server. The server can tell when enough of the right data has been gathered to “teach” the computer and only grabs the data it needs, ensuring a relative amount of privacy.

The model uses a variant of stochastic (sub)gradient descent instead of batch processing, to grab data for machine learning, which is what makes the Crowd-ML…

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Don’t Fear Artificial Intelligence

TIME

Stephen Hawking, the pre-eminent physicist, recently warned that artificial intelligence (AI), once it sur­passes human intelligence, could pose a threat to the existence of human civilization. Elon Musk, the pioneer of digital money, private spaceflight and electric cars, has voiced similar concerns.

If AI becomes an existential threat, it won’t be the first one. Humanity was introduced to existential risk when I was a child sitting under my desk during the civil-­defense drills of the 1950s. Since then we have encountered comparable specters, like the possibility of a bioterrorist creating a new virus for which humankind has no defense. Technology has always been a double-edged sword, since fire kept us warm but also burned down our villages.

The typical dystopian futurist movie has one or two individuals or groups fighting for control of “the AI.” Or we see the AI battling the humans for world domination. But this is not…

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