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AI services like Apple’s Siri and others operate by sending your queries to faraway data centers, which send back responses. The reason they rely on cloud-based computing is that today’s electronics don’t come with enough computing power to run the processing-heavy algorithms needed for machine learning. The typical CPUs most smartphones use could never handle a system like Siri on the device. But Dr. Chris Eliasmith, a theoretical neuroscientist and co-CEO of Canadian AI startup Applied Brain Research, is confident that a new type of chip is about to change that.

For the most part, the AI achievements touted in the media aren’t evidence of great improvements in the field. The AI program from Google that won a Go contest last year was not a refined version of the one from IBM that beat the world’s chess champion in 1997; the car feature that beeps when you stray out of your lane works quite differently than the one that plans your route. Instead, the accomplishments so breathlessly reported are often cobbled together from a grab bag of disparate tools and techniques. It might be easy to mistake the drumbeat of stories about machines besting us at tasks as evidence that these tools are growing ever smarter—but that’s not happening.

Public discourse about AI has become untethered from reality in part because the field doesn’t have a coherent theory. Without such a theory, people can’t gauge progress in the field, and characterizing advances becomes anyone’s guess. As a result the people we hear from the most are those with the loudest voices rather than those with something substantive to say, and press reports about killer robots go largely unchallenged.

I’d suggest that one problem with AI is the name itself—coined more than 50 years ago to describe efforts to program computers to solve problems that required human intelligence or attention. Had artificial intelligence been named something less spooky, it might seem as prosaic as operations research or predictive analytics.

Baxter can read your mind to learn if it’s getting a simple task right or wrong, possibly setting up a foundation for thought-controlled machines.

A prospective footwear concept inspired by SpaceX project of Mars colonization and my personal interest for this planet. PERSONAL PROJECT. 10 days.

At its Cloud Next conference in San Francisco, Google today announced the launch of a new machine learning API for automatically recognizing objects in videos and making them searchable.

The new Video Intelligence API will allow developers to build applications that can automatically extract entities from a video. Until now, most similar image recognition APIs available in the cloud only focused on doing this for still images, but with the help of this new API, developers will be able to build applications that let users search and discover information in videos. That means you can search for “dog” or “flower,” for example.

Besides extracting metadata, the API allows you to tag scene changes in a video.

Continue reading “Google’s new machine learning API recognizes objects in videos” »

In Brief Researchers have found a way to bridge the gap between light and electricity—the two main components of current data transmission. Using the liquid light produced by polaritons, they were able to unite the two, a development that would lead to faster data transmission.

As we reach the smallest units known to physics, it’s becoming more apparent than ever: Moore’s Law can’t hold strong forever. But although it seems we are exhausting the extent to which we can miniaturize processors (as far as we know now), it seems Moore’s Law won’t be scrapped for good…at least not entirely.

Continue reading “Liquid Light: Scientists Unite Light and Electricity to Make Electronics Smaller and Faster” »

The European Council for Nuclear Research (CERN) works to help us better understand what comprises the fabric of our universe. At this French association, engineers and physicists use particle accelerators and detectors to gain insight into the fundamental properties of matter and the laws of nature. Now, CERN scientists may have found an answer to one of the most pressing mysteries in the Standard Model of Physics, and their research can be found in Nature Physics.

According to the Big Bang Theory, the universe began with the production of equal amounts of matter and antimatter. Since matter and antimatter cancel each other out, releasing light as they destroy each other, only a minuscule number of particles (mostly just radiation) should exist in the universe. But, clearly, we have more than just a few particles in our universe. So, what is the missing piece? Why is the amount of matter and the amount of antimatter so unbalanced?

Continue reading “Scientists May Have Solved the Biggest Mystery of the Big Bang” »