Industrial IoT startup Sight Machine raises $5M, expands to robots
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Sight Machine, a startup trying to simplify the collection and analysis of industrial data, has raised a $5 million venture capital round…
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Sight Machine, a startup trying to simplify the collection and analysis of industrial data, has raised a $5 million venture capital round…
Carnegie Mellon University is touting a new $3.6 million research grant from the Defense Advanced Research Projects Agency, or DARPA, to build…
Smart devices, appliances and the internet of things are dominating International CES this week, but we’re probably just getting a small taste of…
Computer vision has seen some major advances over the past couple of years, and a New York-based startup called Dextro wants to take the…
San Francisco-based computer vision startup Fyusion released a new version of its Fyuse app for iOS and Android Wednesday that allows consumers to record…
Virtual reality headsets have all drifted toward a similar ski goggle-like form. But exactly how we interact with them is still evolving.…
The Paul G. Allen Foundation announced on Wednesday that it has awarded $5.7 million in grants to five projects that aim to teach machines to understand what they see and read. That can be anything from a photograph to a chart, a diagram to an entire textbook.
An algorithm can discern a baseline image of just about anything by averaging its features across a collection of photos. Finally, we can answer questions such as what the average internet cat looks like, or whether I really do always look angry. Or perhaps do useful stuff.
Lead researcher Dharmendra Modha described the chip as “a supercomputer the size of a stamp, the weight of a feather.”
Researchers at Carnegie Mellon have created a method called LiveLight that they claim can watch generally uneventful videos and pick out the parts that viewers probably want, or need, to see.
Premise, the company trying to reinvent macroeconomic indicators in developing countries, has raised an $11 million series B round led by Social+Capital…
How to analyze 100 million images for $624 O’Reilly Radar has a useful post from Jetpac CTO Pete Warden on how his…
A new algorithm from University of Toronto researchers can predict the identity of untagged photo subjects by analyzing the relationships of the other people (or things) in the photo.