As achievements go, learning how to pick up objects doesn’t sound quite as impressive as twice beating the world Go champion – it is, after all, something the average toddler can do. But it’s the fact that the robots themselves figured out the best way to do it using neural networks that makes this notable.
A recent Google report spotted by TNW explains how the company let robot arms pick up a variety of different objects, using neural networks to learn by trial-and-error the best way to handle each. Some 800,000 goes later, the robots seemed to have it figured out pretty well …
Google is at the forefront of machine learning, and has already brought some of its AI-powered technology to apps like Gmail and Search. It’s also keen to get its tools in to the hands of developers and recently made Tensorflow machine open source. As part of that focus on giving developers the resources, it’s also launched the Cloud Vision API, giving devs the ability to build apps (and robots) which recognize objects and facial expressions, then respond to them…
When you upload a video to YouTube, the site processes each frame individually and passes them through an algorithm to try and find a few of the best to be your video’s thumbnail. You get to pick between the ones it chooses, or you can upload your own. And while many YouTube creators opt for the latter option, those who aren’t as dedicated usually just go with whatever frame the site chooses.
Now, thanks to deep neural networks, the algorithm that picks the best frames is much more powerful… Expand Expanding Close
Behind the Mic: The Science of Talking with Computers
Language. Easy for humans to understand (most of the time), but not so easy for computers. This is a short film about speech recognition, language understanding, neural nets, and using our voices to communicate with the technology around us.
Just in case you didn’t get the memo, Google is really big on voice search. The company’s voice command-friendly Google Now tech is available across multiple platforms and according to some recent research, teenagers are crazy about talking to their smartphones, but how does it all work?
Speaking to your mobile devices are starting to become more commonplace, however there’s a lot of behind the scenes work that goes into developing speech recognition.
Google has shown off its winning entry in an annual computer vision challenge whose entrants include both academic institutions and industry labs, and made its work available to other researchers.
In this year’s challenge, team GoogLeNet tasks, doubling the quality on both tasks over last year’s results. The team participated with an open submission, meaning that the exact details of its approach are shared with the wider computer vision community to foster collaboration and accelerate progress in the field …
Google X Laboratory scientists have worked on a simulation of the human brain for the last few years, and now they are using it to indentify cats.
According to The New York Times, Google researchers created “one of the largest neural networks for machine learning by connecting 16,000 computer processors, which they turned loose on the Internet to learn on its own.” More specifically, Google turned the “brain” to 10 million images found in YouTube videos about cats:
The neural network taught itself to recognize cats, which is actually no frivolous activity. This week the researchers will present the results of their work at a conference in Edinburgh, Scotland. The Google scientists and programmers will note that while it is hardly news that the Internet is full of cat videos, the simulation nevertheless surprised them. It performed far better than any previous effort by roughly doubling its accuracy in recognizing objects in a challenging list of 20,000 distinct items.
The research is representative of a new generation of computer science that is exploiting the falling cost of computing and the availability of huge clusters of computers in giant data centers. It is leading to significant advances in areas as diverse as machine vision and perception, speech recognition and language translation.
Google’s brain eventually constructed a digital patchwork of a cat by cropping general features from the millions of images that it identified. The method could eventually prove useful in image search, speech recognition, and language translation. The Googlers maintained caution, however, about whether their research is, as The New York Times put it, “the holy grail of machines that can teach themselves.”
The research project is no longer a part of Google X laboratory, but rather search business and related services.
