MIT AI Animates Still Photos – Robotics Trends

Researchers from MIT have developing a deep-learning algorithm that, given a still image from a scene, can create a brief video that simulates the future of that scene. Trained on 2 million unlabeled videos that include a year’s worth of footage, the algorithm generated videos that human subjects deemed to be realistic 20 percent more often than a baseline model.

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Whoa, Google’s AI Is Really Good at Pictionary | WIRED

FOR GOOGLE, IT’S not enough that its products rely on machine learning and artificial intelligence. The company also wants you, its customer, to understand how these technologies work.

Last year, a few months after it open sourced its deep learning engine, a Google researcher partnered with The New York Times to create this data visualization explaining neural networks

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Google’s neural network learns to translate languages it hasn’t been trained on • The Register

The gap between human and machine translators could be narrowing as researchers find a new way to improve the learning capabilities of Google Translate’s neural network.

On the same day that Google announced its translation services were now operating with its Neural Machine Translation (NMT) system, a team of researchers released a paper on arXiv showing how its NMT could be pushed one step further.

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Microsoft’s next big AI project? Helping ‘solve’ cancer | ZDNet

Microsoft today launched a number of key cancer-fighting projects it has under way, showing off its application of machine learning to solve bigger challenges than identifying dog breeds.

One such application, called Project Hanover, is seeking to make personalized, precision cancer therapy available to all cancer patients by helping oncologists sift through reams of biomedical research papers faster.

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The AI Revolution: Why Deep Learning Is Suddenly Changing Your Life

Over the past four years, readers have doubtlessly noticed quantum leaps in the quality of a wide range of everyday technologies. Most obviously, the speech-recognition functions on our smartphones work much better than they used to.

In fact, we are increasingly interacting with our computers by just talking to them, whether it’s Amazon’s Alexa, Apple’s Siri, Microsoft’s Cortana, or the many voice-responsive features of Google.

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Generating Faces with Deconvolution Networks

One of my favorite deep learning papers is Learning to Generate Chairs, Tables, and Cars with Convolutional Networks. It’s a very simple concept – you give the network the parameters of the thing you want to draw and it does it – but it yields an incredibly interesting result.

The network seems like it is able to learn concepts about 3D space and the structure of the objects it’s drawing, and because it’s generating images rather than numbers it gives us a better sense about how the network “thinks” as well.

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