Star Trek got it right: In the future, we'll use computers by talking to them. Google held an event this week and new hardware got most of the attention. Google unveiled a couple of Google-built Pixel phones, Google Wi-Fi home mesh routers, the Google Home virtual assistant appliance, a new 4k Chromecast Ultra streaming media box and the Daydream View VR headset. Critics say Google is copying and competing directly with Apple with the Pixel phones and with Amazon with Home. But this misses the point.

I speculate/talk about Artificial Intelligence (the way the robots work in the movie i,Robot, Siri in your iPhone, or the AI in the Avengers: Age of Ultron) and a possible scenario which could cause AI to become self-aware and start making self decisions for it's self and us humans. I don't know, I also express my interest in the future of social interactions between humans and machines!

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Star Trek got it right: In the future, we'll use computers by talking to them. Artificial intelligence will open the door to ever-more devastating attacks - but the most effective ones may be the most subtle, Darktrace's Dave Palmer says.China Photos/Getty Images Computational sustainability is a new interdisciplinary research field with the overarching goal of developing computational models, methods, and tools to help manage the balance between environmental, economic, and societal needs for a sustainable future. I will provide examples of computational sustainability problems, ranging from wildlife conservation and biodiversity, to poverty mitigation, to materials discovery for renewable energy materials. Technology # In the AI wars, Google wants to change the world one'Pixel' at a time By ECONOMICTIMES.COM Updated: 8 Oct, 2016, 11:50 hrs IST VIEW IN APP Hardware devices launched by Google are beads of pearl strung together by their A.I. powered technology running on clouds. By Sreeraman Thiagarajan Since Steve Jobs' version of Macintosh the one that was'the computer for the rest of us', to today's iPhone7, hardware devices are the strategy and cloud is a tactic at App...

The "creepy clown crisis" is a case study in the science of the heebie-jeebies. Clowns are creeping across America, lurking in the woods and generally freaking out the general public. Over the past few months, "clown sightings" have become a thing, occupying a dark crevice in the popular imagination that was once filled by UFO or Slender Man sightings. And like all viral trends, this one is spreading, with creepy-clown alerts popping up in England, Australia, Canada, and Scotland. Whether they're teenage pranksters, a movie-marketing ploy, figments of the imagination, or really out to do harm (all possibly true in some cases), the clowns are universally described with one word: creepy.

Have people not been doing this up till now? Seems fairly obvious to me. A similar technique I've used for fun is applying k-NN to each layer to see how well the classes are separated at this layer based on the Euclidean distance (or whatever other metric one wishes to employ). Note that technically k-NN doesn't need any training at all!

Snippets of Lady Gaga's new album Joanne have been shared online after fans "tricked" Amazon's virtual assistant into playing preview clips. Previews can commonly be heard on Amazon when an album is available for pre-order, but they had been disabled for Gaga's highly-anticipated comeback. Owners of Amazon's Echo speaker then discovered they could access songs by issuing the instruction: "Play Joanne by Lady Gaga." The 30-second clips have been recorded and shared online by several fans, with one video showing Amazon's speaker lighting up and responding to the voice commands. Among the leaked songs are Hey Girl, in which Gaga duets with Florence Welch of Florence The Machine over a stomping, staccato piano line reminiscent of Elton Johns's Benny and the Jets.

My data is 64 x 64 images. I have tried the obvious approach of training the whole autoencoder end-to-end, but I could not get good reproductions as I decreased the feature vector length.

This is basically the same setting as semantic segmentation so any architecture that solves this problem can be adapted for your application. You can have a look at papers like U-Net, or Fully Convolutional Neural Networks, whose implementations are available for Caffe (I would also be surprised if there weren't re-implementations in other frameworks like TensorFlow or Theano). On a side note, I am personally using the very naive approach that you are describing (extracting patches around each pixel) and I have found that the performance loss is not that critical for my application. It might still be a good idea for you to try it anyway before switching to more "exotic" architectures.

The pound plummeted in Asian trading early on Friday. And no-one really seems to know why. Did a trader make a mistake? Did the computers go haywire? And where will the pound go next?