Many vendors like Cisco and Intel are proponents of Edge Processing (also called Edge computing). The main idea behind Edge Computing is to push processing away from the core and towards the Edge of the network. For IoT, that means pushing processing towards the sensors or a gateway. This enables data to be initially processed at the Edge device possibly enabling smaller datasets sent to the core. Devices at the Edge may not be continuously connected to the network.

Chris Nicholson co-founded Skymind and Deeplearning4j, the most popular deep-learning framework for Java. Quitting Twitter is easy -- I've done it a hundred times. Someone called it "a clown car that drove into a gold mine," and like all clown cars, Twitter makes the passengers get out once in awhile. If I go back, it's because I'm addicted. For an information junkie, that little bubble is hard to resist.

Redmond today announced the Azure Bot Service, a new service for its Azure cloud platform designed to help developers create intelligent bots for Azure using the Microsoft Bot Framework. The bots will run on Azure Functions, a serverless environment, that allows enterprises to scale their bots as needed. The Azure Bot Service will let enterprises build, connect, deploy, and manage bots that interact naturally with users through an app, Web site, SMS platform, Slack, Facebook Messenger, Skype, and several other popular services. Microsoft said the new service will help companies accelerate intelligent bot development. "You can get started quickly with out-of-the-box templates such as the basic bot, Language Understanding Intelligent Service bot, form bot, and proactive bot," Lili Cheng, an engineer with Microsoft's Artificial Intelligence and Research Group, said on the company's blog.

The most promising aspect of Deep Learning for me is the possibility of removing many of the "magic touch of the analyst" steps of feature extraction, model selection, manual data transformations, etc. that make machine learning models traditionally difficult to generalize. I tried to take a small step towards improvements of this process with my dissertation research by finding features that would generalize between scenes and using those to automatically classify urban areas in images. I used those cross-scene features to provide labels for spectra extracted through an unsupervised process. I'm struck by the fact that deep learning tasks in image recognition take this process back one step further and provide a means to learn those features at multiple orders and levels of abstraction. To see what I'm referring to, see some of the papers on deep convolutional networks, for example:

As video becomes an even more popular way for people to communicate, we want to give everyone state-of-the art creative tools to help you express yourself. We recently began testing a new creative-effect camera in the Facebook app that helps people turn videos into works of art in the moment. That technique is called "style transfer." It takes the artistic qualities of one image style, like the way Van Gogh paintings look, and applies it to other images and videos. It's a technically difficult trick to pull off, normally requiring the content to be sent off to data centers for processing on big-compute servers -- until now.

Google DeepMind has extended its controversial partnership with the Royal Free London NHS Foundation Trust, signing a new five-year deal. The London trust will work with the British machine learning company, which was acquired by Google in 2014, on further developing the Streams clinical app, which has so far used algorithms to detect acute kidney injury. In a statement, Royal Free said that app will be used as a diagnostic support tool for a far wider range of illness, alerting doctors earlier of patients at risk of getting ill. "Like breaking news alerts on a mobile phone, the technology will notify nurses and doctors immediately when test results show a patient is at risk of becoming seriously ill, and provide all the information they need to take action. "Streams will be extended beyond AKI to help care for patients with other serious conditions including sepsis and organ failure." The expanded Streams will alert doctors to patient in need "within seconds", rather than hours, it added. It should also free up doctors from paperwork, creating more than half a million hours of extra direct care, the trust claimed. Royal Free medical director Stephen Powis said: "This is about bringing information to doctors and nurses, much in the way we get news alerts on our phones.

HOW HAS ARTIFICIAL intelligence, associated with hubris and disappointment since its earliest days, suddenly become the hottest field in technology? The term was coined in a research proposal written in 1956 which suggested that significant progress could be made in getting machines to "solve the kinds of problems now reserved for humans…if a carefully selected group of scientists work on it together for a summer". That proved to be wildly overoptimistic, to say the least, and despite occasional bursts of progress, AI became known for promising much more than it could deliver. Researchers mostly ended up avoiding the term, preferring to talk instead about "expert systems" or "neural networks". The rehabilitation of "AI", and the current excitement about the field, can be traced back to 2012 and an online contest called the ImageNet Challenge.

Google says its artificial intelligence has taught itself to'translate between languages that it doesn't even know' 'Zero-shot translation' can translate between languages it doesn't know Deep-learning researchers developed Google Neural Machine Translation GNMT developed algorithm that'self-teaches' it to translate languages'Zero-shot translation' can translate between languages it doesn't know GNMT developed algorithm that'self-teaches' it to translate languages Google headquarters in Menlo Park, California is seen in the above stock photo. Google says it has built an algorithm that allows its Google Translate service to translate languages it doesn't even know Google says that its artificial intelligence uses a'token' at the beginning of the input sentence to specify the required target language to translate to Skating on thin ice: Wife of Vladimir Putin's spokesman... 'This had nothing to do with Donald': Rosie O'Donnell... Liberty University President Jerry Falwell Jr. says ...

Deep learning is impacting everything from healthcare to transportation to manufacturing, and more. Companies are turning to deep learning to solve hard problems, like speech recognition, object recognition, and machine translation. One of the most impressive achievements this year was AlphaGo beating the best Go player in the world. With the victory, Go joins checkers, chess, othello, and Jeopardy as games machines have defeated human at. While beating someone at a board game might not seem useful on the surface, this is a huge deal.

In a major win for artificial intelligence, Google DeepMind's AlphaGo has beat European Go champion Fan Hui in the complex 2,500-year-old Chinese game of Go, touted the official Google blog. A victory in a Go game against a human champion has long been coveted among AI researchers, because the possible moves that a player can take can reach into the quadrillions and beyond. As a result, Go has proven a formidable challenge for artificial intelligence researchers. Microsoft and Facebook, for example, have been working on ways to win in the game over a human champion, but have had no luck to date, according to a BBC news report. Last October, Google DeepMind held a private, closed-door Go match in its London office between its AlphaGo system and Hui.