That's the simple and iconic slogan Apple used during the second coming of Jobs, during his reinvention and revitalization of the company in the late '90s. Yes, there are second acts in American life. Jobs gave one to Apple, and it's still living it. But the world, of course, keeps changing, and computing changes, and Apple itself may need to think different. This year has been the year of artificial intelligence, natural language processing, machine learning, and computer vision.

Simonite goes on, "A new version of Apple's Photos app, coming this fall with a new version of Apple's mobile operating system, will use facial recognition to maintain virtual albums of snaps containing people you frequently photograph. It will also look at the contents of your photos, so you can search your collection using keywords such as'horses' or'mountains.' Federighi said those features are powered by deep learning, a technique that underpins significant recent progress in artificial intelligence. They are also playing catch-up with Google, which introduced a photos service with those same features over a year ago (see'Google Rolls Out New Automated Helpers'). But Federighi said that Apple didn't want its algorithms to spill data to Apple about the content of user photos. 'When it comes to performing advanced deep learning and intelligence of your data, we're doing it on your device, keeping your personal data under your control,' he said."

Google's Project Soli plays a key role in the magic. Soli is designed for what the project calls "virtual tools," in which a gesture mimics the familiar interaction with a physical tool. In this case, of course, the gesture is the rubbing of thumb and forefinger associated with playing a tiny violin. Another key piece of the World's Tiniest Violin is an open-source machine-learning tool called the Wekinator.

There is such a thing as the "IKEA effect," which, according to one description, suggests that "when individuals construct products themselves, they tend to overvalue their (often mediocre) creations." The "IKEA effect" highlights the importance of "self-agency": when you make something yourself, the work it takes to make that thing gives you a richer sense of initiative and ownership. The result is you get a more positive perception of your creation (even if it's made of particle board). Now two researchers from Pennsylvania State University's Media Effects Research Laboratory want to find out if the same thing applies to robots. The researchers, Yuan Sun and S. Shyam Sundar, say previous studies in human-computer interaction have demonstrated that the "self-agency" effect is present in things as basic as customizing the interface of a software application, resulting in "more positive attitudes toward the technology, a heightened sense of control and identity, greater user engagement, and product attachment."

Sometimes, smaller is better, especially with robots. That's what researchers developing mini-robots have in mind. Robots that cost US 10 to 100 are cheaper to make and more useful to deploy in emergency situations than big robots with limited mobility. Researchers at the University of California, Berkeley, are developing sophisticated robots that are up to 10 centimeters long, and can run, climb, fly, and communicate with emergency personnel. Once the multi-leg robots serve their purpose, they can be disposed of without any regrets.

StrepHit is a Natural Language Processing pipeline that understands human language, extracts facts from text and produces Wikidata statements with references. StrepHit is a IEG project funded by the Wikimedia Foundation. StrepHit will enhance the data quality of Wikidata by suggesting references to validate statements, and will help Wikidata become the gold-standard hub of the Open Data landscape. You can run all the NLP pipeline components through a command line. Do not specify any argument, or use --help to see the available options.

In the following section we introduce the back-propagation learning algorithm and use mini-batching to express the most compute intensive equations in matrix form. Once expressed in matrix form, hardware optimized numerical linear algebra routines are used to achieve an efficient mapping of the algorithm on to the Intel Xeon Phi co-processor. Section 3 describes the preparation of the data used to train the DNN. Section 4 describes the implementation of the deep neural networks. Section 5 then presents results measuring the performance of a DNN. Finally in Section 6, we demonstrate the application of DNNs to backtesting using a walk forward methodology, and provide performance results for a simple buy-hold-sell strategy.

As a Principal Engineer, you'll be responsible for the architecture of a complex analytics platform that is already changing the way large industrial companies manage their assets. A Principal Engineer understands cutting-edge tools and frameworks, and is able to determine what the best tools are for any given task. You will enable and work with our other developers to use cutting-edge technologies in the fields of distributed systems, data ingestion and mapping, and machine learning, to name a few. We also strongly encourage Principal Engineers to tinker with existing tools, and to stay up to date and test new technologies--all with the aim of ensuring that our existing systems don't stagnate or deteriorate.

Anirudh Koul's grandfather was slowly losing his ability to see. By 2014, he was having a hard time recognizing Koul's face in their weekly Skype calls bridging the vast distance between the Silicon Valley, where Koul is a data scientist at Microsoft, and the elderly man's home in New Delhi. So Koul started reading up on the challenges of vision loss and thinking about how the recent advances in deep learning, a potential-packed area of machine learning, could help give people a new way to recognize what's around them without actually seeing it. That was the modest beginning of Seeing AI. Two years later, Microsoft CEO Satya Nadella introduced the budding technology to thundering applause at this year's Build conference.

Elon Musk claims that humans are at risk of becoming the dumb "house pets" of artificial intelligence, unless we implant technology into our brains to help us compete with machine learning of the future. In a public talk and on Twitter last week, Musk announced that a'neural lace' - which is basically a brain implant that can augment natural intelligence by hooking us up to computers - will be the key to maintaining our authority as a species. "I don't love the idea of being a house cat, but what's the solution?" said Musk during a live interview at Recode's Code Conference in California on Wednesday. "I think one of the solutions that seems maybe the best is to add an AI layer. Just as your cortex works symbiotically with your limbic system, your third digital layer could work symbiotically with you."