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."

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Get customer service right, and you've got an advocate for life. Get it wrong and it can damage the perception of your brand and, as a result, your balance sheet. Today's consumers are more refined and demanding than ever. Instant gratification, whether in the form of 24/7 food ordering, taxis or information, has become such a large part of our life that limitations in service delivery, like hold times or untrained staff are causing brands high levels of customer attrition. An estimated two-thirds of customers have left brands due to bad service, so when Mark Zuckerberg addressed his audience at this year's F8 conference, he seemed to provide us all with a glimpse of customer service nirvana in a burst of slick, fast and efficient exchanges.

After almost 10 years of working incognito, former National Aeronautics and Space Administration head Daniel Goldin is finally ready to formally present KnuEdge to the world. KnuEdge is a "neural technology innovation company," an outfit that builds hardware and software based on neural technology, with a main focus on human-machine interaction. While newly revealed publicly, it has been in stealth mode for a decade now, and has already raised 100 million in funding to build its neural chips. The company has revealed its two primary products: KnuVerse, which is a voice authentication technology, and KnuPath, its state-of-the-art neural chip. It has also unveiled Knurld.io, a software development kit with a cloud-based voice recognition and authentication service. Foremost of these offerings is KnuPath.