The premise is simple: Correspondents, editors and producers from our newsroom share the pieces that have kept them reading, using the #NPRreads hashtag. Each weekend, we highlight some of the best stories. The irony was irresistible: The same week NPR went to Greenland to look at high suicide rates, The New York Times Magazine went to Greenland's neighbor, Iceland -- but for a story on high rates of happiness and how that contentedness is partly powered by the country's vulcanic geology. Iceland came in second on a list of world's happiest countries, despite its arctic weather. It has no public plazas or pubs, but it does have public pools, heated geothermically to hot-tub temperatures.

Researchers from MIT's Computer Science and Artificial Laboratory (CSAIL) alongside machine learning-startup PatternEx have created a new cybersecurity defense system that makes use of both unsupervised and supervised learning methods. Human analysts are then presented with the data and given an opportunity to identify actual attacks, which are then fed back into the machine. The system learns and refines its accuracy over time. CSAIL research scientist Kalyan Veeramachaneni, one of AI,2's co-creators, described it this way: "The more attacks the system detects, the more analyst feedback it receives, which, in turn, improves the accuracy of future predictions. That human-machine interaction creates a beautiful, cascading effect."

Justin Sullivan/GettyEatsa is San Francisco's fully automated fast food restaurant where orders appear in a cubby. At San Francisco's first fully automated restaurant, meals appear in little glass cubbies, just 90 seconds after customers order and pay on wall-mounted iPads. It's a human-less experience – no waitstaff, no cashier, no one to get your order wrong and no one to tip. The moment before the meal appears, the see-through display screen that fronts the cubbies goes black for the few seconds when you might catch sight of the hand that feeds you. Eatsa has not yet achieved total automation.

Despite plans to invest in machines and artificial intelligence as part of their strategy to boost productivity, many automotive and industrial equipment companies are failing to implement the measures needed to harness these capabilities, according to a new report from Accenture. The report, "Machine dreams: Making the Most of the Connected Industrial Workforce," is based on interviews with more than 500 business executives in Asia, Europe and the United States involved in setting their company's strategy for the connected industrial workforce. According to the report, manufacturing and production are undergoing rapid change as machines and AI are becoming closely integrated with personnel, creating the connected industrial workforce. By combining mobile, safety and tracking technologies with analytics, companies are enhancing the activities of an industrial worker. The report concludes that the creation of a connected industrial workforce is already part of the business strategy of the majority of automotive and industrial equipment producers, cited by 94 percent of respondents.

In previous articles, we've talked about the merits of artificial intelligence and big data and how these technologies can enable a multitude of industries to begin learning how to do things more effectively. One area where these technologies can be used is in digital marketing. Also referred to as "programmatic marketing", AI and big data can be used to figure out what digital ad to serve you up at any given time to increase the likelihood that you'll click on it. While we've said before that you can't invest in artificial intelligence yet as a retail investor, we did come across one publicly traded company called Rocket Fuel (NASDAQ:FUEL) which is playing in the "programmatic marketing" space and while their value proposition sounds exciting, there's much more to this company than meets the eye. Founded in 2008, Rocket Fuel uses artificial intelligence and big data to determine which ad is best to serve at any given moment in order to increase the likelihood of you clicking on that ad, and then engaging with the advertiser.

I'm often asked about my thoughts on the future. What will transportation be like? What new forms of entertainment will we enjoy? While I have covered these topics in previous articles, it's important to understand that they're all forms of work. So today I want to discuss the future of work -- how technological advancements, namely robotic assistants and tools, as well as tech-enhanced globalization, will affect our daily work flow and the labor market in general.

Human patrols serve as the most direct form of protection of endangered animals, especially in large national parks. However, protection agencies have limited resources for patrols. With support from the National Science Foundation (NSF) and the Army Research Office, researchers are using artificial intelligence (AI) and game theory to solve poaching, illegal logging and other problems worldwide, in collaboration with researchers and conservationists in the U.S., Singapore, Netherlands and Malaysia. "In most parks, ranger patrols are poorly planned, reactive rather than pro-active, and habitual," according to Fei Fang, a Ph.D. candidate in the computer science department at the University of Southern California (USC). Fang is part of an NSF-funded team at USC led by Milind Tambe, professor of computer science and industrial and systems engineering and director of the Teamcore Research Group on Agents and Multiagent Systems.

Recently, there has been rising interest in Bayesian optimization -- the optimization of an unknown function with assumptions usually expressed by a Gaussian Process (GP) prior. We study an optimization strategy that directly uses an estimate of the argmax of the function. This strategy offers both practical and theoretical advantages: no tradeoff parameter needs to be selected, and, moreover, we establish close connections to the popular GP-UCB and GP-PI strategies. Our approach can be understood as automatically and adaptively trading off exploration and exploitation in GP-UCB and GP-PI. We illustrate the effects of this adaptive tuning via bounds on the regret as well as an extensive empirical evaluation on robotics and vision tasks, demonstrating the robustness of this strategy for a range of performance criteria.

This paper addresses an open challenge in educational data mining, i.e., the problem of automatically mapping online courses from different providers (universities, MOOCs, etc.) onto a universal space of concepts, and predicting latent prerequisite dependencies (directed links) among both concepts and courses. We propose a novel approach for inference within and across course-level and concept-level directed graphs. In the training phase, our system projects partially observed course-level prerequisite links onto directed concept-level links; in the testing phase, the induced concept-level links are used to infer the unknown course-level prerequisite links. Whereas courses may be specific to one institution, concepts are shared across different providers. The bi-directional mappings enable our system to perform interlingua-style transfer learning, e.g. treating the concept graph as the interlingua and transferring the prerequisite relations across universities via the interlingua. Experiments on our newly collected datasets of courses from MIT, Caltech, Princeton and CMU show promising results.

Sum-Product Networks (SPNs) are a class of expressive yet tractable hierarchical graphical models. LearnSPN is a structure learning algorithm for SPNs that uses hierarchical co-clustering to simultaneously identifying similar entities and similar features. The original LearnSPN algorithm assumes that all the variables are discrete and there is no missing data. We introduce a practical, simplified version of LearnSPN, MiniSPN, that runs faster and can handle missing data and heterogeneous features common in real applications. We demonstrate the performance of MiniSPN on standard benchmark datasets and on two datasets from Google's Knowledge Graph exhibiting high missingness rates and a mix of discrete and continuous features.