The news that Christina Grimmie -- the 22-year-old singer who, as a New Jersey teen, made a name for herself on YouTube before broadening her fame in 2014 on Season 6 of "The Voice" – was shot and killed Friday while signing autographs for fans after a concert in Orlando, Fla., is tragic. But for fans of "The Voice" who watched Grimmie show off, during her time on the show, not only her impressive vocal chops and stage presence, but also her musical creativity, willingness to experiment and upbeat resilience, the loss must be heartbreaking. Those who watched Grimmie turn four chairs during her blind audition and then stick around to finish third on the show, behind only sweet, shy, country-singing runner-up Jake Worthington (of Team Blake Shelton) and silky-soulful winner Josh Kaufman (of Team Usher), knew she was an unusual talent. Grimmie's coach, Adam Levine, believed in her so fiercely that, at one point, he promised the audience she would end up winning the show. Then, when she didn't, he announced that he planned to sign her to his own label.

GE understands solving the world's toughest problems through advanced manufacturing techniques and processes requires collaboration. By crowdsourcing innovation--both internally and externally--GE is improving customer value and driving advancements across industries. By sourcing and supporting innovative ideas, wherever they might come from, and applying GE's scale and expertise, GE's approach to open innovation is helping to address customer needs more efficiently and effectively. GE invites data scientists, analysts, etc. to compete for an opportunity to co-develop a wastewater optimization methodology and algorithm set. This algorithm set will catalyze a site-selection decision support tool-a data science model that melds data elements and key business rules sets into a decisioning criteria.

Uncertainty hinders many interesting applications of planning - it may come in the form of sensor noise, unpredictable environments, or known limitations in problem models. In this paper we explore heuristic guidance for forward-chaining planning with continuous random variables, while ensuring a probability of plan success. We extend the Metric Relaxed Planning Graph heuristic to capture a model of uncertainty, providing better guidance in terms of heuristic estimates and dead-end detection. By tracking the accumulated error on numeric values, our heuristic is able to check if preconditions in the planning graph are achievable with a sufficient degree of confidence; it is also able to consider acting to reduce the accumulated error. Results indicate that our approach offers improvements in performance compared to prior work where a less-informed relaxation was used.

The analytics community has long been discussing whether analytics is about art or science. Analytics is more an art than a science in its ability to form conditions to drive business toward an action that is based on the confidence that the action will improve business performance. This ability to be actionable have recognized recently as the most important aspect in analytics [1]. The concept is known as Prescriptive Analytics [2] shares some similar statements with Actionable Analytics, but some meaningful differences are present as well. Classification rules plays a significant role in practical predictive analytics.

The race is on for artificial intelligence in cybersecurity, empowering computer solutions with the ability to understand threats and respond immediately to them without (or with reduced) human intervention. Will it will survive the hype? A lot depends on how well the cybersecurity industry draws on the lessons we learned in trying to implement artificial intelligence in legal applications. IBM recently announced its foray into the cybersecurity–artificial intelligence arena using its flagship technology Watson. Watson has famously demonstrated its remarkable versatility already; so far it is has won the game show Jeopardy against two former champions and released its own cookbook (admittedly with mixed success from those who have tried the recipes). While it is a remarkable piece of technology, that doesn't necessarily equate to success in cybersecurity.

Vinod Iyengar is Director of Marketing at California-based developer H2O.ai. H2O.ai are the makers behind H2O, the leading open source machine learning platform for smarter applications and data products. They work across a number of mission critical applications, including predictive maintenance, operational intelligence, security, fraud, auditing, credit scoring, user based insurance, ICU monitoring and more in over 5,000 organizations. And with customers including Capital One, PricewaterhouseCoopers, Comcast, Nielsen Catalina Solutions, Macy's and Aetna – to name just a select few – they are clearly in a prominent position in this space. Vinod details some key use cases of artificial intelligence in specific industries, while also sharing H2O.ai's vision for AI and the challenges we face in adopting it… Across industries and business disciplines, businesses use artificial intelligence to increase revenue or reduce costs by performing tasks more efficiently than humans could do unaided. With more than 150 million active digital wallets than 200 billion in annual payments, PayPal leads the online payments industry.

A market basket analysis or recommendation engine [1] is what is behind all these recommendations we get when we go shopping online or whenever we receive targeted advertising. The underlying engine collects information about people's habits and knows that if people buy pasta and wine, they are usually also interested in pasta sauces. So, the next time you go to the supermarket and buy pasta and wine, be ready to get a recommendation for some pasta sauce! A typical analysis goal when applying market basket analysis it to produce a set of association rules in the following form: IF {pasta, wine, garlic} THEN pasta-sauce The first part of the rule is called "antecedent", the second part is called "consequent". A few measures, such as support, confidence, and lift, define how reliable each rule is.

With rapid growth of medical informatics technology, a large number of electronic health records (EHRs) have been available in recent years, including a huge mass of data, such as clinical narratives. They have been being used not only to support computerized clinical systems (e.g., computerized clinical decision support systems [1][2]), but also to help the development of clinical and translational research [3]. One of the challenges to use them is that much information is embedded in clinical notes, but cannot be directly accessible for computerized clinical systems which rely on structured information. Therefore, natural language processing (NLP) technologies, which can extract structured information from narrative text, have received great attention in medical domain [4], and many clinical NLP systems have been developed for different applications [5]. Clinical concept recognition (CCR) as a fundamental task of clinical NLP has also attracted great attention, and a large number of systems have been developed to recognize clinical concepts from various types of clinical notes in last two decades.

Employer wellness programs can gather medical information from employees and spouses -- so long as financial incentives or penalties don't exceed 30 percent of the annual cost for an individual in the company's group health plan, according to final rules issued by the Equal Employment Opportunity Commission Monday. Although such penalties or incentives could run into the hundreds or even thousands of dollars, the programs are considered voluntary -- and therefore legal, the commission said. The rules seek to ensure "wellness programs actually promote good health and are not just used to collect or sell sensitive medical information about employees and family members or to impermissibly shift health insurance costs to them," the EEOC said. But the final rules drew immediate concern from some groups. Jennifer Mathis, director of programs for the Bazelon Center for Mental Health Law, says the new rule rolls back protections in existing law.

Association Rules: How do you put these into practice? I've learned how to create product association rules. What are common ways people use these association rules to make business decisions?