It has been around for decades, but AI technologies are only making headway now due to the proliferation of data and the investments being made in storage, tracking and analytics technologies. The second annual report was developed by Narrative Science, a leader in advanced natural language generation (Advanced NLG) for the enterprise. Partnering with National Business Research Institute (NBRI), Narrative Science surveyed the executives to understand the use, value, and impact of AI-powered business applications within their organizations.

In the last several decades, thousands of machine learning algorithms have been developed. Very often, the selection of an algorithm to solve a particular problem is driven more by the data scientist's familiarity with a small subset of available algorithms, than optimizing for predictive power or operational constraints. This is unsurprising: Newcomers to machine learning and veteran data scientists alike, may be overwhelmed by the multitude of machine learning algorithms and where and how it is most appropriate to use them. In this webinar, Daniel Emaasit will introduce Model-Based Machine Learning (MBML), an approach to machine learning which addresses these challenges. Daniel will discuss the various uses of MBML, from tasks such as classification, to regression and clustering, and how it allows data scientists to address the uncretainty inherent to real-world machine learning applications.

As one of the internet's pioneers, eBay's seen it all. And now, it has a plan to stay relevant for years to come and even be "one of the winners," the CEO told CNBC's "Squawk on the Street." "We are planting the seeds right now to ensure that eBay is not only relevant, but a leader -- a disruptor -- in artificial intelligence," CEO Devin Wenig said on Thursday. "Some of the acquisitions we've done are world class teams of both data analysts and engineering capability that are going to allow us to use this incredible data set that we have." Wenig said eBay hopes to build a personalized shopping experience based on data they have on how people browse their site.

The Microsoft Bot Framework provides just what you need to build and connect intelligent bots that interact naturally wherever your users are talking, from text/sms to Skype, Slack, Office 365 mail and other popular services. This is a step-by-step guide which my colleague Peter Daukintis has developed this tutorial which walks you through the development of a Microsoft Bot in C# using the Bot Framework Connector SDK .NET template. You will need to have a Microsoft Band and have collected some sleep data using a Microsoft Band and have had that synchronised up to the Microsoft cloud as this tutorial uses the Bot Framework to provide access to that data. Heart Rate monitoring depends on the current Band mode, It only monitors hr continuously when in exercise mode (along with all other sensors), all of the other scenarios have pre-defined cadences that allow the data to have analytical relevance whilst only using the sensors as much as needed. This is the information that is sent to MS Health, and as such is available through the Cloud API, along with the curated information that is derived by MS Health, e.g sleep efficiency, recovery time, etc.

As the field of "big data" has emerged as a tool for solving all sorts of scientific and societal questions, one of the main challenges that remains is whether, and how, multiple sets of data from various sources could be combined to determine cause-and-effect relationships in new and untested situations. Now, computer scientists from UCLA and Purdue University have devised a theoretical solution to that problem. Their research, which was published this month in the Proceedings of the National Academy of Sciences, could help improve scientists' ability to understand health care, economics, the environment and other areas of study, and to glean much more pertinent insight from data. The study's authors are Judea Pearl, a distinguished professor of computer science at the UCLA Henry Samueli School of Engineering and Applied Science, and Elias Bareinboim, an assistant professor of computer science at Purdue University who earned his doctorate at UCLA. Big data involves using mountains and mountains of information to uncover trends and patterns.

Colin O'Donnell is the chief innovation officer of Intersection. It clogs systems, causes accidents, wastes energy and makes people unhappy. It's more than a bad experience on a website -- in cities, bad user experience (UX) design can actually kill. We're talking about signage, public spaces, civic and emergency communications and other forms of urban design that influence our daily routines and, in some cases, are there expressly for our safety. As more parts of our cities go digital, we have the opportunity to make cities not just safer and more functional, but more human, intuitive and enjoyable with UX that's responsive to the world around us. While bad UX can cause anxiety, confusion and even injury, great user experience design can create efficiencies, keep us safe, get us where we are going faster and turn everyday city drudgery into moments of discovery, surprise and enjoyment.

How many windows were in the house or apartment in which you lived when you were ten? Can you name all 50 states? What was served at your birthday party when you were 13? When you came back from your first trip abroad, how did you describe the experience to your friends? What was the most difficult interaction you ever had with a teacher and what did you learn from that experience?

With the unemployment rate falling to 5.3 percent, the lowest in seven years, policy makers are heaving a sigh of relief. Indeed, with the technology boom in progress, there is a lot to be optimistic about. Manufacturing will be returning to U.S. shores with robots doing the job of Chinese workers; American carmakers will be mass-producing self-driving electric vehicles; technology companies will develop medical devices that greatly improve health and longevity; we will have unlimited clean energy and 3D print our daily needs. The cost of all of these things will plummet and make it possible to provide for the basic needs of every human being. I am talking about technology advances that are happening now, which will bear fruit in the 2020s. But policy makers will have a big new problem to deal with: the disappearance of human jobs. Not only will there be fewer jobs for people doing manual work, the jobs of knowledge workers will also be replaced by computers.

Learning needs to be less like memorization, and more like…Angry Birds. Half of school dropouts name boredom as the number one reason they left. The post is about why the future of education will be about flipping our current model on its head and about how key exponential technologies like AI, VR and gamification are going to drive a revolution in education. In the traditional education system, you start at an "A," and every time you get something wrong, your score gets lower and lower. You start with zero, and every time you come up with something right, your score gets higher and higher. It completely flips the way we currently learn, and it's addictively fun.

We propose a scalable temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots. The model assumes that each user lies in an unobserved latent space and interactions are more likely to form between similar users in the latent space representation. In addition, the model allows each user to gradually move its position in the latent space as the network structure evolves over time. We present a global optimization algorithm to effectively infer the temporal latent space, with a quadratic convergence rate. Two alternative optimization algorithms with local and incremental updates are also proposed, allowing the model to scale to larger networks without compromising prediction accuracy. Empirically, we demonstrate that our model, when evaluated on a number of real-world dynamic networks, significantly outperforms existing approaches for temporal link prediction in terms of both scalability and predictive power.