Recent computing advances--fostered by Moore's law and its corollaries, as well as big data and algorithmic advances--have caused AI business applications to mushroom. Many of them also take advantage of recent advances in vision and language by machines. Machine vision, for example, is a core component of robots, drones, and self-driving vehicles, while speech recognition and natural language processing are integral to document processing, chatbots, and translation devices. But until recently, AI was largely relegated to an academic niche. As a result, few seasoned professionals currently work in the field--and still fewer of them understand business processes, such as supply chains, or have experience interacting with business executives.

The vast majority of machines is of highly anonymous nature and, like background music, has crept into our lives almost without anybody noticing. They include refrigerators, sensors, automatic cameras, servers, mainframe computers and Turing machines. To a certain very rudimentary degree, they are already linked although most only among the same kind. It is a preliminary phase of the Internet of Things. Our day-to-day link with them passes through the smart phones.

LogMeIn, the company that provides authentication and other connectivity solutions for those who connect remotely to networks and services, has made another acquisition to expand the products it offers to customers, specifically in its new Bold360 CRM platform, launched in June. The company has picked up Nanorep, a startup out of Israel that develops chatbots and other AI-based tools to help people navigate self-service apps. LogMeIn is paying $45 million plus up to $5 million more in earn-outs based on performance and employees staying put over the next two years. Nanorep had raised just under $7 million from investors that included Titanium out of Russia (which had also backed Cloudyn, recently acquired by Microsoft), Oryzn Capital and OurCrowd. The startup already had around 500 large customers, including big names like FedEx, Toys"R"Us and Vodafone.

Xi Jinping's bookshelf includes not only classics on communism but also works on artificial intelligence, as TV viewers spotted during his new year's speech this year. One of the books that helps the Chinese president understand AI is The Master Algorithm, a 2015 bestseller by Pedro Domingos. In a recent interview with German magazine Der Spiegel, Domingos, who teaches computer science at the University of Washington, said that when he saw his book on Xi's bookshelf, he found it "both exciting and scary." Exciting because China is developing rapidly, and there are all sorts of ways the Chinese and the rest of the world can benefit from AI. Scary because this is an authoritarian government, going full tilt on using AI to control their population. In fact, what we are seeing now is just the beginning.

The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started. In the movie "Her," a man falls in love with his operating system, and romance ensues. In reality, people may or may not expect to build romantic relationships with AI systems in the future. They do, however, expect that humans will one day love and trust AI systems enough to depend on them for their well being.

Italy's population is growing older. Today, more than 22 percent of our citizens are 65-plus. By 2050, experts say that number will reach 40 percent. There will be an immense need for elderly care. But will the healthcare industry be prepared for this demographic shift?

Those who fear the rise of the machines, look away now. In a laboratory in Singapore two robots have mastered a task that roundly defeats humans every weekend: they have successfully assembled an Ikea chair. Engineers at Nanyang Technological University used a 3D camera and two industrial robot arms fitted with grippers and force sensors to take on the challenge of building an £18 "Stefan" chair from the furniture company. Working together, the robots completed the job in 20 minutes and 19 seconds after having the parts placed in front of them. More than half of the time was spent planning moves, with the execution taking nine minutes in total.

Importance-weighting is a popular and well-researched technique for dealing with sample selection bias and covariate shift. It has desirable characteristics such as unbiasedness, consistency and low computational complexity. However, weighting can have a detrimental effect on an estimator as well. In this work, we empirically show that the sampling distribution of an importance-weighted estimator can be skewed. For sample selection bias settings, and for small sample sizes, the importance-weighted risk estimator produces overestimates for datasets in the body of the sampling distribution, i.e. the majority of cases, and large underestimates for data sets in the tail of the sampling distribution. These over- and underestimates of the risk lead to suboptimal regularization parameters when used for importance-weighted validation.

Conjunctive query answering over expressive Horn Description Logic ontologies is a relevant and challenging problem which, in some cases, can be addressed by application of the chase algorithm. In this paper, we define a novel acyclicity notion which provides a sufficient condition for termination of the restricted chase over Horn-SRIQ TBoxes. We show that this notion generalizes most of the existing acyclicity conditions (both theoretically and empirically). Furthermore, this new acyclicity notion gives rise to a very efficient reasoning procedure. We provide evidence for this by providing a materialization based reasoner for acyclic ontologies which outperforms other state-of-the-art systems.

The family of image visibility graphs (IVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such operator in the scenario of image processing and image classification. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters and compressors. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classification tasks.