Given a number of pairwise preferences of items, a common task is to rank all the items. Examples include pairwise movie ratings, New Yorker cartoon caption contests, and many other consumer preferences tasks. What these settings have in common is two-fold: a scarcity of data (it may be costly to get comparisons for all the pairs of items) and additional feature information about the items (e.g., movie genre, director, and cast). In this paper we modify a popular and well studied method, RankCentrality for rank aggregation to account for few comparisons and that incorporates additional feature information. This method returns meaningful rankings even under scarce comparisons. Using diffusion based methods, we incorporate feature information that outperforms state-of-the-art methods in practice. We also provide improved sample complexity for RankCentrality in a variety of sampling schemes.

This decade has seen us move our notion and application of Artificial Intelligence (AI) forward in dramatic terms. Fuelled by continual advances in computer processing and data analytics, the Machine Learning (ML) that goes to help create AI brains (often called'software agents') has been the subject of much debate in both technical and human cultural circles. As soon as we started to realize that AI and ML are actually going to change our world, we immediately wanted to know how, when, where and by how much. There has been widespread concern over which human jobs would be made redundant by AI; although that furore appears to be abating now that humans understand that AI will more likely take away the'grunt work' and help to create new higher-value jobs. Quite apart from the technical arguments (could hackers use quantum computing and AI to create a computing mega virus, for example), we have also concerned ourselves with the human cultural aspects of AI.

In 2009, the U.S. Army Intelligence and Security Command wanted the ability to track, in real-time, national security threats. Potential solutions had to provide instant results, and use graphics to provide insight into their extremely large streaming datasets. At the time, there was nothing available to meet their needs. Both NoSQL solutions and classical relational systems couldn't handle the scaling requirements. In response, Nima Negahban and Amit Vij, of Kinetica, designed and built a new database.

"Basic analytics are out; machine learning (and beyond) are in," says Kenneth Sanford, U.S. lead analytics architect for collaborative data science platform Dataiku, as he looks back on 2017. Sanford says practical applications of machine learning, deep learning, and AI are "everywhere and out in the open these days," pointing to the "super billboards" in London's Piccadilly Circus that leverage hidden cameras gathering data on foot and road traffic (including the make and model of passing cars) to deliver targeted advertisements. So where will these frameworks and tools take us in 2018? We spoke with a number of IT leaders and industry experts about what to expect in the coming year. AI is already here, whether we recognize it or not.

"Basic analytics are out; machine learning (and beyond) are in," says Kenneth Sanford, U.S. lead analytics architect for collaborative data science platform Dataiku, as he looks back on 2017. Sanford says practical applications of machine learning, deep learning, and AI are "everywhere and out in the open these days," pointing to the "super billboards" in London's Piccadilly Circus that leverage hidden cameras gathering data on foot and road traffic (including the make and model of passing cars) to deliver targeted advertisements. So where will these frameworks and tools take us in 2018? We spoke with a number of IT leaders and industry experts about what to expect in the coming year. AI is already here, whether we recognize it or not.

Artificial intelligence and machine learning, which found solid footing among the hyperscalers and is now expanding into the HPC community, are at the top of the list of new technologies that enterprises want to embrace for all kinds of reasons. But it all boils down to the same problem: Sorting through the increasing amounts of data coming into their environments and finding patterns that will help them to run their businesses more efficiently, to make better businesses decisions, and ultimately to make more money. Enterprises are increasingly experimenting with the various frameworks and tools that are on the market and available as open source software, in both small scale experiments run by a growing number of data scientists who have the expertise to find the valuable information the growing lakes of data and in full blown production deployments that are, conceptually, every bit as sophisticated as what the hyperscalers are deploying. The top cloud service providers and hyperscalers have for several years embrace data-driven AI and machine learning techniques and built their own internal frameworks and platforms that enable them to quickly take advantage of them. But as the technologies begin to cascade into more mainstream enterprises, the complexity of software and systems are throwing roadblocks in front of initiatives aimed at leveraging AI and machine learning for the good of the business.