As companies increase their digital footprints, 'identify and diagnose' capabilities will not defend against the growing array of security threats, according to analysts at Gartner Group. Because the types of data ingested by analytics packages are evolving from structured to hybrid data–containing text, objects and other formats– the market will respond to that transition by offering packaged applications that utilize more powerful predictive and prescriptive analytics. Machine Learning (ML) and Artificial Intelligence (AI) (I use these terms interchangeably) continue to be hotly debated in security circles. The pessimists believe hackers will always outmaneuver ML, while the believers view AI as an essential companion to finding and displaying threat patterns in a complex, cloud-enhanced IT environment. While both sides have merit, the market itself is moving ahead with real-life ML applications in 2017.

We consider existential rules (aka Datalog+) as a formalism for specifying ontologies. In recent years, many classes of existential rules have been exhibited for which conjunctive query (CQ) entailment is decidable. However, most of these classes cannot express transitivity of binary relations, a frequently used modelling construct. In this paper, we address the issue of whether transitivity can be safely combined with decidable classes of existential rules. First, we prove that transitivity is incompatible with one of the simplest decidable classes, namely aGRD (acyclic graph of rule dependencies), which clarifies the landscape of `finite expansion sets' of rules. Second, we show that transitivity can be safely added to linear rules (a subclass of guarded rules, which generalizes the description logic DL-Lite-R) in the case of atomic CQs, and also for general CQs if we place a minor syntactic restriction on the rule set. This is shown by means of a novel query rewriting algorithm that is specially tailored to handle transitivity rules. Third, for the identified decidable cases, we pinpoint the combined and data complexities of query entailment.

AI in Production – AI is only used by banks in production in a few key use cases such as high frequency trading, fraud detection and credit scoring. In 2016 many machine learning R&D projects started across other business functions. In 2017 banks will move from testing machine learning models to putting models into production to make a real impact on business KPIs. Open-Source AI Platforms – Leading on from the last point, banks will have to consider if the best strategy for operationalising models is to use a major cloud vendor, proprietary tech, open-source tech or in-house build. I think the winning combination is an open-source core machine learning platform supported by in-house R&D higher up the stack, and cloud provider focused mostly on the lower level compute tasks.

Machine learning algorithms, in contrast to regular algorithms, improve their performance after they acquire more experience. The "intelligence" is not hard-coded by the developer, but instead the algorithm learns from the data it receives. A supervised learning task is a task in which the testing data is labeled with both inputs and their desired outputs. These tasks search for patterns between inputs and outputs in test data samples, determine rules based on those patterns, and apply those rules to new input data in order to make predictions on the output. Classification and regression are examples of supervised learning tasks.

From Amazon and Facebook to Google and Microsoft, leaders of the world's most influential technology firms are highlighting their enthusiasm for Artificial Intelligence (AI). While there is growing interest in AI, the field is understood mainly by specialists. Our goal for this primer is to make this important field accessible to a broader audience. We'll begin by explaining the meaning of'AI' and key terms including'machine learning'. We'll illustrate how one of the most productive areas of AI, called'deep learning', works.

Check out these smart home startups. A lot of these use AI behind the scene to get smarter over time.

After three years of almost single-handedly juicing up the slow-growing economy, Janet L. Yellen and the Federal Reserve should be looking at easier days ahead. Yellen, in what will probably be her last full year as Fed chair, may finally get help from somewhere else in Washington. Tax cuts and infrastructure spending planned by President-elect Donald Trump, if backed by the Republican-controlled Congress, would lighten the load for a Fed whose easy-money policies have been the primary economic support for the nation. She is already breathing easier on the Fed's employment mandate; the jobless rate has fallen to a nine-year low of 4.6%. Inflation, too, is under control and, by all accounts, creeping toward the central bank's optimal level of 2%.

The Smart Data Analytics group is very happy to announce SANSA 0.1 – the initial release of the Scalable Semantic Analytics Stack. SANSA combines distributed computing and semantic technologies in order to allow powerful machine learning, inference and querying capabilities for large knowledge graphs. You can find the FAQ and usage examples at http://sansa-stack.net/faq/. We want to thank everyone who helped to create this release, in particular, the projects Big Data Europe, HOBBIT and SAKE.

The piano ditty below, which ascends jauntily, then finishes with a tuneful flourish, sounds a bit like a jingle composed for the latest toothpaste campaign. The tune was, in fact, dreamed up by a musical AI program developed at Google. And the program's latest compositions show how combining a powerful machine-learning approach with simple musical rules can produce creative works that sound remarkably human. Music composition is an enigmatic form of human creativity. Songwriting programs already exist, but they typically follow a specific set of rules, and they tend to produce tunes that feel rigid and mechanical.

From Amazon and Facebook to Google and Microsoft, leaders of the world's most influential technology firms are highlighting their enthusiasm for Artificial Intelligence (AI). While there is growing interest in AI, the field is understood mainly by specialists. Our goal for this primer is to make this important field accessible to a broader audience. We'll begin by explaining the meaning of'AI' and key terms including'machine learning'. We'll illustrate how one of the most productive areas of AI, called'deep learning', works.