Fraugster, the young company that uses an Artificial Intelligence (AI) technology to eliminate payment fraud, has raised €4.7 million in funding. The technology learns from each transaction in real-time and can anticipate fraudulent attacks even before they happen. Online merchants lose more than €15 billion to fraudulent transactions every year. Most attempt to tackle this with anti-fraud solutions that are based on outdated technologies. However, older solutions tend to block many sound transactions, leading to false positives that cost the industry over €259 billion in 2015 alone.

In 2004, Charles Lickel was eating in a dinner with some colleagues when he noticed that all of the patrons were rushing to the bar. Curious, he followed them to see what all the commotion was about. As it turned out, they were going to see Ken Jennings' historic six-month run on the game show, Jeopardy! Paul Horn, then director of IBM Research, had been bugging Lickel to come up with an idea for the company's next "grand challenge," Big Blue's tradition of tackling incredibly tough problems just to see if they can be solved. The last one drew wide attention when the firm's Deep Blue computer beat Garry Kasparov at chess in 1996.

Artificial Intelligence Is Already Deep Inside Your Wallet – Here's How Artificial intelligence (AI) is the key for financial service companies and banks to stay ahead of the ever-shifting digital landscape, especially given competition from Google, Apple, Facebook, Amazon and others moving strategically into fintech. AI startups are building data products that not only automate the ingestion of vast amounts of data, but also provide predictive and actionable insights into how people spend and save across digital channels. Financial companies are now the biggest acquirers of such data products, as they can leverage the massive data sets they sit upon to achieve higher profitability and productivity, and operational excellence. Here are the five ways financial service companies are embracing AI today to go even deeper inside your wallet. Your Bank Knows More About You Than Facebook Banks and financial service companies today live or die by their ability to differentiate their offering and meet the unique needs of their customers in real-time.

Business functions such as compliance, which historically rely on rules-based systems, are ripe for the use of AI. This has been a key driver of recent innovations in regulatory technologies (RegTech). Rules-based systems produce large quantities of "noise" – vast amounts of unstructured and mostly irrelevant information which humans need to manually review, creating a considerable mundane burden for even the largest of compliance teams. By automating simple tasks like real-time scanning of changes to Sanctions and Watchlists workloads can be significantly reduced. Smart systems that learn from your decisions can also dramatically reduce the number of'false positives' (incorrect risk alerts) produced by searches, in some cases by as much as 60%.

To solve real-world problems with AI, a deep learning system would need to be trained on a trillion parameters in 20 minutes. Even Intel is willing to admit that computers are great at crunching numbers, but not so great that they also make good decision-makers. Based on a recent webinar about the hardware advancements that have made better artificial intelligence (AI) possible, and what the future holds, that is about to change, and much faster than many would believe. Pradeep Dubey, the director of the Parallel Computing Lab at Intel, explained the difference between traditional AI systems and newer implementations like deep learning--primarily, it's about who is making the rules. In traditional AI, humans have to create rule-based systems for understanding which data should be processed, and how.

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.