Abstract--This paper describes an automatic process for combining patterns and features, to guide a search process and reason about it. It is based on the functionality that a human brain might have, which is a highly distributed network of simple neuronal components that can apply some level of matching and cross-referencing over retrieved patterns. The process uses memory in a more dynamic way and it can realise results using a shallow hierarchy, which is a recognised brain-like construct. The paper gives one example of the process, using computer chess as a case study. The second half of the paper then presents a formal language for describing the global pattern sequences and transitions. These pattern ensembles are created from the same techniques that the search and prediction processes require and they define an outer framework that a distributed setup can try to learn. They can also be created automatically, resulting in further functionality for the generic cognitive model.

Over the past years, there has been a resurgence of Datalog-based systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowl\-edge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable. Many efforts have been made to define decidable fragments. Warded Datalog+/- is a very promising one, as it captures PTIME complexity while allowing ontological reasoning. Yet so far, no implementation of Warded Datalog+/- was available. In this paper we present the Vadalog system, a Datalog-based system for performing complex logic reasoning tasks, such as those required in advanced knowledge graphs. The Vadalog system is Oxford's contribution to the VADA research programme, a joint effort of the universities of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the main contribution of this paper, we illustrate the first implementation of Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive termination control strategy. We also provide a comprehensive experimental evaluation.

Bruce Newsome reviews the recently published book: "Strategy, Evolution, and War: From Apes to Artificial Intelligence," authored by Kenneth Payne and published by Georgetown University Press. Artificial intelligence (AI) has been explicit in the practices and policies of defence since at least the 1970s, at least in high-capacity countries, given the exponential growth in the power of electronic computing per unit cost. It was already specified in training and forecasting simulations, decision-making aids, targeting aids, robotics, adaptive navigation systems (as in the Tomahawk Cruise Missile), and ballistic missile defence. Any child with a video game could experience AI. AI raced up Western governmental priorities in the 2000s by application to countering terrorism; in 2009, the US escalated its cyber capabilities and authorities, partly on the promise of AI; in 2014, the Russians seemed to know first what the defenders of Ukraine were doing, in part because of integration of AI; and in 2016, Western governments consensually blamed Russia for unprecedented interference in American and other elections, partly aided by AI.

The majority of the deep learning applications that we see in the community are usually geared towards fields like marketing, sales, finance, etc. We hardly ever read articles or find resources about deep learning being used to protect these products, and the business, from malware and hacker attacks. While the big technology companies like Google, Facebook, Microsoft, and Salesforce have already embedded deep learning into their products, the cybersecurity industry is still playing catch up. It's a challenging field but one that needs our full attention. In this article, we briefly introduce Deep Learning (DL) along with a few existing Information Security (hereby referred to as InfoSec) applications it enables. We then deep dive into the interesting problem of anonymous tor traffic detection and also present a DL-based solution to detect TOR traffic.

It seems everyone is talking about artificial intelligence and machine learning, especially within the fraud prevention sphere. But despite all the buzz, it's not always clear how these intelligent elements actually help curb fraud rates. First things first: though they are often used interchangeably, artificial intelligence (AI) and machine learning (ML) are not the same thing. AI refers to machines that are able to carry out tasks in a human way, while machine learning is a component of AI that involves giving a machine access to large amounts of data and allowing it to learn for itself and solve problems based on that data and patterns the machine recognizes. The concepts of Artificial Intelligence and machine learning have been around since the 1950s. However, only recently have they become a reality for businesses due to advanced developments in the field and newfound affordability.

That's the rough amount of money that counterfeiters displaced last year by selling phony products. Some 2.5% of all trade is for fake goods. The United States is hit hardest by the scourge of counterfeit products -- U.S. brands accounted in 2013 for 20% of the world's infringed intellectual property. When most people think about counterfeiting, they think of knock-off Louis Vuitton handbags sold on the sidewalk. But fake products also include business and enterprise products, as well as everyday consumer goods.

Two Japanese national research institutes have succeeded in using artificial intelligence to identify early stage stomach cancer with a high accuracy rate. The breakthrough may help extend the lives of patients in Japan, where stomach cancer is one of the leading causes of death. According to the National Cancer Center, 45,531 people died of stomach cancer in 2016. According to Riken and the National Cancer Center, it took AI only 0.004 seconds to judge whether an endoscopic image showed early stage cancer or normal stomach tissue. AI correctly detected cancer in 80 percent of cancer images, while the accuracy rate was 95 percent for normal tissue.

The BEAD sensor device analyzes and learns the daily use cycle, energy consumption, user behavior and occupancy changes in all kinds of buildings, and then provides feedback to its automation systems, connecting them to the real-time operation of the building in order to optimize marketing, operations, and energy efficiency. Energy consumption in buildings currently accounts for over 40% of all energy consumed in Europe and the US. This makes for the largest share of the total energy consumption, ahead of transport and industrial production. Europe alone wastes over EUR 43 billion worth of energy in commercial buildings annually. The reason for this is that traditional automation technologies operate on fixed schedules and standard assumptions of occupancy in commercial and residential buildings. But in reality, only about one-third of these assumptions are true.

For banks, the capabilities of artificial intelligence (AI) have always been evident. The only question is when and how to implement them in a way that reaps the best returns. Banks, for instance, have always had large volumes of data, over which it performs monitoring, analysis and insights-generating functions. Yet AI now presents banks with a future worth considering: what will it look like for banks to integrate advanced machine learning capabilities to help with all those processes? AI, with its wide spectrum of technologies could either remain a passing trend today or transform banking for ever.