It was only a few weeks ago that Microsoft announced enhancements heading to Power BI and PowerApps, its no-code business analytics service and web apps design platforms, respectively. But that didn't stop it from unveiling yet another set of features during the Microsoft Business Applications Summit in Atlanta, Georgia this week, where the company took the wraps off a new look for Power BI and improvements in Microsoft Flow, a service which lets users create rule-based workflows that automatically trigger actions, along with improvements in Power BI and PowerApps. "We are getting tremendous feedback and energy from our customers and developers. That feedback helps us develop products that are tailored to their needs," said Microsoft corporate vice president James Phillips. "From there we get to see them innovate and thrive. It's been amazing to see us growing across the board, but there is nothing more rewarding than seeing our customers, partners, and developers in action."

One challenge in fact checking is the ability to improve the transparency of the decision. We present a fact checking method that uses reference information in knowledge graphs (KGs) to assess claims and explain its decisions. KGs contain a formal representation of knowledge with semantic descriptions of entities and their relationships. We exploit such rich semantics to produce interpretable explanations for the fact checking output. As information in a KG is inevitably incomplete, we rely on logical rule discovery and on Web text mining to gather the evidence to assess a given claim. Uncertain rules and facts are turned into logical programs and the checking task is modeled as an inference problem in a probabilistic extension of answer set programs. Experiments show that the probabilistic inference enables the efficient labeling of claims with interpretable explanations, and the quality of the results is higher than state of the art baselines.

In 1965, Herbert Simon predicted that "within twenty years, machines would be capable of doing any work a man can do." Five years later, Marvin Minsky forecasted that "in from three to eight years we would have a machine with the general intelligence of an average human being." Bringing up these predictions is not necessarily meant to emphasize that roughly 50 years later they're still a long way from turning real – in spite of the obvious breakthroughs. It's more about acknowledging the fact that the early founders of AI were overly optimistic about the future of AI. This optimism fueled not only the hype, but also their own dismissal of any criticism – particularly coming from philosophers.

Knowledge graph reasoning, which aims at predicting the missing facts through reasoning with the observed facts, is critical to many applications. Such a problem has been widely explored by traditional logic rule-based approaches and recent knowledge graph embedding methods. A principled logic rule-based approach is the Markov Logic Network (MLN), which is able to leverage domain knowledge with first-order logic and meanwhile handle their uncertainty. However, the inference of MLNs is usually very difficult due to the complicated graph structures. Different from MLNs, knowledge graph embedding methods (e.g. TransE, DistMult) learn effective entity and relation embeddings for reasoning, which are much more effective and efficient. However, they are unable to leverage domain knowledge. In this paper, we propose the probabilistic Logic Neural Network (pLogicNet), which combines the advantages of both methods. A pLogicNet defines the joint distribution of all possible triplets by using a Markov logic network with first-order logic, which can be efficiently optimized with the variational EM algorithm. In the E-step, a knowledge graph embedding model is used for inferring the missing triplets, while in the M-step, the weights of logic rules are updated based on both the observed and predicted triplets. Experiments on multiple knowledge graphs prove the effectiveness of pLogicNet over many competitive baselines.

Business rules work very well to represent the logic for certain domains. They work well because they result intuitive and close to the way of thinking of many types of domain experts. The reason for that it is that they permit to decompose a large problem in single components. In this way, the user does not deal with the orchestration of all the single rules: this is the added value provided by the business rule engine. In this article, we will discuss one specific example of an application written by using business rules. We will write the rules to decide, which email to send to the subscribers to a newsletter.

We introduce the General Video Game Rule Generation problem, and the eponymous software framework which will be used in a new track of the General Video Game AI (GVGAI) competition. The problem is, given a game level as input, to generate the rules of a game that fits that level. This can be seen as the inverse of the General Video Game Level Generation problem. Conceptualizing these two problems as separate helps breaking the very hard problem of generating complete games into smaller, more manageable subproblems. The proposed framework builds on the GVGAI software and thus asks the rule generator for rules defined in the Video Game Description Language. We describe the API, and three different rule generators: a random, a constructive and a search-based generator. Early results indicate that the constructive generator generates playable and somewhat interesting game rules but has a limited expressive range, whereas the search-based generator generates remarkably diverse rulesets, but with an uneven quality.

Obtaining training data for Question Answering (QA) is time-consuming and resource-intensive, and existing QA datasets are only available for limited domains and languages. In this work, we explore to what extent high quality training data is actually required for Extractive QA, and investigate the possibility of unsupervised Extractive QA. We approach this problem by first learning to generate context, question and answer triples in an unsupervised manner, which we then use to synthesize Extractive QA training data automatically. To generate such triples, we first sample random context paragraphs from a large corpus of documents and then random noun phrases or named entity mentions from these paragraphs as answers. Next we convert answers in context to "fill-in-the-blank" cloze questions and finally translate them into natural questions. We propose and compare various unsupervised ways to perform cloze-to-natural question translation, including training an unsupervised NMT model using non-aligned corpora of natural questions and cloze questions as well as a rule-based approach. We find that modern QA models can learn to answer human questions surprisingly well using only synthetic training data. We demonstrate that, without using the SQuAD training data at all, our approach achieves 56.4 F1 on SQuAD v1 (64.5 F1 when the answer is a Named entity mention), outperforming early supervised models.

This work is done as part of a master's thesis project. The increase in the volume of data has given rise to various issues related to the collection, storage, analysis and exploitation of these data in order to create an added value. In this master, we are interested in the search of frequent closed patterns in the transaction bases. One way to process data is to partition the search space into subcontexts, and then explore the subcontexts simultaneously. In this context, we have proposed a new approach for extracting frequent closed itemsets. The main idea is to update frequent closed patterns with their minimal generators by applying a strategy of partitioning of the initial extraction context. Our new approach called UFCIGs-DAC was designed and implemented to perform a search in the test bases. The main originality of this approach is the simultaneous exploration of the research space by the update of the frequent closed patterns and the minimal generators. Moreover, our approach can be adapted to any algorithm of extraction of the frequent closed patterns with their minimal generators.

Smartphones have become the ultimate'personal' computer, yet despite this, general-purpose data mining and knowledge discovery tools for mobile devices are surprisingly rare. DataLearner is a new data mining application designed specifically for Android devices that imports the Weka data mining engine and augments it with algorithms developed by Charles Sturt University. Moreover, DataLearner can be expanded with additional algorithms. Combined, DataLearner delivers 40 classification, clustering and association rule mining algorithms for model training and evaluation without need for cloud computing resources or network connectivity. It provides the same classification accuracy as PCs and laptops, while doing so with acceptable processing speed and consuming negligible battery life. With its ability to provide easy-to-use data mining on a phone-size screen, DataLearner is a new portable, self-contained data mining tool for remote, personalised and educational applications alike. DataLearner features four elements - this paper, the app available on Google Play, the GPL3-licensed source code on GitHub and a short video on YouTube.

The interconnectedness of technology, coupled with the growing number of mobile devices, quickly evolving technologies, and more prominent use of Wi-Fi has resulted in a severe uptick of cyber attacks. To ward off impending threats, we're increasingly turning to machine learning for help. Machine learning has the potential to offer better, more efficient solutions than what's currently available on the market to prevent cybercrime. In this article, we'll give a deep-dive into how machine learning is currently improving cybersecurity. Machine learning can be used to monitor and detect breaches in a certain network, and can also help generate an automated response to an attack.