Machine learning technology will provide the best results in detection of fraud in the future. Indeed, many organizations are actively driving replacement of human-driven rules analysis with machine-driven solutions. However, I believe that a mix of machined-led and human-led activity is the best fit for many organizations to maximize performance. There are several perceptions that suggest fraud rules are no longer fit. Ultimately a data-driven approach, regardless of human or machine involvement, is a state that organizations need to move to in order to maximize detection in the present and to ease the transition to a more machine-driven future.

Although the headline that gets the clicks is "AI is taking our jobs," the current reality is that "Automation is replacing some of our tasks." I know, it's nowhere near as catchy. I say automation rather than AI because it doesn't really matter what technology the underlying system uses, so long as it does the job well. For example, being a bank teller was a human job requiring intelligence, but there's no AI in an ATM cash machine (when it comes to technology evolution, some of them don't even seem to have caught up to Windows Vista yet). I also prefer "tasks" over "jobs" because mostly AI can only do certain elements.

Multilateral solutions remain the best way to address threats to international peace and security. This is true when we talk about the situation in Syria, Libya, Yemen or the Middle East peace process.

Interpretability has always been a major concern for fuzzy rule-based classifiers. The usage of human-readable models allows them to explain the reasoning behind their predictions and decisions. However, when it comes to Big Data classification problems, fuzzy rule-based classifiers have not been able to maintain the good trade-off between accuracy and interpretability that has characterized these techniques in non-Big Data environments. The most accurate methods build too complex models composed of a large number of rules and fuzzy sets, while those approaches focusing on interpretability do not provide state-of-the-art discrimination capabilities. In this paper, we propose a new distributed learning algorithm named CFM-BD to construct accurate and compact fuzzy rule-based classification systems for Big Data. This method has been specifically designed from scratch for Big Data problems and does not adapt or extend any existing algorithm. The proposed learning process consists of three stages: 1) pre-processing based on the probability integral transform theorem; 2) rule induction inspired by CHI-BD and Apriori algorithms; 3) rule selection by means of a global evolutionary optimization. We conducted a complete empirical study to test the performance of our approach in terms of accuracy, complexity, and runtime. The results obtained were compared and contrasted with four state-of-the-art fuzzy classifiers for Big Data (FBDT, FMDT, Chi-Spark-RS, and CHI-BD). According to this study, CFM-BD is able to provide competitive discrimination capabilities using significantly simpler models composed of a few rules of less than 3 antecedents, employing 5 linguistic labels for all variables.

Finding hard instances, which need a long time to solve, of graph problems such as the graph coloring problem and the maximum clique problem, is important for (1) building a good benchmark for evaluating the performance of algorithms, and (2) analyzing the algorithms to accelerate them. The existing methods for generating hard instances rely on parameters or rules that are found by domain experts; however, they are specific to the problem. Hence, it is difficult to generate hard instances for general cases. To address this issue, in this paper, we formulate finding hard instances of graph problems as two equivalent optimization problems. Then, we propose a method to automatically find hard instances by solving the optimization problems. The advantage of the proposed algorithm over the existing rule based approach is that it does not require any task specific knowledge. To the best of our knowledge, this is the first non-trivial method in the literature to automatically find hard instances. Through experiments on various problems, we demonstrate that our proposed method can generate instances that are a few to several orders of magnitude harder than the random based approach in many settings. In particular, our method outperforms rule-based algorithms in the 3-coloring problem.

The Industrial Revolution conjures up images of steam engines, textile mills, and iron workers. This was a defining period during the late 18th and early 19th centuries, as society shifted from primarily agrarian to factory-based work. A second phase of rapid industrialization occurred just before World War I, driven by growth in steel and oil production, and the emergence of electricity. Fast-forward to the 1980s, when digital electronics started having a deep impact on society--the dawning Digital Revolution. Building on that era is what's called the Fourth Industrial Revolution.

It's no secret that marketing today relies heavily on data analytics and data science. Endless applications have been wildly studied and successfully applied in this regard, ranging from customer segmentation and targeting to building recommender systems and predicting churn. In this blogpost, we are going to address yet another interesting application of data science in marketing, which is marketing attribution. Unlike the above examples, marketing attribution unfortunately still lacks a rigorous data-driven approach, and it is largely addressed nowadays through rigid business rules. The content of this blogpost will be very technical at times.

As we make more cashless payments for retail purchases, restaurants, and transportation – not to mention the increase in online shopping – wallets loaded with legal tender may become a thing of the past. According to 2018 research by BigCommerce, software vendor and Square payment processing solution provider, 51 percent of Americans think that online shopping is the best option. Last year, 1.66 billion people worldwide bought goods online. And the number of digital buyers is expected to exceed 2.14 billion. Unfortunately, growing sales may mean not only greater revenue but also bigger losses due to fraud.

Example 1 – Forms Input mode Document characteristics Outcome Output mode Workflow application Data entry Type of content Structured Variance Low Document type Readable/ image PDF Outcome type Extract set of fields Transformation Rules-based and look-ups Private and confidential.

A survey of early work exploring how AI can be used in medicine, with somewhat more technical expositions than in the complementary volume Artificial Intelligence in Medicine."Each chapter is preceded by a brief introduction that outlines our view of its contribution to the field, the reason it was selected for inclusion in this volume, an overview of its content, and a discussion of how the work evolved after the article appeared and how it relates to other chapters in the book.