The South Bend Tribune reports the lawsuit was filed Tuesday in U.S. District Court for Northern Indiana. In addition to Notre Dame's abortion policies, it challenges the Trump administration's interim rules allowing universities to disregard a requirement of the Affordable Care Act that health plans cover birth control for women without out-of-pocket costs.

Marketing automation has driven a lot of hype in the marketing space in recent years, and for good reason. Innovations like Marketo and Eloqua have given companies the ability to scale up their marketing efforts like never before and run multi-channel campaigns that are truly measurable. That said, this is a fast-paced industry – and now, there's a whole new crop of entrants to the martech space that are transforming the way that marketers think about marketing automation. This year, we're seeing a true shift in how marketers think about campaign automation. Instead of designing campaigns aimed to guide prospects through the sales funnel, now companies are looking to design holistic marketing campaigns that aren't just aimed at converting prospects, but also at walking leads through the entire customer lifecycle.

Cognitive applications are being applied to a wide variety of uses and across various industries. Based on statistical and rule-based methods, they are excellent to process a large volume of information. But many companies are battling with the imprecise results this technology delivers. Complex algorithms to simulate how the human brain works lead data scientists to a bottleneck for taking cognitive computing to the next level.

Recent advances in interpretable Machine Learning (iML) and eXplainable AI (XAI) construct explanations based on the importance of features in classification tasks. However, in a high-dimensional feature space this approach may become unfeasible without restraining the set of important features. We propose to utilize the human tendency to ask questions like "Why this output (the fact) instead of that output (the foil)?" to reduce the number of features to those that play a main role in the asked contrast. Our proposed method utilizes locally trained one-versus-all decision trees to identify the disjoint set of rules that causes the tree to classify data points as the foil and not as the fact. In this study we illustrate this approach on three benchmark classification tasks.

A plant is operated on the basis of its manual usually; however, it is not realistic that a manual contains instructions for all cases, especially regarding abnormal ones. For obtaining appropriate operation procedures for a wide variety of cases, multilevel flow modeling (MFM) has been studied ([1]-[3]). MFM is a functional modeling framework, in which a plant structure is expressed as a directed graph. The framework also has a set of influence propagation rules, which consists of if-then rules regarding the states of related components. If the state of a component has changed, the resulting state of the other components can be obtained by applying the rules in the forward direction. Conversely, given a desired state of a component, we can obtain the states of other components to be satisfied for achieving the desired state by tracing back the propagation rules. This leads an action to a desired state. Our contributions are as follows: 1) We propose a method to translate MFM into an FOL. This enables the application of techniques used in the FOL to MFM, such as inference engines and abductive reasoners [6].

BANGKOK (Reuters) - Britain and France should highlight deteriorating human rights in Thailand when its prime minister visits this month and they should make clear there will be no "business as usual" until he holds a fair election, a rights group said on Monday.

I gave a talk at MaRS on this topic. The event was put on by Steve O'Neil and his team, who all did an excellent job. The venue was packed to standing-room only with a fantastic audience of 300-400 people. The goal of the event was to have a discussion around "Rational AI in the Enterprise." I think all of the speakers did a wonderful job of honoring the topic.

Last week's White House summit on artificial intelligence (AI) is an encouraging sign of American government and industry working collaboratively to advance this transformative technology. Defense Secretary Jim Mattis recently told a congressional committee that the Department of Defense (DoD) is "not going to have more papers, we're going to move on [AI]." DoD is broadly pursuing AI, not just as another set of programs, but also as a powerful enabler for nearly every defense mission and function. Strategic competitors are not standing idly by, either, as they reshape their economies to more service-based industries bolstered by technology. The U.S. commercial sector has a sense of urgency in adopting AI in the face of increasing international competition.

The problem with all new hotel technology, unfortunately, is that innovation gets turned into buzzwords quickly, usually in order to sell more products and services. Even worse, all these buzzwords are thrown into the same marketing pot, creating a vast confusion that only "experts" can understand. With this article and accompanying infographic, we will demystify this subject of artificial intelligence (AI) and explain, in plain terms, what it is and what it means for hotels. This guide was created with the intention of clarifying the most important terms and concepts related to artificial intelligence and shedding some light on what AI really means for hotels, without technicalities and by providing easy-to-understand industry examples. Note: This article is also available as a PDF to download here. "What is a lobby boy? A lobby boy is completely invisible, yet always in sight. A lobby boy remembers what people hate. A lobby boy anticipates the client's needs before the needs are needed".

Explaining the behavior of a black box machine learning model at the instance level is useful for building trust. However, it is also important to understand how the model behaves globally. Such an understanding provides insight into both the data on which the model was trained and the patterns that it learned. We present here an approach that learns if-then rules to globally explain the behavior of black box machine learning models that have been used to solve classification problems. The approach works by first extracting conditions that were important at the instance level and then evolving rules through a genetic algorithm with an appropriate fitness function. Collectively, these rules represent the patterns followed by the model for decisioning and are useful for understanding its behavior. We demonstrate the validity and usefulness of the approach by interpreting black box models created using publicly available data sets as well as a private digital marketing data set.