The Trump administration issued a ruling that expands the entities which can exempt themselves from the contraception mandate in the Affordable Care Act. A federal judge in Boston Monday threw out a lawsuit by Massachusetts' attorney general that attempted to block the Trump administration's rules expanding exemptions from ObamaCare's birth control mandate. U.S. District Judge Nathaniel Gorton said Massachusetts lacked standing to sue and noted that "the record is uniquely obscure" regarding whether employers in the state would take advantage of the exemptions. In a statement, Massachusetts Attorney General Maura Healey said that she was disappointed in the decision but remained committed to ensuring "affordable and reliable reproductive health care for women." ObamaCare originally required most companies to cover birth control at no additional cost, though it included exemptions for religious organizations.

A federal judge has tossed the Massachusetts attorney general's lawsuit against President Donald Trump's administration over rules allowing more employers to opt out of providing no-cost birth control to women.

BOSTON (Reuters) - A federal judge on Monday rejected a lawsuit by Massachusetts' attorney general challenging new rules by President Donald Trump's administration that make it easier for employers to avoid providing insurance that covers women's birth control.

Neural networks can answer a question about a photo and point to the evidence for their answer by annotating the image. How it works: To test the Pointing and Justification Explanation (PJ-X) model, researchers gathered data sets made up of pairs of photographs showing similar scenes, like different types of lunches. Then they came up with a question that has distinct answers for each photo ("Is this a healthy meal?"). What it does: After being trained on enough data, PJ-X could both answer the question using text ("No, it's a hot dog with lots of toppings"') and put a heat map over the photo to highlight the reasons behind the answer (the hot dog and its many toppings). Why it matters: Typical AIs are black boxes--good at identifying things, but with algorithmic logic that is opaque to humans.

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the physical processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

In human-in-the-loop machine learning, the user provides information beyond that in the training data. Many algorithms and user interfaces have been designed to optimize and facilitate this human--machine interaction; however, fewer studies have addressed the potential defects the designs can cause. Effective interaction often requires exposing the user to the training data or its statistics. The design of the system is then critical, as this can lead to double use of data and overfitting, if the user reinforces noisy patterns in the data. We propose a user modelling methodology, by assuming simple rational behaviour, to correct the problem. We show, in a user study with 48 participants, that the method improves predictive performance in a sparse linear regression sentiment analysis task, where graded user knowledge on feature relevance is elicited. We believe that the key idea of inferring user knowledge with probabilistic user models has general applicability in guarding against overfitting and improving interactive machine learning.

The recent National Labor Relations Board (NLRB) decision that effectively reinstated the Obama era's over-reaching joint employer regulation is a perfect example of how the left plays by its own set of rules. The joint employer rule made headlines in 2015 when the NLRB, under President Obama, rewrote the definition of what the government considered a "joint employer." Traditionally, a joint employer was an employer who shared direct control over an employee's workplace or employment with another employer. The idea was that since all employers shared and exercised similar and immediate control over employees, all should be responsible for making sure the employees had safe and reasonable working conditions. It also meant all joint employers were responsible for mistakes or bad behavior at their businesses.

EnterpriseCyc is a fully supported version of the knowledge base and reasoning technology that includes enterprise-grade development, deployment, and administration capabilities. It can be licensed for commercial applications. Academic institutions also have the option to license ResearchCyc, a full version of the knowledge base and reasoning technology that is strictly for non-commercial research purposes. The Platforms provide a powerful knowledge representation language (CycL), a vast ontology of concepts and relations, and a formally modeled repository of knowledge about these concepts enabling you to build on decades of knowledge modeling rather than starting from a blank page. In addition, Cyc includes an inference (reasoning) engine that makes use of a large suite of custom reasoners that provide unparalleled performance over a large knowledge base and any volume of data.

This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers' and practitioners' efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution.

In December, Oakland-based U.S. District Judge Haywood Gilliam blocked the changes to President Barack Obama's health care law. The judge said the Trump administration failed to give the notice and comment period required before implementing the changes.