Under new rules issued Oct. 7 by the Department of Health and Human Services, employers and universities are allowed to cite religious or moral objections in order to end birth control coverage that was available under President Barack Obama's Affordable Care Act.

In this paper, we demonstrate how a planner (or a robot as an embodiment of it) can explain its decisions to multiple agents in the loop together considering not only the model that it used to come up with its decisions but also the (often misaligned) models of the same task that the other agents might have had. To do this, we build on our previous work on multi-model explanation generation and extend it to account for settings where there is uncertainty of the robot's model of the explainee and/or there are multiple explainees with different models to explain to. We will illustrate these concepts in a demonstration on a robot involved in a typical search and reconnaissance scenario with another human teammate and an external human supervisor.

Multi-Context Systems (MCS) are a powerful framework for interlinking possibly heterogeneous, autonomous knowledge bases, where information can be exchanged among knowledge bases by designated bridge rules with negation as failure. An acknowledged issue with MCS is inconsistency that arises due to the information exchange. To remedy this problem, inconsistency removal has been proposed in terms of repairs, which modify bridge rules based on suitable notions for diagnosis of inconsistency. In general, multiple diagnoses and repairs do exist; this leaves the user, who arguably may oversee the inconsistency removal, with the task of selecting some repair among all possible ones. To aid in this regard, we extend the MCS framework with preference information for diagnoses, such that undesired diagnoses are filtered out and diagnoses that are most preferred according to a preference ordering are selected. We consider preference information at a generic level and develop meta-reasoning techniques on diagnoses in MCS that can be exploited to reduce preference-based selection of diagnoses to computing ordinary subset-minimal diagnoses in an extended MCS. We describe two meta-reasoning encodings for preference orders: the first is conceptually simple but may incur an exponential blowup. The second is increasing only linearly in size and based on duplicating the original MCS. The latter requires nondeterministic guessing if a subset-minimal among all most preferred diagnoses should be computed. However, a complexity analysis of diagnoses shows that this is worst-case optimal, and that in general, preferred diagnoses have the same complexity as subset-minimal ordinary diagnoses. Furthermore, (subset-minimal) filtered diagnoses and (subset-minimal) ordinary diagnoses also have the same complexity.

WASHINGTON/TAIPEI – New security measures including stricter passenger screening take effect on Thursday on all U.S.-bound flights to comply with government requirements designed to avoid an in-cabin ban on laptops, airlines said. Airlines contacted by Reuters said the new measures could include short security interviews with passengers at check-in or the boarding gate, sparking concerns over flight delays and extended processing time. They will affect 325,000 airline passengers on about 2,000 commercial flights arriving daily in the United States, on 180 airlines from 280 airports in 105 countries. The United States announced the new rules in June to end its restrictions on carry-on electronic devices on planes coming from 10 airports in eight countries in the Middle East and North Africa in response to unspecified security threats. Those restrictions were lifted in July, but the Trump administration said it could reimpose measures on a case by case basis if airlines and airports did not boost security.

Google is training AI to identify human behavior, using clips from movies. A link has been sent to your friend's email address. A link has been posted to your Facebook feed. Google is training AI to identify human behavior, using clips from movies.

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future has a dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers will need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, these workers are digging their own graves. In this paper, we propose Human-in-the-loop Artificial Intelligence (HIT-AI) as a fairer paradigm for Artificial Intelligence systems. HIT-AI will reward aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Robin Hoods, HIT-AI researchers should fight for a fairer Artificial Intelligence that gives back what it steals.

So much has changed in high school sports since the Southern Section voted to eliminate Rule 313 in 2008, otherwise known as the Association Rule. The rule restricted coaches from working with their athletes out of season. You couldn't coach your school's players in off-season games let alone hold workouts after school. The one-hour gym class was it. This is the 10th season of unregulated freedom.

Upwork) critically rely on the ability to propose adequate matches based on imperfect knowledge of the two parties to be matched. This prompts the following question: Which matching recommendation algorithms can, in the presence of such uncertainty, lead to efficient platform operation? To answer this question, we develop a model of a task / server matching system. For this model, we give a necessary and sufficient condition for an incoming stream of tasks to be manageable by the system. We further identify a so-called back-pressure policy under which the throughput that the system can handle is optimized. We show that this policy achieves strictly larger throughput than a natural greedy policy. Finally, we validate our model and confirm our theoretical findings with experiments based on logs of Math.StackExchange, a StackOverflow forum dedicated to mathematics.

DARPA (Defense Advanced Research Projects Agency), is a division of the American Defense Department that investigates new technologies. It has for some time regarded the current generation of AI technologies as important in the future. It has been in the forefront of AI research in image recognition, speech recognition and generation, robotics, autonomous vehicles, medical diagnostic systems, and more. However, DARPA is well aware that despite the high level of problem-solving capabilities of AI programs – they lack explainability. AI deep learning algorithms use complex mathematics that is very difficult for human users to understand or comprehend.

The Trump administration's new birth control rule is raising questions among some doctors and researchers. WASHINGTON -- The Trump administration's new birth control rule is raising questions among some doctors and researchers, who say it overlooks known benefits of contraception while selectively citing data that raise doubts about effectiveness and safety. "This rule is listing things that are not scientifically validated, and in some cases things that are wrong, to try to justify a decision that is not in the best interests of women and society," said Dr. Hal Lawrence, CEO of the American Congress of Obstetricians and Gynecologists, a professional society representing women's health specialists. Two recently issued rules -- one addressing religious objections and the other, moral objections -- allow more employers to opt out of covering birth control as a preventive benefit for women under the Obama health care law. Although the regulations ultimately address matters of individual conscience and religious teaching, they also dive into medical research and scholarly studies on birth control. It's on the science that researchers are questioning the Trump administration.