In 2009, the U.S. Environmental Protection Agency (EPA) established the so-called "Endangerment Finding." This defined a suite of six long-lived greenhouse gases as "air pollution." Such air pollution was anticipated to represent a danger to the health and welfare of current and future generations. Thus, the EPA has the authority to regulate these gases under the rules of the U.S. Clean Air Act. Duffy et al. provide a comprehensive review of the scientific evidence gathered in the years since then. These findings further support and strengthen the basis of the Endangerment Finding. Thus, a compelling case has been made even more compelling with an enormous body of additional data. The Clean Air Act requires the Environmental Protection Agency (EPA) to regulate air pollutants when the EPA Administrator finds that they "cause, or contribute to, air pollution which may reasonably be anticipated to endanger public health or welfare." In Massachusetts v. EPA, the U.S. Supreme Court held that the EPA has the authority to regulate greenhouse gases (GHGs) under the Clean Air Act and that the EPA may not refuse to regulate once it has made a finding of endangerment. In December 2009, the EPA released its "Endangerment and Cause or Contribute Findings for Greenhouse Gases under Section 202(a) of the Clean Air Act," known informally as the Endangerment Finding (EF). The EF found that six long-lived GHGs, in combination, should be defined as "air pollution" under the Clean Air Act and may reasonably be anticipated to endanger the health and welfare of current and future generations. The EF is an essential element of the legal basis for regulating GHG emissions under the Clean Air Act. It provides foundational support for important aspects of U.S. climate policy, including vehicle mileage standards for cars and light trucks and the emissions standards for electricity generation known as the "Clean Power Plan." The EF was rooted in careful evaluation of observed and projected effects of GHGs, with assessments from the U.S. Global Change Research Program, the Intergovernmental Panel on Climate Change, and the U.S. National Research Council providing primary evidence. The EF was clear that, although many aspects of climate change were still uncertain, the evidence available in 2009 was strong.

Next week in Washington, DC, the Annual Meeting of the American Association for the Advancement of Science (AAAS, the publisher of Science) will celebrate science and explore many daunting global challenges. The meeting's theme--Science Transcending Boundaries--considers how science can bring together people, ideas, and solutions from across real and artificial borders, disciplines, sectors, ideologies, and traditions. The scientific community must evolve to meet new realities if it is to continue its path of growth and progress and address the world's most pressing problems. The benefits of science and technology cannot be dismissed. They are embedded in our daily lives and undergird the solutions to everything from poverty to disease; sustainable food, water, and energy; climate change; and national and international security.

AI can enhance complex decision-making processes, which is why it is a catalyst for transformation in every industry. It allows onerous and time-consuming tasks to be completed more efficiently and effectively, and can give management teams a depth of insight that was never available before. Machine learning – a form of AI where computer algorithms improve over time through their experience of using data – plays an increasingly prominent role in enterprise risk management. AI can be used to create sophisticated tools to monitor and analyze behavior and activities in real time. Since these systems can adapt to changing risk environments, they continually enhance the organization's monitoring capabilities in areas such as regulatory compliance and corporate governance.

AI is the latest recruit in the ongoing efforts to stamp out the scourge of human trafficking – by helping police figure out which hotels victims are being held. Hundreds of thousands of people are shuttled across borders every year against their will and exploited, most of them young women coerced into prostitution. Traffickers often take photos of their victims in hotel rooms to use in online escort ads. Now, boffins are trying to use machine-learning software to help cops and non-profits identify where these victims are being held based on patterns discerned from the ad images. A group of researchers from George Washington University, Temple University, and Adobe in the US have built a large dataset containing over a million images from 50,000 hotels across different countries.

Facial recognition technology significantly reduces the amount of time it takes to identify people or objects in photos and video. This makes it a powerful tool for business purposes, but just as importantly, for law enforcement and government agencies to catch criminals, prevent crime, and find missing people. We've already seen the technology used to prevent human trafficking, reunite missing children with their parents, improve the physical security of a facility by automating access, and moderate offensive and illegal imagery posted online for removal. Our communities are safer and better equipped to help in emergencies when we have the latest technology, including facial recognition technology, in our toolkit. In recent months, concerns have been raised about how facial recognition could be used to discriminate and violate civil rights.

The need for greater gender and ethnic in diversity in technology is growing from a whisper a decade ago to the roar of a world cup football goal. We can no longer ignore the injustice of a male-dominated algorithmic trade, a despicable parade of inequity and inequality. The naysayers who call out about the discrimination against white males, need to look at the facts of what Joy Boulamwini calls the coded gaze and the increases in algorithmic bias. True, having greater gender and ethnic diversity won't solve all the problems of unfairness, but it will bleed its greatest excesses. Potential imbalances are less likely to go unnoticed.

On the one hand there is a service provided to users free of charge,

Hundreds of thousands of travelers each year deal with flights that are delayed, canceled, or overbooked -- or have their baggage misplaced. But passengers may not know that they can be compensated for these inconveniences. AirHelp, a Europe-based company that assists people in pursuing such claims, today announced two new bots to further automate its operations and sift through the monumental number of requests it receives. AirHelp provides a free website people can use to determine if they are eligible for a refund from their airline. Founded in 2013 as a Y Combinator-backed startup, AirHelp claims to have aided more than 7 million people in processing airline compensation worth almost $930 million in total.

Abstract-- In this paper we propose the use of multiple local binary patterns(LBPs) to effectively classify land use images. We use the UC Merced 21 class land use image dataset. Task is challenging for classification as the dataset contains intra class variability and inter class similarities. Our proposed method of using multi-neighborhood LBPs combined with nearest neighbor classifier is able to achieve an accuracy of 77.76%. Further class wise analysis is conducted and suitable suggestion are made for further improvements to classification accuracy. INTRODUCTION The world is changing rapidly, new technology and infrastructure is resulting in faster growth. To meet the demands of the growing populations, cities are expanding and land use pattern are changing to accommodate the needs.

During the recently-held fourth-quarter earnings call, Elon Musk all but stated that Tesla holds a notable lead in the self-driving field. While responding to Loup Ventures analyst Gene Munster, who inquired about Morgan Stanley's estimated $175 billion valuation for Waymo and its self-driving tech, Musk noted that Tesla actually has an advantage over other companies involved in the development of autonomous technologies, particularly when it comes to real-world miles. "If you add everyone else up combined, they're probably 5% -- I'm being generous -- of the miles that Tesla has. And this difference is increasing. A year from now, we'll probably go -- certainly from 18 months from now, we'll probably have 1 million vehicles on the road with -- that are -- and every time the customers drive the car, they're training the systems to be better. I'm just not sure how anyone competes with that," Musk said.