A year ago, network security specialists spotted a worrying new trend: hackers began unleashing ransomware attacks on really big targets--America's cities. Atlanta, Baltimore, and Greenville, N.C. would later grind to a halt after devastating computer outages disrupted everything from the collection of parking tickets to the sale of new homes. The next big thing that keeps computer scientist Adam Kujawa up at night? Ransomware powered by artificial intelligence, a development that could give exploits such as RobbinHood and WannaCry a potent new makeover to evade cyber defenses, burrow into computer networks and wreak mayhem. In recent years, artificial intelligence and machine learning have been a godsend to IT security professionals, enabling them to detect malware sooner--even the moment it enters the wild--keeping networks more secure and corporate assets safer. But the same technologies that are supercharging network defenses could become a powerfully destructive counter-threat in the wrong hands, experts warn.

NASA is gearing up for a rescue operation that they hope will save a critical instrument on its Mars lander that remains trapped just centimeters below the surface. In March, after less than a year on Mars' surface, NASA's InSight Lander reported that a critical instrument -- a'mole' probe that is designed to burrow into the planet and assess heat emissions -- hit a snag. For several months, the probe, which was meant to bore 16 feet downward, has been trapped just 30 centimeters beneath the planet's surface after less than a month into its burrowing process. A newly devised plan, however, could extricate the probe once and for all. NASA's InSight probe will try to save a critical instrument that is trapped beneath Mars' surface.

Existing graph-based methods for extractive document summarization represent sentences of a corpus as the nodes of a graph or a hypergraph in which edges depict relationships of lexical similarity between sentences. Such approaches fail to capture semantic similarities between sentences when they express a similar information but have few words in common and are thus lexically dissimilar. To overcome this issue, we propose to extract semantic similarities based on topical representations of sentences. Inspired by the Hierarchical Dirichlet Process, we propose a probabilistic topic model in order to infer topic distributions of sentences. As each topic defines a semantic connection among a group of sentences with a certain degree of membership for each sentence, we propose a fuzzy hypergraph model in which nodes are sentences and fuzzy hyperedges are topics. To produce an informative summary, we extract a set of sentences from the corpus by simultaneously maximizing their relevance to a user-defined query, their centrality in the fuzzy hypergraph and their coverage of topics present in the corpus. We formulate a polynomial time algorithm building on the theory of submodular functions to solve the associated optimization problem. A thorough comparative analysis with other graph-based summarization systems is included in the paper. Our obtained results show the superiority of our method in terms of content coverage of the summaries.

With so many commercialized algorithms on the market, many health systems have an important decision to make: Should they buy an artificial intelligence (AI) model or build their own? If a health system elects to build its own model, it has to invest time and manpower into it. But the benefits could be tremendous. The case for building a customized AI model is simple: Instead of the algorithm learning on national data, it is learning on the health system's data, said Pamela Peele, Ph.D., chief analytics officer at UPMC insurance division and UPMC enterprises. She spoke during a World Health Care Congress keynote called "More than Buzz: Realize the Potential of AI and Machine Learning."

Often, large corporations – which may dominate a market sector like the medical imaging field, which includes computerized tomography (CT), magnetic resonance imaging (MRI) and radiography – are so large they are not able to adapt to new, even disruptive, technologies. Sometimes these are spun off into other companies, as is the case with Harris Corporation and AuthenTec, which developed the fingerprint technology used on Apple phones. Other times, a smaller, nimbler company may emerge that introduces something transformative. Such may be the case with Central Florida's Omega Medical Imaging led by Brian Fleming, which recently received FDA clearance for its FluoroShield system. EW: I want to talk about how you became CEO of Omega, but first, explain the FluoroShield system.

What do a Yemeni refugee in the queue for food aid, a checkout worker in a British supermarket and a depressed university student have in common? They're all being sifted by some form of artificial intelligence. Advanced nations and the world's biggest companies have thrown billions of dollars behind AI - a set of computing practices, including machine learning that collate masses of our data, analyse it, and use it to predict what we would do. Yet cycles of hype and despair are inseparable from the history of AI. Is that clunky robot really about to take my job?

Scientists are developing a multitude of artificial intelligence algorithms to help radiologists, like this one that lights up likely pneumonia in the lungs. Artificial intelligence (AI) is poised to upend the practice of medicine, boosting the efficiency and accuracy of diagnosis in specialties that rely on images, such as radiology and pathology. But as the technology gallops ahead, experts are grappling with its potential downsides. "Just working with the technology, I see lots of ways it can fail," says Albert Hsiao, a radiologist at the University of California, San Diego, who has developed algorithms for reading cardiac images and improving their quality. One major concern: Most AI software is designed and tested in one hospital, and it risks faltering when transferred to another.

A central concern surrounding AI is how it might affect the labour market. In recent years, technology that relies on automation has become more advanced, and its application is increasing across a range of different business settings. Does it pose a threat to business and what are the wider implications for society? AI has wide-ranging implications and not just in the places you might first expect. However, it's not necessarily true that AI will destabilize whole areas of the work force as it pertains to certain kinds of workers in the economy.

Americans prefer a space program that focuses on potential asteroid impacts, scientific research and using robots to explore the cosmos over sending humans back to the moon or on to Mars, a poll shows. The poll by The Associated Press and the NORC Center for Public Affairs Research, released Thursday, one month before the 50th anniversary of the Apollo 11 moon landing, lists asteroid and comet monitoring as the No. 1 desired objective for the United States space program. About two-thirds of Americans call that very or extremely important, and about a combined 9 in 10 say it's at least moderately important. The poll comes as the White House pushes to get astronauts back on the moon, but only about a quarter of Americans said moon or Mars exploration by astronauts should be among the space program's highest priorities. About another third called each of those moderately important.

Iranian state television published images on its website Friday morning that claimed show debris from a U.S. military surveillance drone the country shot down Thursday. The pictures appear to show the skin of the U.S. Navy RQ-4A Global Hawk, which was shot down by a surface-to-air missile. The downing of the drone came just days after the administration blamed Iran for attacks on two oil tankers in the Strait of Hormuz, adding to tensions in the region. The United States made a last-minute decision to call off retaliatory strikes against Iran in response to the downing Thursday of a Navy drone that Washington said was over international airspace, a source told Fox News early Friday. But few details about the aborted mission and the circumstances that led to the reversal were available.