RESTON, Va.--(BUSINESS WIRE)--Software AG (Frankfurt TecDAX: SOW) today announced that it has acquired Zementis, Inc. with headquarters in San Diego, California. Zementis Inc. provides software for "Deep Learning", a key capability in machine learning and data science, and a fundamental technology driving "artificial intelligence" (AI) development. Software AG sees the current machine learning and AI advances as the basis for the next generation of Internet of Things applications such as self-driving cars, personal digital assistants, medical diagnosis, predictive maintenance and robotics. Software AG has already embedded Zementis' ADAPA (Adaptive Decision and Predictive Analytics) into its Digital Business Platform to offer enterprises comprehensive insights for real time business analytics. The combination of Software AG's real-time streaming analytics and ADAPA predictive analytics delivers precise business and technical insights into customer behavior, market dynamics, security risks and sensor information from the Internet of Things (IoT).

Artificial intelligence (AI) and machine learning have emerged as a major talking point for the likes of IBM, Google and Microsoft, with all keen to talk up the work they are doing to help enterprises make the most of these technologies. What to move, where and when. Corporate E-mail Address: This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. Corporate E-mail Address: This email address is already registered.

Using algorithms partially modeled on the human brain, researchers from the Massachusetts Institute of Technology have enabled computers to predict the immediate future by examining a photograph. A program created at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) essentially watched 2 million online videos and observed how different types of scenes typically progress: people walk across golf courses, waves crash on the shore, and so on. Now, when it sees a new still image, it can generate a short video clip (roughly 1.5 seconds long) showing its vision of the immediate future. "It's a system that tries to learn what are plausible videos -- what are plausible motions you might see," says Carl Vondrick, a graduate student at CSAIL and lead author on a related research paper to be presented this month at the Neural Information Processing Systems conference in Barcelona. The team aims to generate longer videos with more complex scenes in the future.

The'Netflix AI' set to hunt for aliens: Machine learning algorithm developed for online recommendations will scour the skies for systems that could sustain life Researchers are using machine learning to find stable planetary systems It uses techniques developed for Google's and Netflix recommendations The tool will also reveal the mass and how elliptical an exoplanet's orbit is Will be used to analyse data from NASA planet hunting mission It uses techniques developed for Google's and Netflix recommendations The tool will also reveal the mass and how elliptical an exoplanet's orbit is Machine learning software (pictured) that pull inspiration from Google and Netflix's algorithms could soon discover alien life in outer space. Did HALLUCINOGENS spark the Salem witch trials? Experts say... Iron Man suits, X ray detectors and a fake Facebook and... Hello there! Chimps can recognise friends with a single... How Donald Trump's administration could change the internet:... Did HALLUCINOGENS spark the Salem witch trials? Experts say... Iron Man suits, X ray detectors and a fake Facebook and... Hello there!

Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide. Accurately predicting resistance phenotypes from genome sequences shows great promise in promoting better use of antimicrobial agents, by determining which antibiotics are likely to be effective in specific clinical cases. In healthcare, this would allow for the design of treatment plans tailored for specific individuals, likely resulting in better clinical outcomes for patients with bacterial infections. In this work, we present the recent work of Drouin et al. (2016) on using Set Covering Machines to learn highly interpretable models of antibiotic resistance and complement it by providing a large scale application of their method to the entire PATRIC database. We report prediction results for 36 new datasets and present the Kover AMR platform, a new web-based tool allowing the visualization and interpretation of the generated models.

The presence of bacteria or fungi in the bloodstream of patients is abnormal and can lead to life-threatening conditions. A computational model based on a bidirectional long short-term memory artificial neural network, is explored to assist doctors in the intensive care unit to predict whether examination of blood cultures of patients will return positive. As input it uses nine monitored clinical parameters, presented as time series data, collected from 2177 ICU admissions at the Ghent University Hospital. Our main goal is to determine if general machine learning methods and more specific, temporal models, can be used to create an early detection system. This preliminary research obtains an area of 71.95% under the precision recall curve, proving the potential of temporal neural networks in this context.

Artificial intelligence and increasing automation is going to decimate middle class jobs, worsening inequality and risking significant political upheaval, Stephen Hawking has warned. In a column in The Guardian, the world-famous physicist wrote that "the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining." He adds his voice to a growing chorus of experts concerned about the effects that technology will have on workforce in the coming years and decades. The fear is that while artificial intelligence will bring radical increases in efficiency in industry, for ordinary people this will translate into unemployment and uncertainty, as their human jobs are replaced by machines. Technology has already gutted many traditional manufacturing and working class jobs -- but now it may be poised to wreak similar havoc with the middle classes.

The capabilities of artificial intelligence and machine learning are accelerating, and many cybersecurity tasks currently performed by humans will be automated. There will still be plenty of work to go around so job prospects should remain good, especially for those who keep up with technology, broaden their skill sets, and get a better understanding of their company's business needs. Cybersecurity jobs won't go the way of telephone operators. Take, for example, Spain-based antivirus company Panda Security. When the company first started, there were a number of people reverse-engineering malicious code and writing signatures.

The new British sci-fi film "Ex Machina," rolling into U.S. theaters over the next few weeks, is the kind of movie that discerning science fiction fans will want to seek out. Directed by Alex Garland (screenwriter of Sunshine and 28 Days Later), "Ex Machina" is a modern-day riff on the Frankenstein story, with high-tech labs, mad scientists and troublesome artificial intelligence (A.I.). It's got some thrilling twists, but "Ex Machina" is more about ideas than action, and it takes its science seriously. The setup: Computer coder Caleb (Domhnall Gleeson) is summoned to the remote research lab of his boss Nathan (Oscar Isaac), the reclusive genius founder of a ginormous tech company that doesn't rhyme with Google, but may as well. There, Caleb meets Ava -- a super-advanced A.I. housed in a super-advanced robotic body, played by Swedish actress Alicia Vikander.

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