The judicial decisions of the European Court of Human Rights (ECtHR) have been predicted to 79% accuracy using an artificial intelligence (AI) method developed by researchers at UCL, the University of Sheffield and the University of Pennsylvania. The method is the first to predict the outcomes of a major international court by automatically analysing case text using a machine learning algorithm. The study behind it was published today in PeerJ Computer Science. "We don't see AI replacing judges or lawyers, but we think they'd find it useful for rapidly identifying patterns in cases that lead to certain outcomes. It could also be a valuable tool for highlighting which cases are most likely to be violations of the European Convention on Human Rights," explained Dr Nikolaos Aletras, who led the study at UCL Computer Science.

A series of algorithms dubbed the Nightmare Machine is an effort to find the root of horror by generating ghoulish faces, and then relying on user feedback to see which approach makes the freakiest images. MIT also used Google's DeepDream method to create ghastly portraits of famous locations around the world, just in time for Halloween. It's easiest to think of the fear-generating AI as a complex black box that draws a best fit line. When given the task of creating something, it generates an image based on everything it has seen before: in this case, scary faces. Each "scary" or "not scary" vote in MIT's game pulls the best fit line slightly in some direction: more teeth, paler skin, darker background.

Artificial intelligence (AI), which many experts predict will have an enormous impact on the travel industry, is becoming a reality not in the form of blockbuster apps but as a slow, steady trickle of apps, features and technological innovations. The pace of its progress was measured recently by a London School of Economics study, which identified AI and big data as "key disruptive factors shaping the travel distribution industry over the next decade." But the resulting report also noted that those factors have yet to spark major changes. More and more companies are developing and using AI technology today, and experts agree that a recognizable impact isn't too far off; it will begin trickling into agents' workflows over the next several months. "It's definitely not going to be like a flip switch -- one day there's no AI and the next day there's AI," said Paul English, co-founder of the travel agency Lola, where agents use AI to augment their workflows on a daily basis.

In spite of warnings from some of the world's smartest people, engineers at the Massachusetts Institute of Technology have developed a very scary AI. To be more specific, they've built a deep learning algorithm to teach an AI what various kinds of spooky image look like, so that it can tweak other images to make them look spookier. They call it the'Nightmare Machine'. It's learnt a number of styles, from'Fright night' to'Inferno', which the team has applied to a bunch of famous landmarks from around the world. There are a bunch more examples over on the team's Instagram account.

While IBM has invested heavily in Watson, and the artificial intelligence technology is paying off in innovations within healthcare and other sectors, it's performing less brilliantly for the company's bottom line, at least so far. "IBM has pursued big, bespoke moonshot initiatives that can take years and are extremely expensive," Gartner research fellow Tom Austin told The New York Times. "It seems like they're swimming upstream with that." According to the article, the company believes more lucrative times lie ahead. IBM points to a collaboration announced Oct. 18 with Quest Diagnostics, Memorial Sloan Kettering Cancer Center and the Broad Institute of MIT and Harvard as an example.

When Shayla Haddock's doctors tested her for a rare genetic disease in 2012, they couldn't pinpoint a diagnosis. Her lifelong symptoms -- which include club feet, short stature, unusual facial features and congenital deafness -- led her doctors to suspect a disease-causing gene mutation. But for children like Shayla, finding the culprit among 3 billion base pairs of DNA can be very difficult. Each case takes 20 to 40 hours of analysis by a trained geneticist after gene sequencing has been done, and around 75 percent of patients don't get a diagnosis on the first try. As I described in a recent story, Shayla's case was eventually solved by a team of Stanford computer scientists who devised an automated way to compare patients' symptoms and mutated genes to information in existing databases of genetic diseases.

Every week I read about 150 to 200 articles regarding big data, data science and technology. In the future I am going to share with you every week my favourite articles. I am still going to continue writing my on stories, like the upcoming big data platform blueprint. Baidu's chief data scientist Andrew Ng recently gave a great talk about how to apply deep learning. The talk was at the 2016 deep learning school (http://www.bayareadlschool.org)

"SAS has morphed from a pure tools-based analytics company to solutions-based company. That is driving SAS to get more involved with other technologies and ecosystems." SAS is the latest long-time analytics firm to enter the era of'open' and'cloud-based' analytics. SAS Viya, revealed last spring and rolling out now, aims to take businesses into the new-gen of analytics offering full list of lifecycle support features and capabilities, SAS' chief customer officer Fritz Lehman told IDN. "With SAS Viya, we have a complete rewrite [of the popular SAS analytics platform] for today's new business challenges. New ways to access and build analytics apps are key for so many workers inside businesses today," Lehman said.

Today, a digital stethoscope has the ability to record and store heartbeat and respiratory sounds. Tomorrow, the stethoscope could function as an "intelligent thing" by collecting a massive amount of such data, relating the data to diagnostic and treatment information, and building an artificial intelligence (AI)-powered doctor assistance app to provide the physician with diagnostic support in real-time. AI and machine learning increasingly will be embedded into everyday things such as appliances, speakers and hospital equipment. This phenomenon is closely aligned with the emergence of conversational systems, the expansion of the IoT into a digital mesh and the trend toward digital twins. Three themes -- intelligent, digital, and mesh -- form the basis for the Top 10 strategic technology trends for 2017, announced by David Cearley, vice president and Gartner Fellow, at Gartner Symposium/ITxpo 2016 in Orlando, Florida.

Nvidia, a publicly traded company that makes graphics processing units (GPUs), has been focusing its business more and more completely on artificial intelligence (A.I.) after having managed to sell considerable quantities of GPUs for that type of computing work to big companies like Facebook and Google. Those GPUs sit in servers, rather than desktops, laptops, or mobile devices, where Nvidia sticks GPUs for gaming, image processing, and other workloads. But the use of Nvidia's GPUs for A.I., and specifically deep learning -- an approach that involves training artificial neural networks on bunches of data, such as images, and then getting the neural networks to make inferences about new data -- has gained particular traction in the technology industry. Now Nvidia wants to see government agencies adopt and expand their use of deep learning -- which today typically relies on GPUs -- particularly during the training phase. "One of the reasons why I'm going to Washington is I want to talk to a lot of government customers and find out what they're most interested in and what they want to find out about," Nvidia chief scientist Bill Dally told VentureBeat in an interview.