Driverless cars are the most pressing AI-related consideration for the insurance industry, with recent advances from the likes of Google, Uber, and Volvo making it likely they will dominate the roads within the next decade. In June, British insurance company Adrian Flux began offering the first policy specifically geared towards autonomous and partly automated vehicles. The policy covers typical car insurance staples such as damage, fire, and theft, as well as accidents specific to AI - loss or damage as a result of malfunctions in the car's driverless systems, interference from hackers who have got into a car's operating system, failure to install vehicle software updates and security patches, satellite failure or outages affecting navigation systems, or failure of the manufacturer's vehicle operating system or other authorised software.

While you likely have at least a vague sense that artificial intelligence is the future of the devices and services around you, you may or may not know much about the specific technologies that enable machines to process data and react intelligently -- such as by recognizing objects or translating speech in real-time. The concept of deep learning, a technology that MIT Technology Review reports "attempts to mimic the activity in layers of neurons in the neocortex, the wrinkly 80 percent of the brain where thinking occurs" -- can be especially difficult to wrap your head around. Curious about deep learning, and what you need to know about it? Here's exactly how the stuff of science fiction films is coming to life. Deep learning software learns to recognize patterns in digital representations of sounds, images, and other data.

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)