Self-driving car technology is advancing at a rapid pace, thanks to work by technology and automotive companies ranging from Google to Ford. While still years from being widely adopted, driverless cars are increasingly capable of handling a wide variety of driving conditions. With the basic technical hurdles out of the way, driverless car experts now face a different set of problems -- namely, ethical issues. Among the most pressing questions: What should a driverless car do if it faces a choice between putting its passengers at risk or harming someone outside the vehicle? New research reveals that people are conflicted about such a dilemma.

Would you buy a driverless car that is programmed to kill you? Ok, how about a car programmed to kill you if it's the only way to avoid plowing into a crowd of dozens? That's one of the conundrums an international group of researchers put to 2,000 US residents through six online surveys. The questions varied the number of people that would be sacrificed or saved in each instance--if you want to try it for yourself, see if you'd make a good martyr here. The study, just published in the journal Science is the latest attempt to answer ethics' classic "trolley problem"--forcing you to choose between saving one life and saving many more.

Self-driving cars have a lot of learning to do before they can replace the roughly 250 million vehicles on U.S. roads today. They need to know how to navigate when their pre-programmed maps are out of date. They need to know how to visualize the lane dividers on a street that's covered with snow. And, if the situation arises, they'll need to know whether it's better to mow down a group of pedestrians or spare their lives by steering off the road, killing all passengers onboard. Once self-driving cars are logging serious miles, they're sure to find themselves in situations where an accident is unavoidable.

Dublin-based Text and Image Analysis startup, AYLIEN, is looking to hire Research Fellows, Postdoctoral Researchers and Lecturers to conduct novel and significant research in the fields of Artificial Intelligence, Machine Learning and Natural Language Processing. This is a unique opportunity to work with a team of talented Scientists and Engineers at AYLIEN to push the boundaries of AI research. Please send a brief introduction about yourself, your CV and links to GitHub, Google Scholar, papers and articles if applicable to jobs@aylien.com AYLIEN is a leading Text and Image Analysis solution provider in Europe, helping tens of thousands of developers and data scientists in more than 500 cities globally to extract meaning and insights from unstructured data, such as news articles, social media updates and customer reviews. We are a team of 13 people spread across Science, Engineering and Sales & Marketing, based near the beautiful River Liffey in the heart of Dublin.

University of Skövde is seeking a post-doc in machine learning for a project where the main application scenario will be text analytics. The post-doc will have an unique opportunity to develop new machine learning algorithms, e.g., from the field of deep learning, to detect and predict how text flows from the internet evolve over time based on over 700 000 different sources on the open web (through an API provided by our partner company Recorded Future). The post-doc will be affiliated with the Skövde Artificial Intelligence Lab (SAIL), which is one of the oldest and most prominent research groups in artificial intelligence (AI) in Sweden. At the University of Skövde Informatics is defined as the science that addresses how information is represented, processed and communicated in artificial and natural systems, and how such systems are used and developed in order to achieve usable and effective applications and solutions for individuals, organizations or society. The post-doc is positioned at the School of Informatics, which is a school in expansion.

WIRE)--Ipswitch, the leader in easy to try, buy and use IT management software, today announced the findings of an independent global study, carried out by analyst firm Freeform Dynamics. The survey examines the attitudes and readiness of IT decision makers with regard to intelligent machines and business systems (machines with decision making and learning capabilities). Exploring the fast-paced adoption of these systems, the report looks at the positive impacts already being observed in the commercial world and the potential barriers to even further mainstream adoption over the next decade. According to the research, investment in intelligent business systems and automation is well underway across the globe. Top current application deployment areas cited by respondents include digital customer engagement systems (55 percent), process automation and workflow systems (52 percent) and automated risk monitoring and management solutions (50 percent).

SITTING IN AN office in San Francisco, Igor Barani calls up some medical scans on his screen. He is the chief executive of Enlitic, one of a host of startups applying deep learning to medicine, starting with the analysis of images such as X-rays and CT scans. It is an obvious use of the technology. Deep learning is renowned for its superhuman prowess at certain forms of image recognition; there are large sets of labelled training data to crunch; and there is tremendous potential to make health care more accurate and efficient. Dr Barani (who used to be an oncologist) points to some CT scans of a patient's lungs, taken from three different angles.

"I think there's recognition it makes sense to have some people thinking about [AI safety] now," says Oxford University philosophy professor Nick Bostrom. Over the past year, Oxford University philosophy professor Nick Bostrom has gained visibility for warning about the potential risks posed by more advanced forms of artificial intelligence. He now says that his warnings are earning the attention of companies pushing the boundaries of artificial intelligence research. Many people working on AI remain skeptical of or even hostile to Bostrom's ideas. But some prominent technologists and scientists--including Elon Musk, Stephen Hawking, and Bill Gates--have echoed some of his concerns.

America's tax enforcement agency, the Inland Revenue Service, is pretty sure Paulo Marques is an international tax evader. For the last five years, without fail, he's been summoned by the authorities to spend hours explaining the ins and outs of his revenue streams. But the Inland Revenue's computer doesn't know that, Marques tells the audience WIRED Money 2016. In fact, when it comes to stopping fraud, machines get it wrong far too often. "If you want to stop fraud, you really need to understand human behaviour," says Marques, who founded Feedzai, a company that uses big data to combat fraud.

Like many autistic children, Julian Brown has trouble reading emotions in people's faces, one of the biggest challenges for people with the neurological disorder. Now the 10-year-old San Jose boy is getting help from'autism glass' -- an experimental device that records and studies faces in real-time and alerts him to the emotions they're expressing. The facial recognition software was developed at Stanford University and runs on Google Glass, a computerised headset with a front-facing camera and a tiny display just above the right eye. Julian Brown has trouble reading emotions in people's faces, one of the biggest challenges for people with the neurological disorder. Now the 10-year-old San Jose boy is getting help from'autism glass' Autism glass records and studies faces in real-time.