Suppose that a driverless car is headed towards five pedestrians. It can stay on course and kill them or swerve into a concrete wall, killing its passenger. This ethical dilemma is a difficult one - and it appears that people cannot decide which outcome is better. New research shows that people generally approve of cars programmed to sacrifice their passengers to save others, but these same people would not want to be in the car themselves. Self-driving cars are expected to usher in a new era of mobility, safety and convenience.

Computers could soon be helping to diagnose cancer in patients with the help of artificial intelligence that has been trained to spots the early signs of the disease. An AI machine capable of accurately diagnosing breast cancer 92 per cent of the time has been developed by researchers. While it is still not quite as good as human specialists – who are correct 96 per cent of the time – it suggests that AI could soon be used to speed up and improve cancer screening. Scientists have used machine learning to create an artificial intelligence system capable of diagnosing breast cancer from lymph node biopsies with 92 per cent accuracy (cancer cells in a lymph node pictured). When combined with a human pathologist this accuracy increased to 99.5 per cent The system was developed by computer scientists at Harvard Medical School gave a machine learning algorithm slides of lymph nodes from breast cancer patients.

Columbus, Ohio – The area of the brain responsible for recognizing facial expressions appears to be on the right side, just behind the ear. A study published Tuesday in the Journal of Neuroscience found that nerve patterns within this area, which is known as the posterior superior temporal sulcus (pSTS), seem to be programmed to identify movement in certain areas of the face. While one neural pattern detects a furrowed brow, another recognizes the upturned lips of a smile, according to the paper. "That suggests that our brains decode facial expressions by adding up sets of key muscle movements in the face of the person we are looking at," study author Aleix Martinez said in a university press release, as reported by CBS News. Martinez, a cognitive scientist who teaches electrical and computer engineering at Ohio State, added that humans use a significant number of facial movements to express emotion, as well as other non-verbal communications signals and language.

NIIT, a global leader in skills and talent development, today launched a course in Web App Development with MEAN Stack under the DigiNxt Series. The company has recently ventured into Digital Transformation to offer pioneering programs to young aspirants wishing to enter the digital services industry, as well as to IT professionals wishing to reskill themselves for the new digital world. The cutting-edge program will use the student-centred pedagogy of project-based learning to help them carve a successful career in the emerging digital era. Some of the famous web applications like LinkedIn, Netflix, Uber, Paypal, etc. have been built using MEAN Stack. AngularJS, Node.js (MEAN) represents a group of open source technologies which are known to synergize well together, thereby empowering students to launch their own web and mobile apps.

After great promise in the 1960s that machines would soon think like humans, progress stalled for decades. Only in the past 10 years or so has research picked up, and now there are several popular products on the market that do a decent job of at least recognizing spoken speech. For Björn Schuller, full professor and head of the chair of Complex and Intelligent Systems at the University of Passau, Germany, who grew up watching Knight Rider--a television show about a car that could talk--this is the fulfillment of a childhood fantasy. Schuller is a World Economic Forum Young Scientist who will speak at the World Economic Forum's Annual Meeting of the New Champions in Tianjin, China, from June 26 to 28.He recently spoke about the possibility of machines soon tuning in to human language quirks, behavior and emotion. How did you get interested in machine intelligence and speech recognition?

In the last decade, in the Semantic Web field, knowledge bases have attracted tremendous interest from both academia and industry and many large knowledge bases are now available. However, both generation of new knowledge and population of already existing knowledge bases with new facts face several challenges. Most of the time knowledge bases have been manually built, resulting in a highly specialistic and time consuming activity. Nevertheless, sources of unstructured and semi-structured data are still growing at a much faster rate than structured ones, as such it could be desirable to exploit such a large non-structured sources to populate structured knowledge bases. In the Semantic Web, a major cornerstone of knowledge bases are ontologies and schemas that play a key role for providing common vocabularies and for describing and constructing the Web of Data.

By the end of this article, you'll understand how Numerai is using advances in cryptography like homomorphic encryption to allow for open participation in the problem of stock market efficiency. Over the last few years, machine learning algorithms solved big problems in computer vision. One such problem was getting an algorithm to learn how to recognize handwritten digits in the MNIST dataset. Everyone writes digits differently, so the problem was difficult for computers to grasp. When the dataset first became available in 1998, machine learning algorithms for computer vision were not very accurate.

Everyone wants to keep ahead of the technology curve, whether it's being'in-the-know' or as a vital part of your business, to allow for effective future planning or perhaps to start building systems and products based on that technology. So how do you know what that next big thing will be? Were people betting on touch interfaces in 2002? Not me, I was in a Macromedia Flash phase and couldn't possibly imagine anything beating a vector shape moving across the screen at 12 frames per second. I am not that same guy anymore.

That's just one of "five practical research problems" proposed by scientists at Google, OpenAI, Stanford and Berkeley in a paper called "Concrete Problems in AI Safety" (pdf). Others included "safe exploration" issues, or how to stop a curious cleaning robot from sticking a wet mop in an electrical socket, and "avoiding negative side effects" such as a robot breaking granny's vase when cleaning in a rush. The problems may seem a bit silly, when compared to an AI-induced doomsday, but Google researcher Chris Olah wrote, "These are all forward thinking, long-term research questions – minor issues today, but important to address for future systems." A particularly interesting portion of the paper was devoted to avoiding reward hacking, or how to stop AI from gaming its reward function. "Imagine that an agent discovers a buffer overflow in its reward function: it may then use this to get extremely high reward in an unintended way."

Data analytics firm Orbital Insight is partnering with the World Bank to test technology that could help measure global poverty using satellite imagery and artificial intelligence. The new partnership will test the use of AI to supplement these surveys and increase the accuracy of poverty data. Orbital said its AI software will analyze satellite images to see if characteristics such as building height and rooftop material can effectively indicate wealth. The pilot study will be conducted in Sri Lanka. If successful, the World Bank hopes to scale it worldwide.