We design, conduct and present the results of a highly personalized baseline emotion recognition experiment, which aims to set reliable ground-truth estimates for the subject's emotional state for real-life prediction under similar conditions using a small number of physiological sensors. We also propose an adaptive stimuli-selection mechanism that would use the user's feedback as guide for future stimuli selection in the controlled-setup experiment and generate optimal ground-truth personalized sessions systematically. Initial results are very promising (85% accuracy) and variable importance analysis shows that only a few features, which are easy-to-implement in portable devices, would suffice to predict the subject's emotional state.

The massive amount of available data potentially used to discover patters in machine learning is a challenge for kernel based algorithms with respect to runtime and storage capacities. Local approaches might help to relieve these issues. From a statistical point of view local approaches allow additionally to deal with different structures in the data in different ways. This paper analyses properties of localized kernel based, non-parametric statistical machine learning methods, in particular of support vector machines (SVMs) and methods close to them. We will show there that locally learnt kernel methods are universal consistent. Furthermore, we give an upper bound for the maxbias in order to show statistical robustness of the proposed method.

TRAVELLERS sometimes have to show their travel documents five times when catching a flight: at check-in, at security, then occasionally at outbound immigration, before another check when boarding. Finally there is passport control at the destination. Each is a potential queue. So regular flyers will be interested in anything that might speed up the process. One answer could be facial-recognition technology.

Driverless cars could lead to the end of car insurance as we know it; at least that is the conclusion of a Bank of England report on the impact that autonomous vehicles will have on insurers. The Bank estimates that the market could shrink by as much as 40 per cent by 2040 if the adoption of self-driving cars proves faster than its baseline forecast, which says the market could be a fifth smaller in just over 20 years. Cars represent the biggest line in general insurance with more than £15 billion of gross written premiums reported in 2015, meaning some insurers could face a significant loss of business as more cars become driverless.

Artificial intelligence will have a dramatic impact on business by 2020, according to study released this week by IT services, consulting and business solutions provider Tata Consultancy Services (TCS). The firm's study, "Getting Smarter by the Day: How AI is Elevating the Performance of Global Companies," shows that 84 percent of the 835 executives TCS surveyed from North America, Europe, Asia-Pacific and Latin America said their companies see the use of AI as "essential" to competitiveness. Artificial intelligence in the real world: What can it actually do? What are the limits of AI? And how do you go from managing data points to injecting AI in the enterprise?

In 2011, scientists made one of the most important discoveries in the history of AI development. They found that graphics processing units (GPUs) are far better at simulating biological learning than central processing units (CPUs). In retrospect, it seems obvious. Human brains are much more like GPUs than CPUs. Both brains and GPUs rely on parallel processing that simulates and predicts real world physics. In light of this, AI developers created powerful deep neural networks (DNNs) that emulate human brain function.

DJI Technology Inc, the largest civilian drone maker, is claiming in a new study that "59 lives have been saved by civilian drones in 18 different incidents, with one life a week being saved by drones on average." The study was published on March 14, 2017, and is based on reports in the news. The majority of the rescues have occurred in USA and China, although instances have occurred in Canada and Turkey as well. One third were saved by civilians using their hobbyist drones and not by emergency personnel, showing their far-reaching abilities. The Hindu of Dec 7 reported that in Chennai, India during heavy flash floods the city police "were able to locate as many as 200 people with the help of drones and rescued all of them."

Carmakers and suppliers gave widely differing timelines for the introduction of self-driving vehicles on Thursday, showing the uncertainties surrounding the technology as well as a split between cautious established players and bullish new entrants. Chipmaker Nvidia, facing direct competition with the world's top chipmaker after Intel's $15 billion deal to buy autonomous driving technology firm Mobileye this week, gave the most optimistic predictions. Chief Executive Jen-Hsun Huang forecast carmakers may speed up their plans in the light of technological advances and that fully self-driving cars could be on the road by 2025. "Because of deep learning, because of AI (artificial intelligence) computing, we've really supercharged our roadmap to autonomous vehicles," he said in a keynote speech to the Bosch Connected World conference in Berlin. Germany's Bosch, however, the world's biggest automotive supplier, gave a timetable as much as six years longer to get to the final stage before fully autonomous vehicles, and declined even to forecast when a totally self-driving car might take to the streets.

Who are the Women in STEM who changed the world through science, technology, engineering and mathematics? We asked this question of women at Ayogo, adding "who are the women in STEM you wish you knew?" Here's our list of 13 amazingly cool women in STEM who we wish we knew. Born in 1918, Katherine Johnson, graduated from university at 18. She was awarded the Presidential Medal of Freedom in 2015 for a lifetime of work as a pioneering physicist, mathematician and space scientist. She and her colleagues, Dorothy Vaughan and Mary Jackson did the calculations that guided NASA's 1962 Friendship 7 Mission.

Scientists from the Oxford University along with Google's DeepMind have developed an artificial intelligence system that can lip-read better than humans. The system was trained by thousands of hours of BBC news programs, the media outlet said Friday. The system, called "Watch, Attend and Spell," can correctly lip-read 50 percent of silent speech correctly, while professional…