If recent clickbait headlines are to be believed, robots are already taking over our schools, relegating "Sir" or "Miss" to the status of a second-rate computer dumped at the back of the class. Yet to many experts, the real value of artificial intelligence (AI) to education may be far more humdrum as a back-of-house tool to free up time for human teachers to build students' social skills, resilience, appetite for learning and character. Miles Berry, principal lecturer in computing education at the University of Roehampton and a key architect of the national curriculum for computing, introduced to replace ICT four years ago, is disappointed at how few schools have exploited the new programme fully. "AI is difficult to teach and schools either lack relevant resources or don't know how to apply them, but in order to plug the technology skills gap, we must give our youngsters time to experiment with creating rudimentary chatbots for example," he says. "Setting up a Google Assistant, Apple Siri or Amazon Alexa and getting it to answer some of the questions that come up in a lesson would be a fairly simple task for many computing teachers, but to get them on-side, we need to talk far more about the role of machine-learning and far less about the dawn of the robots."

The Nature Morte gallery in New Delhi opens its doors to a unique, one of kind show today. Titled'Gradient Descent', the show is the country's first Artificial Intelligence (AI) art exhibition. Curated by 64/1, a Bengaluru-based curation and research collective (founded by Raghava KK and Karthik Kalyanaraman that focuses on raising awareness on AI's place in the realm of contemporary art), 'Gradient Descent' showcases the works of seven, carefully picked artists from the US, Japan, Germany, Turkey, India, UK and New Zealand. Each of these artists, equipped with a strong foundational background in artificial neural networking, has collaborated with AI to produce art. Conceptualised and planned meticulously since February, the show will run till 15 September.

Most of previous machine learning algorithms are proposed based on the i.i.d. hypothesis. However, this ideal assumption is often violated in real applications, where selection bias may arise between training and testing process. Moreover, in many scenarios, the testing data is not even available during the training process, which makes the traditional methods like transfer learning infeasible due to their need on prior of test distribution. Therefore, how to address the agnostic selection bias for robust model learning is of paramount importance for both academic research and real applications. In this paper, under the assumption that causal relationships among variables are robust across domains, we incorporate causal technique into predictive modeling and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm by jointly optimize global confounder balancing and weighted logistic regression. Global confounder balancing helps to identify causal features, whose causal effect on outcome are stable across domains, then performing logistic regression on those causal features constructs a robust predictive model against the agnostic bias. To validate the effectiveness of our CRLR algorithm, we conduct comprehensive experiments on both synthetic and real world datasets. Experimental results clearly demonstrate that our CRLR algorithm outperforms the state-of-the-art methods, and the interpretability of our method can be fully depicted by the feature visualization.

Abstract--Security surveillance is one of the most important issues in smart cities, especially in an era of terrorism. Deploying a number of (video) cameras is a common surveillance approach. Given the never-ending power offered by vehicles to metropolises, exploiting vehicle traffic to design camera placement strategies could potentially facilitate security surveillance. This article constitutes the first effort toward building the linkage between vehicle traffic and security surveillance, which is a critical problem for smart cities. We expect our study could influence the decision making of surveillance camera placement, and foster more research of principled ways of security surveillance beneficial to our physical-world life. Security surveillance is one of the most important issues in smart cities. Due to the continuous growth of cities in size and complexity, keeping cities safe becomes critical to attracting skilled people and investments necessary for economic growth and development. Compounded by terrorism, cities, especially metropolises, have to carefully conduct security surveillance. To fight against the adversary, deploying a number of (video) cameras is a common surveillance approach, which has gained prominence in policy proposals on combating terrorism [1].

Only a handful of countries and companies have any real hope of winning the race for artificial intelligence supremacy. China, Germany, Japan, Russia, South Korea, and the US are among the national contenders and primarily Chinese and American companies lead the pack of commercial contenders, including Alibaba, Baidu, Tencent, Amazon, Facebook, and Google. What distinguishes all of them is the resources they have already devoted and the achievements they have already made in the AI arena. They are poised to leap further and further ahead of those who are lagging behind or have not yet even entered the race. While Germany, Japan, and South Korea are focused primarily on commercial applications, Russia excels in military applications, and the US maintains its general lead (for the time being) in the space, but only China and these leading Chinese companies have positioned themselves to sprint ahead of all the others in the coming decade.

Improving dementia care through increased and timely diagnosis is an NHS priority, yet around half of those living with dementia live with the condition unaware. Now a new machine-learning model that scans routinely collected NHS data has shown promising signs of being able to predict undiagnosed dementia in primary care. Led by the University of Plymouth, the study collected Read-encoded data from 18 consenting GP surgeries across Devon, UK, for 26,483 patients aged over 65. The Read codes – a thesaurus of clinical terms used to summarise clinical and administrative data for UK GPs – were assessed on whether they may contribute to dementia risk, with factors included such as weight and blood pressure. These codes were used to train a machine-learning classification model to identify patients that may have underlying dementia.

Andrea Soltoggio received a combined BSc and MSc degree in Computer Science in 2004 from the Norwegian University of Science and Technology, Norway, and from Politecnico di Milano, Italy. He was awarded a Ph.D. in Computer Science in 2009 from the University of Birmingham, UK. He was with the Laboratory of Intelligent Systems at EPFL, Lausanne, CH, in 2006 and 2008-2009. He was a visiting researcher at the University of Central Florida, US, in 2009. From 2010 to 2014 he was Technical Coordinator of the FP7 European large-scale integration project AMARSi with the Research Institute for Cognition and Robotics, Bielefeld University, Germany.

WIRE)--New technology using deep learning and advanced algorithms to evaluate blood flow to the heart is now being used in English hospitals to fight against coronary heart disease. Coronary heart disease (CHD) is one of the leading causes of death in the UK. It is responsible for more than 66,000 deaths each year and it is estimated that 2.3 million people in the UK are currently living with the diseasei. CHD develops when the arteries leading to the heart narrow or become blocked, which can reduce blood flow, and cause chest pain and heart attacksii. The HeartFlow FFRct Analysis is being supported by NHS England as part of the Innovation and Technology Payment (ITP) programme to help physicians better diagnose coronary heart disease.

Artificial intelligence (AI) has the potential to amplify and enhance people's ability to the point where they will be able to solve the most perplexing problems facing society today. But that potential won't be realised if leaders in organisations don't demystify AI for their teams and create a world of understanding around the impact and value that technology presents to a modern workforce. This is the view of Paul Shanahan, Cloud & Enterprise Business Group lead with Microsoft Ireland. "There is apprehension around AI and its introduction to the workplace," he says. "On the one hand we have new generations coming into the workforce who have grown up in a society where intelligent technology is widely used and accepted. On the other, we have older generations within the workforce who started working before smartphones and maybe even PCs weren't in common use. For that older cohort it would be very easy to imagine AI as something from the future, something unattainable in today's workplace."

Alexander Gerst will test the technology demonstrator aboard the ISS Watson AI (IBM's artificial intelligence technology) is designed to support space flight crews Friedrichshafen / Bremen, 26/02/2018 – Airbus, in cooperation with IBM, is developing CIMON (Crew Interactive MObile CompanioN), an AI-based assistant for astronauts for the DLR Space Administration. The technology demonstrator, which is the size of a medicine ball and weighs around 5 kg, will be tested on the ISS by Alexander Gerst during the European Space Agency's Horizons mission between June and October 2018. "In short, CIMON will be the first AI-based mission and flight assistance system," said Manfred Jaumann, Head of Microgravity Payloads from Airbus. "We are the first company in Europe to carry a free flyer, a kind of flying brain, to the ISS and to develop artificial intelligence for the crew on board the space station." Pioneering work was also being done in the area of manufacturing, Jaumann continued, with the entire structure of CIMON, which is made up of plastic and metal, created using 3D printing.