The robot revolution is here, and it's not all good for emerging-market economies. As the conversion to more automated factories picks up steam in countries like the U.S., Japan and Germany, there'll be less factory work outsourced to developing nations with relatively low labor costs, according to a report by Moody's Investors Service. The impact will be most severe in Hungary, Czech Republic, Slovakia, Vietnam, Malaysia and Thailand. While most robot-related anxiety in popular culture has swirled around unfounded concern of a violent cyborg rebellion and the more likely possibility of blue-collar job losses, Moody's raises the specter that developing countries that depend on manufactured exports could be in for a painful reckoning. Automated factories require a huge up-front investment in technology, but once that's in place the operational costs will often be far lower than in fully staffed manufacturing sites in Eastern Europe and Southeast Asia.

The robotics work programme implements the robotics strategy developed by SPARC, the Public-Private Partnership for Robotics in Europe (see the Strategic Research Agenda). Every week, euRobotics will publish a video interview with a project, so that you can find out more about their activities. The goal of the proposed project is the design and development of an integrated underground robotic system capable for autonomous construction of subterranean small-diameter, highly curved tunnel networks and for localization, mapping and autonomous navigation during its operation. The proposed robotic system will enable the execution of tasks in different application domains of high societal and economic impact including trenchless constructions (cabling and piping) installations, etc., search and rescue operations, remote science and exploration applications. See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Chances are, you're already hip-deep in machine-learning applications. It's how Google Photo organizes those pictures from your vacation in Spain. It's how Facebook suggests tags for the pictures you took at last week's soccer match. It's how the cars of nearly every major automaker can help you avoid unsafe lane changes. Machine learning – which enables a computer to learn without new programming – is exploding in its ability to handle highly complex tasks.

A Thomson flight this week was grounded because of concerns over a Wi-Fi hotspot named "Jihadi London". The aircraft, which was due to fly from Cancun to London Gatwick on Tuesday afternoon, had to be evacuated after a passenger spotted the network and reported it to the crew. Security and police were then called on board to check the plane, and the flight was eventually rescheduled. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

This week's Featured Blog Friday comes from our Reykjavik University student intern, Guðbjörn Einarsson aka Mannsi, who has been working closely with our Data Scientist, Agnes Jóhannsdóttir, to implement Machine Learning technology into our AGR software. As always, if you have any questions or comments regarding this blog post, feel free to comment on this blog post, tweet us @AGRDynamics, or contact us here. AGR Dynamics is certain Machine Learning will play a big role in the future of our business. If you haven't already read through the other Machine Learning blog posts on Recommender Systems and Introduction To Machine Learning you really should, as they are great. Another area where Machine Learning can be applied is sales forecasting.

Clever computers that learn on the job could recast Britain's job market - for better or worse. What are the parties vying for power in the general election saying on the subject? Twenty-nine-year-old Lee Hayhow is the third generation of his family to work as a lorry driver, following his father and grandfather. He is proud of his job. "I've always enjoyed lorries and driving. I trained as a professional driver. I always do it to the best of my ability. He estimates it costs £3,000 to train as a heavy goods vehicles (HGVs) driver. Mr Hayhow's employer, O'Donovan Waste Disposal, paid for this, but not all firms do, he says. And he would be delighted to see the next generation of Hayhows - his two young daughters - follow his career path. But by then, the decision may not be theirs to take. Lorry driving, like many other jobs that help power the British economy, could be facing a huge shake-up. Advances in artificial intelligence (AI) - a field of computer science in which machines are taught to carry out tasks that require human traits of thought or intelligence - have led some to predict a knock-on catastrophe for jobs. Nowhere is the exponential growth of AI more apparent than in the race towards self-driving vehicles. There have been stark warnings about its impact on the jobs market as computer programs are honed to perform a number of roles, including call centre work, banking and paralegal responsibilities, retail and catering tasks, and journalism. Up to 46% of jobs in Scotland could be at risk within the next decade, the Institute for Public Policy Research Scotland recently claimed. Accountancy firm PwC predicted 30% of existing jobs in the UK could be "at high risk of automation" by the 2030s. Calum Chace, author of Surviving AI and the Economic Singularity, foresees "quite a lot" of unemployment caused by the takeover of technology "in a decade and a lot in two decades". "The industrial revolution was mechanisation and humans had something else to offer - cognitive skills.

According to a new market research report by MarketsandMarkets, the market is expected to grow from $667.1 million in 2016 to $7,988.8 million by 2022, at a CAGR of 52.68 percent during the forecast period. The growing usage of big data in the healthcare industry, ability of AI to improve patient outcomes, imbalance between health workforce and patients, reducing the healthcare costs, growing importance on precision medicine, cross-industry partnerships, and significant increase in venture capital investments are expected to drive the AI in healthcare market. Software to hold the largest share of the AI in healthcare market The AI software is used to assist the medical system in relevant insights, medical imaging and diagnostics, drug discovery, in-patient care and hospital management, virtual assistance, precision medicine, lifestyle management and monitoring, patient data and risk analysis, and research. The growing usage of smart devices, and the presence of major AI software providers such as IBM Corporation (US), Google Inc. (US) and Microsoft Corporation (US), Enlitic, Inc. (US), Next IT Corp (US) are driving the growth of the AI in healthcare market for the software offering. Deep learning technology expected to grow at the highest rate between 2017 and 2022 The deep learning technology which includes image recognition, signal recognition, and data mining-is expected to witness the highest CAGR during the forecast period.

In 1865 mining engineer Fredrik Idestam established a groundwood pulp mill on the banks of the Tammerkoski rapids in the town of Tampere, in southwestern Finland. Close to plentiful supplies of renewable forests, the company also had easy seaborne access to burgeoning European markets and the rapidly expanding US of the'Gilded Age'. The appetite for paper and other pulp based products seemed set to anchor the company's raison d'etre. The company in question was Nokia, and through a variety of monumental twists and turns, a company set to lead an entirely different market over a century later. By the end of the 20th Century Nokia dominated the global market for mobile devices.

Creating an artificial intelligence similar to the structure of the human brain carries a range of benefits that drastically outweigh any potential drawbacks. Apart from copying a human's ability to think creatively, learn rapidly from inconsistent data, and utilise the evolutionary benefits that many traditional artificial intelligences lack, artificial copies of human brains will also allow us to study brain diseases and disorders without the use of an actual, organic patient. It is most useful for tasks which require visual and auditory signals to complete fully and safely, such as driving a car or holding a realistic face-to-face conversation. A synthetic brain which uses synchronised'spikes' of electricity in a neural network, like an organic one, will be far more able to carry out these tasks than a standard computer. However, this also means that it will suffer the same drawbacks as an organic brain, such as fading memories and a lesser ability to read and utilise data in a logical instead of hierarchal fashion.

We consider a two-sample hypothesis testing problem, where the distributions are defined on the space of undirected graphs, and one has access to only one observation from each model. A motivating example for this problem is comparing the friendship networks on Facebook and LinkedIn. The practical approach to such problems is to compare the networks based on certain network statistics. In this paper, we present a general principle for two-sample hypothesis testing in such scenarios without making any assumption about the network generation process. The main contribution of the paper is a general formulation of the problem based on concentration of network statistics, and consequently, a consistent two-sample test that arises as the natural solution for this problem. We also show that the proposed test is minimax optimal for certain network statistics.