Nasa has cancelled the first ever all-female spacewalk, saying it doesn't have enough spacesuits ready in the right size. Two of the astronauts currently on the International Space Station – Christina Koch and Anne McClain – had been set to head out of the floating lab to instal powerful new batteries on the outside to charge up from its solar arrays. But now the space agency says a problem with spacesuits will mean Ms McClain is unable to take part in the spacewalk, and she will be replaced by a man, fellow Nasa astronaut Nick Hague. We'll tell you what's true. You can form your own view.

Apple's brand new News subscription service appears to have run into problems as soon as it was released. News is a subscription service within Apple News that allows people to sign up for $9.99 and get unlimited access to 300 magazines and newspapers including the Wall Street Journal. But the new app – which arrived in an update yesterday, after the launch event – appears to have caused some of the devices that run it to crash. Numerous users reported problems, specifically with the versions of the app on iPhone XS Max and on Mac. Opening the app would see it break almost straight away, and then shut down again, affected users said.

In my February blog, I explained how to use the tokenization technique in Natural Language Processing (NLP) to predict whether a particular Tweet could be geolocated to a particular neighborhood in the city of Caracas, Venezuela. Almost 37,000 Spanish Tweets that had a latitude and longitude from the city of Caracas, Venezuela were used to observe reactions to the food shortages within each of the city's five municipalities from December 2014 to October 2016.

According to techopedia, a smart city is a city that utilizes information and communication technologies so that it enhances the quality and performance of urban services (such as energy and transportation) so that there's a reduction in resource consumption, wastage, and overall costs. In this article, we will look at components of a smart city and its AI-powered- IoT use cases, how AI helps with the adaption of IoT in Smart cities, and an example of AI-powered-IoT solution. Hence, a smart city would be a city that not only possesses ICT but also employs technology in a way that positively impacts the inhabitants. This article is an excerpt taken from the book'Hands-On Artificial Intelligence for IoT' written by Amita Kapoor. The book explores building smarter systems by combining artificial intelligence and the Internet of Things--two of the most talked about topics today.

You are free to share this article under the Attribution 4.0 International license. An interface system with augmented reality technology could help people with profound motor impairments operate a humanoid robot to feed themselves and perform routine personal care tasks. Those tasks might include feeding and performing routine personal care tasks such as scratching an itch and applying skin lotion. The web-based interface displays a "robot's eye view" of surroundings to help users interact with the world through the machine. Described in PLOS ONE, the system could help make sophisticated robots more useful to people who don't have experience operating complex robotic systems.

When I heard five or so years back that people in Silicon Valley were getting worried about artificial intelligence causing human extinction, my initial reaction was extreme skepticism. A large reason for that was that the scenario just felt silly. What did these folks think would happen -- was some company going to build Skynet and manufacture Terminator robots to slaughter anyone who stood in their way? It felt like a sci-fi fantasy, not a real problem. This is a misperception that frustrates a lot of AI researchers.

Dozens of scientists, health care professionals and academics have written a letter to the U.N. calling for an international ban of autonomous killer robots, saying recent advances in artificial intelligence "have brought us to the brink of a new arms race in lethal autonomous weapons." The letter, which has been signed by more than 70 health care professionals and was put together by the Future of Life Institute, states that lethal autonomous weapons could fall into the hands of terrorists and despots, lower the barrier to armed conflict and "become weapons of mass destruction enabling very few to kill very many." "Furthermore, autonomous weapons are morally abhorrent, as we should never cede the decision to take a human life to algorithms," the letter continues. "As healthcare professionals, we believe that breakthroughs in science have tremendous potential to benefit society and should not be used to automate harm. We therefore call for an international ban on lethal autonomous weapons."

Travelers at major airports across the country are reporting on computer outages causing delays across multiple airlines. Passengers began taking to Twitter around 11 a.m. EST on Tuesday, complaining of delayed flights and computer outages affecting American Airlines, Alaska Airlines and JetBlue. American Airlines has since confirmed the outages, attributing them to a "technical issue" caused by the Sabre computer systems, which are used by "multiple carriers." Shortly after 11:30 a.m., the airline stated that the issue had been resolved. "Earlier today, Sabre had a brief technical issue that impacted multiple carriers, including American Airlines. This technical issue has been resolved. We apologize to our customers for the inconvenience."

Machine learning and, more specifically, deep learning algorithms have seen remarkable growth in their popularity and usefulness in the last years. This is arguably due to three main factors: powerful computers, new techniques to train deeper networks and larger datasets. Although the first two are readily available in modern computers and ML libraries, the last one remains a challenge for many domains. It is a fact that big data is a reality in almost all fields nowadays, and geosciences are not an exception. However, to achieve the success of general-purpose applications such as ImageNet - for which there are +14 million labeled images for 1000 target classes - we not only need more data, we need more high-quality labeled data. When it comes to the Oil&Gas industry, confidentiality issues hamper even more the sharing of datasets. In this work, we present the Netherlands interpretation dataset, a contribution to the development of machine learning in seismic interpretation. The Netherlands F3 dataset acquisition was carried out in the North Sea, Netherlands offshore. The data is publicly available and contains pos-stack data, 8 horizons and well logs of 4 wells. For the purposes of our machine learning tasks, the original dataset was reinterpreted, generating 9 horizons separating different seismic facies intervals. The interpreted horizons were used to generate approximatelly 190,000 labeled images for inlines and crosslines. Finally, we present two deep learning applications in which the proposed dataset was employed and produced compelling results.

Planning paths efficiently in a high-dimensional continuous state and action space is a fundamental yet challenging problem in many real-world applications, such as robot manipulation and autonomous driving. Since the general path planning problem is PSPACE-complete (Reif, 1979), one typically resorts to approximate or heuristic algorithms. Sampling-based planning algorithms, such as probabilistic roadmaps (PRM) (Kavraki et al., 1996), rapidlyexploring random trees (RRT) (LaValle, 1998), and their variants (Karaman & Frazzoli, 2011), provide principled approximate solutions to a wide spectrum of high-dimensional path planning tasks. However, these generic algorithms typically employ a uniform proposal distribution for sampling which does not make use of the structures of the problem at hand and thus may require lots of samples to obtain an initial feasible solution path for complicated tasks, e.g., a narrow passage in a map. To improve the sample efficiency, researchers designed algorithms to take problem structures into account, such as the Gaussian sampler (Boor et al., 1999), the bridge test (Hsu et al., 2003), the reachability-guided sampler (Shkolnik et al., 2009), the