Russia is developing a moon base which will be operated with remote-controlled avatars, according to the country's space boss. Dmitry Rogozin, head of Roscosmos, has laid out plans to put robotic avatars on our natural satellite and have them operated by people on Earth. He claimed this endeavour is more ambitious than the iconic US'Apollo' programme of the '60s and '70s. 'This is about creating a long-term base, naturally, not habitable, but visited. But basically, it is the transition to robotic systems, to avatars that will solve tasks on the Moon surface,' Mr Rogozin said.

FUKUSHIMA – Japan Post Co. on Wednesday began transporting documents by drone in Fukushima Prefecture, the first operation of its kind in Japan, following easing of regulations to cope with labor shortages in the transport industry. The company said it will initially use drones to carry its own documents and advertisements between two post offices in the northeastern prefecture to examine whether the unmanned aircraft can be used to carry mail. In the future, it hopes to use drones for deliveries to mountainous regions and remote islands. It launched the operation after the government eased related regulations in September. Prior to easing restrictions, an operator was required to keep the drone in view. But the new regulations allow for longer flights if safety can be ensured remotely through measures such as equipping the drone with a camera.

Reddit co-founder Alexis Ohanian has warned that the glorification of long working hours, or "hustle porn", has become a dangerous trend among tech workers that is putting people's mental and physical health at risk. Speaking at the annual Web Summit technology conference in Lisbon, Mr Ohanian criticised the spread of a working culture that he said stems from tech startups and companies in Silicon Valley. The 35-year-old entrepreneur, who now heads the early stage venture capital firm Initialized Capital, said it was common practice among tech workers to fetishise long working hours – something he admits to doing in the early days of Reddit. 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.

BERLIN (Reuters) - German Foreign Minister Heiko Maas said he will press China to embrace arms controls during upcoming meetings in Beijing, citing the need to regulate robotic and space-based weapons that could soon shift from "science fiction" to reality.

Yuval Noah Harari says data is the new source of political power, and he worries that big data and AI technology threaten to destroy liberal democracy. Yuval Noah Harari is a historian, lecturer, and author. He is the author of the international bestseller Sapiens: A Brief History of Humankind, as well as Homo Deus: A Brief History of Tomorrow, and 21 Lessons for the 21st Century. Harari received his PhD from Oxford. He is currently a lecturer in the history department at The Hebrew University of Jerusalem.

Russia's space agency says that one of the International Space Station's computers has malfunctioned, but the glitch doesn't pose any risks to the crew. Roscosmos said Tuesday that one of three computers in the station's Russian module has failed. It said Russian flight controllers plan to reboot it Thursday. The International Space Station photographed by Expedition 56 crew members from a Soyuz spacecraft after undocking. Roscosmos said Tuesday that one of three computers in the station's Russian module has failed.

MOSCOW – Russia's space agency says that one of the International Space Station's computers has malfunctioned, but the glitch doesn't pose any risks to the crew. Roscosmos said Tuesday that one of three computers in the station's Russian module has failed. It said Russian flight controllers plan to reboot it Thursday. The agency emphasized that the computer problem wouldn't affect the station's crew -- NASA's Serena Aunon-Chancellor, Russian Sergei Prokopyev and German Alexander Gerst. It said two other computers can maintain the station's operation.

The performance of a reinforcement learning algorithm can vary drastically during learning because of exploration. Existing algorithms provide little information about their current policy's quality before executing it, and thus have limited use in high-stakes applications like healthcare. In this paper, we address such a lack of accountability by proposing that algorithms output policy certificates, which upper bound the suboptimality in the next episode, allowing humans to intervene when the certified quality is not satisfactory. We further present a new learning framework (IPOC) for finite-sample analysis with policy certificates, and develop two IPOC algorithms that enjoy guarantees for the quality of both their policies and certificates.

Supervised dimensionality reduction strategies have been of great interest. However, current supervised dimensionality reduction approaches are difficult to scale for situations characterized by large datasets given the high computational complexities associated with such methods. While stochastic approximation strategies have been explored for unsupervised dimensionality reduction to tackle this challenge, such approaches are not well-suited for accelerating computational speed for supervised dimensionality reduction. Motivated to tackle this challenge, in this study we explore a novel direction of directly learning optimal class-aware embeddings in a supervised manner via the notion of supervised random projections (SRP). The key idea behind SRP is that, rather than performing spectral decomposition (or approximations thereof) which are computationally prohibitive for large-scale data, we instead perform a direct decomposition by leveraging kernel approximation theory and the symmetry of the Hilbert-Schmidt Independence Criterion (HSIC) measure of dependence between the embedded data and the labels. Experimental results on five different synthetic and real-world datasets demonstrate that the proposed SRP strategy for class-aware embedding learning can be very promising in producing embeddings that are highly competitive with existing supervised dimensionality reduction methods (e.g., SPCA and KSPCA) while achieving 1-2 orders of magnitude better computational performance. As such, such an efficient approach to learning embeddings for dimensionality reduction can be a powerful tool for large-scale data analysis and visualization.

We introduce a novel approach to feed-forward neural network interpretation based on partitioning the space of sequences of neuron activations. In line with this approach, we propose a model-specific interpretation method, called YASENN. Our method inherits many advantages of model-agnostic distillation, such as an ability to focus on the particular input region and to express an explanation in terms of features different from those observed by a neural network. Moreover, examination of distillation error makes the method applicable to the problems with low tolerance to interpretation mistakes. Technically, YASENN distills the network with an ensemble of layer-wise gradient boosting decision trees and encodes the sequences of neuron activations with leaf indices. The finite number of unique codes induces a partitioning of the input space. Each partition may be described in a variety of ways, including examination of an interpretable model (e.g. a logistic regression or a decision tree) trained to discriminate between objects of those partitions. Our experiments provide an intuition behind the method and demonstrate revealed artifacts in neural network decision making.