The world's most popular porn site has come up with a way to circumvent online pornography being blocked by the Indian government that cut off access to its third biggest market. An order from the Uttarakhand High Court last month instructed all internet service providers in the country to take "immediate necessary action for blocking 827 websites" that hosted pornographic content. To get around this, Pornhub set up a'mirror' website that uses a web domain not included in the list of banned sites, allowing Indian-based users to continue to access adult content from the popular pornography website. 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.

The AI-based dimension of Espoo's CCP has attracted external interest from many of Finland's 210 other municipalities. The project is happening against a backdrop where the Finnish government has set about reforming the country's local government structure. The merging of smaller neighbouring municipalities is a fundamental part of this reform, as is the centralisation of frontline public services such as healthcare and education. The Local Authorities Reform Bill is expected to reach Finland's national parliament, the Eduskunta, in the second half of 2019. Budget-conscious municipalities, which are to become more self-financing under the proposed reform, are eager to explore the expanded use of customer-centric digital platforms and AI solutions to reduce their operating costs.

Lawyers need not fear an immediate rise of the machines, it emerged today, after a discussion on making arbitration fit for the future concluded that artificial intelligence (AI) will not be able to issue rulings in the near-future. Although panellists said AI would undoubtedly cause changes to the legal profession and have an impact in arbitration disputes, it was accepted a final decision could not be handed over to a machine. International firm Hogan Lovells, which hosted a discussion at its Hong Kong office, asked whether given that AI can assess likely outcomes of cases and perform document reviews, it is realistic to ask if this could be extended to actually making a final ruling. The discussion comes against a continuous debate about the impact of'lawtech'. James Kwan, partner at Hogan Lovells, said there are'few laws' that explicitly ban robots from being decision makers.

Employees at FedEx in the U.S., Telefónica in Spain and the National Health Service in the U.K. opened their work computers one day in May 2017 to find they no longer had access to thousands of crucial documents. A message appeared demanding payment in bitcoin to have them restored. The ransomware attack known as WannaCry afflicted more than 200,000 people in 150 countries, according to Europol, and was the largest of its kind in recent history. The threat of this sort of crippling data security breach has tech giants turning to artificial intelligence for solutions. As online hackers increasingly use advanced technology for penetrative attacks, the companies that host our private information also are engaging the most advanced systems available in a bid to protect us.

Millions of women over the last two decades have undergone vaginal mesh surgery, but it has recently become clear just how many have experienced severe complications. In our main interview this week, we hear from Sohier Elneil, one of the few surgeons in the UK qualified to remove mesh. Here, Kath Sansom shares her story of what it's like to undergo the treatment and the impact it had on her life. She had a mesh sling implanted in March 2015 to treat mild stress urinary incontinence. It was removed seven months later.

Dronemaker DJI has warned that some of its unmanned aerial vehicles are suddenly falling out of the sky mid-flight. The company says there have been a'small number' of reports surrounding its Matrice 200 series drones, where a power issue is causing them to crash mid-flight. However, the warning has prompted UK police to ground some of their drones. DJI says there have been a'small number' of reports surrounding its Matrice 200 series drones (pictured), where a power issue is causing them to crash mid-flight The United Kingdom Civil Aviation Authority issued a safety notice saying that some 200 model drones lost power mid-flight and dropped straight down to the ground. In one case, a drone experienced an'in-flight issue' and landed on the roof of a commercial building.

Software that mimics human society is being tested to see if it can help prevent religious violence. Researchers used artificial intelligence algorithms to simulate actions driven by sectarian divisions. Their model contains thousands of agents representing different ethnicities, races and religions. Norway and Slovakia are trialling the tech to tackle tensions that can arise when Muslim immigrants settle in historically Christian countries. The Oxford University researchers hope their system can be used to help governments respond to incidents, such as the recent London terror attacks.

This paper presents an effective, cooperative, and probabilistically-complete multi-robot motion planner that enables each robot to move to a desired location while avoiding collisions with obstacles and other robots. The approach takes into account not only the geometric constraints arising from collision avoidance, but also the differential constraints imposed by the motion dynamics of each robot. This makes it possible to generate collision-free and dynamically-feasible trajectories that can be executed in the physical world.The salient aspect of the approach is the coupling of sampling-based motion planning to handle the complexity arising from the obstacles and robot dynamics with multi-agent search to find solutions over a suitable discrete abstraction. The discrete abstraction is obtained by constructing roadmaps to solve a relaxed problem that accounts for the obstacles but not the dynamics. Sampling-based motion planning expands a motion tree in the composite state space of all the robots by adding collision-free and dynamically-feasible trajectories as branches. Efficiency is obtained by using multi-agent search to find non-conflicting routes over the discrete abstraction which serve as heuristics to guide the motion-tree expansion. When little or no progress is made, the routes are penalized and the multi-agent search is invoked again to find alternative routes. This synergistic coupling makes it possible to effectively plan collision-free and dynamically-feasible motions that enable each robot to reach its goal. Experiments using vehicle models with nonlinear dynamics operating in complex environments, where cooperation among robots is required, show significant speedups over related work.

Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Counterfactual inference enables one to answer "What if...?" questions, such as "What would be the outcome if we gave this patient treatment t However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatment options, or both. Here, we present Perfect Match (PM), a method for training neural networks for counterfactual inference that is easy to implement, compatible with any architecture, does not add computational complexity or hyperparameters, and extends to any number of treatments. PM is based on the idea of augmenting samples within a minibatch with their propensity-matched nearest neighbours. Our experiments demonstrate that PM outperforms a number of more complex state-of-the-art methods in inferring counterfactual outcomes across several real-world and semisynthetic datasets. Estimating individual treatment effects (ITE) from observational data is an important problem in many domains. In medicine, for example, we would be interested in using data of people that have been treated in the past to predict what medications would lead to better outcomes for new patients (Shalit et al. (2017)).

Recently, with convolutional neural networks gaining significant achievements in many challenging machine learning fields, hand-crafted neural networks no longer satisfy our requirements as designing a network will cost a lot, and automatically generating architectures has attracted increasingly more attention and focus. Some research on auto-generated networks has achieved promising results. However, they mainly aim at picking a series of single layers such as convolution or pooling layers one by one. There are many elegant and creative designs in the carefully hand-crafted neural networks, such as Inception-block in GoogLeNet, residual block in residual network and dense block in dense convolutional network. Based on reinforcement learning and taking advantages of the superiority of these networks, we propose a novel automatic process to design a multi-block neural network, whose architecture contains multiple types of blocks mentioned above, with the purpose to do structure learning of deep neural networks and explore the possibility whether different blocks can be composed together to form a well-behaved neural network. The optimal network is created by the Q-learning agent who is trained to sequentially pick different types of blocks. To verify the validity of our proposed method, we use the auto-generated multi-block neural network to conduct experiments on image benchmark datasets MNIST, SVHN and CIFAR-10 image classification task with restricted computational resources. The results demonstrate that our method is very effective, achieving comparable or better performance than hand-crafted networks and advanced auto-generated neural networks.