SAN, FRANCISCO/BEIJING – DJI, the world's top seller of drones for consumers, has snagged a pair of wins in its effort to court businesses. SZ DJI Technology Co. Ltd. said its latest industrial gadget -- the Mavic 2 -- will soon survey power grids for U.S. utility Southern Co., while American Airlines Group Inc. will test the craft for plane inspections. Those are important alliances for the Chinese company, which is grappling with a U.S. government shut-out, a potentially damaging patent lawsuit and rising American tariffs. Privacy is a particularly thorny issue for DJI -- one of the few Chinese technology giants that's made major strides abroad. Escalating U.S. tensions are fueling concerns about the dominance of a Chinese company in unmanned flying craft.

Religious conflict can occur when a group of people who share the same belief or religion feel threatened by another expanding group. This xenophobia then becomes violent in 20 per cent of cases when people start to question their own beliefs and ideals. An international team of resarchers used artificial intelligence to create a virtual world with various'agents' that interacted with one another. This is the first time this form of AI has been used in research and the scientists claim the findings can be used to support governments address and prevent terrorism and social conflict. The software is a psychologically realistic model of a human that mimics how we think and identify with particular groups.

A Chinese firm has developed a fully-automated warehouse that is run completely by robots and has minimal input from humans. The roles of picking, lifting and moving heavy items are done by machines that are controlled and instructed by'robot controllers'. They do the'thinking' and decision-making processes and instruct other machines to perform certain actions. This reduces the need for all robots to be manually'taught' and streamlines the process, making it more efficient, the manufacturers claim. The roles of picking, lifting and moving heavy items in a warehouse are done by machines that are controlled and instructed by'robot controllers' which process and instruct all the robots Mujin, a start-up from the University of Tokyo, is developing this technology in the hope of achieving full automation.

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.

External collaboration: This program is designed in collaboration with SAS.

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.

In 2013 alone, it tragically claimed the lives of more than 3,154 and injured 424,000. Now, each day in the United States about nine people are killed by an inattentive person behind the wheel. EyeSight, a Tel Aviv, Israel-based artificial intelligence (AI) and hardware startup, promises to eradicate the distracted driving problem once and for all -- at least in cars equipped with its hardware. To further that mission, it today announced a $15 million funding round led by Jebsen Capital, with participation from Arie Capital and Mizrahi Tefahot. EyeSight's tech leans on a combination of cameras and artificial intelligence to monitor driver activity.

Lagrangian data assimilation is a complex problem in oceanic and atmospheric modeling. Tracking drifters in large-scale geophysical flows can involve uncertainty in drifter location, complex inertial effects, and other factors which make comparing them to simulated Lagrangian trajectories from numerical models extremely challenging. Temporal and spatial discretization, factors necessary in modeling large scale flows, also contribute to separation between real and simulated drifter trajectories. The chaotic advection inherent in these turbulent flows tends to separate even closely spaced tracer particles, making error metrics based solely on drifter displacements unsuitable for estimating model parameters. We propose to instead use error in the coherent structure coloring (CSC) field to assess model skill. The CSC field provides a spatial representation of the underlying coherent patterns in the flow, and we show that it is a more robust metric for assessing model accuracy. Through the use of two test cases, one considering spatial uncertainty in particle initialization, and one examining the influence of stochastic error along a trajectory and temporal discretization, we show that error in the coherent structure coloring field can be used to accurately determine single or multiple simultaneously unknown model parameters, whereas a conventional error metric based on error in drifter displacement fails. Because the CSC field enhances the difference in error between correct and incorrect model parameters, error minima in model parameter sweeps become more distinct. The effectiveness and robustness of this method for single and multi-parameter estimation in analytical flows suggests that Lagrangian data assimilation for real oceanic and atmospheric models would benefit from a similar approach.

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.

Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data. A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM. On the basis of literature review, our study is the first to adopt CapsNet and NLSTM in the field of traffic forecasting. An experiment on a Beijing transportation network with 278 links shows that the proposed framework with the capability of capturing complicated spatiotemporal traffic patterns outperforms multiple state-of-the-art traffic forecasting baseline models. The superiority and feasibility of CapsNet and NLSTM are also demonstrated, respectively, by visualizing and quantitatively evaluating the experimental results.