In principle, higher-order networks that have multiple edge types are more informative than their lower-order counterparts. In practice, however, excessively rich information may be algorithmically infeasible to extract. It requires an algorithm that assumes a high-dimensional model and such an algorithm may perform poorly or be extremely sensitive to the initial estimate of the model parameters. Herein, we address this problem of community detection through a detectability analysis. We focus on the expectation-maximization (EM) algorithm with belief propagation (BP), and analytically derive its algorithmic detectability threshold, i.e., the limit of the modular structure strength below which the algorithm can no longer detect any modular structures. The results indicate the existence of a phase in which the community detection of a lower-order network outperforms its higher-order counterpart.

In late August, an astronaut on board the International Space Station remotely operated a humanoid robot to inspect and repair a solar farm on Mars--or at least a simulated Mars environment, which in this case is a room with rust-colored floors, walls, and curtains at the German Aerospace Center, or DLR, in Oberpfaffenhofen, near Munich. European Space Agency astronaut Paolo Nespoli commanded the humanoid, called Rollin' Justin, as the robot performed a series of navigation, maintenance, and repair tasks. Instead of relying on direct teleoperation, Nespoli used a tablet computer to issue high-level commands to the robot. In one task, he used the tablet to position the robot and have it take pictures from different angles. Another command instructed Justin to grasp a cable and connect it to a data port.

At the beginning of this year, an IT system called "Libratus", based on artificial intelligence, beat four world-class players at poker. Long before this, chess-playing machines have already proven themselves superior to their human opponents. But it is not only the domain of games that is being taken over by artificial intelligence. In other facets of our lives, too, intelligent machines are making headway. Robots vacuum the house and mow the lawn by themselves.

The second largest economy of the Arab world is quietly switching from oil to Artificial Intelligence. In a world first, the UAE on Thursday appointed a minister of Artificial Intelligence, which is also the first such acknowledgement by the Arab world that these indeed are the technologies that are going to shape economies around us. Omar Bin Sultan Al Olama, 27, will spearhead UAE's ambition to be at the forefront of the global technological revolution, which will see it planning to build homes on the planet Mars by 2117. The UAE plans to have a fully functioning city of 600,000 people on Mars. "We aspire in the coming century to develop science, technology and our youth's passion for knowledge," tweeted Sheikh Mohammed bin Rashid al Maktoum, the country's vice president and prime minister, when he announced the project -- known as "Mars 2117" -- earlier this year.

Energy, insurance, telecommunications, and many other industries could also have drones in their future.

Just when this old dinosaur was beginning to come to grips with the dizzying speed of the digital world, quantum computing crops up. It was during my halcyon days on Wall Street that the internet was born and began its unfettered march to dominance. At that time, in the late '80s and '90s, no one could foresee the role that the computer and the internet would play. Heck, the iPhone is only 10 years old. One had to think, what could be better than this?

Generalized Linear Bandits (GLBs), a natural extension of the stochastic linear bandits, has been popular and successful in recent years. However, existing GLBs scale poorly with the number of rounds and the number of arms, limiting their utility in practice. This paper proposes new, scalable solutions to the GLB problem in two respects. First, unlike existing GLBs, whose per-time-step space and time complexity grow at least linearly with time $t$, we propose a new algorithm that performs online computations to enjoy a constant space and time complexity. At its heart is a novel Generalized Linear extension of the Online-to-confidence-set Conversion (GLOC method) that takes \emph{any} online learning algorithm and turns it into a GLB algorithm. As a special case, we apply GLOC to the online Newton step algorithm, which results in a low-regret GLB algorithm with much lower time and memory complexity than prior work. Second, for the case where the number $N$ of arms is very large, we propose new algorithms in which each next arm is selected via an inner product search. Such methods can be implemented via hashing algorithms (i.e., "hash-amenable") and result in a time complexity sublinear in $N$. While a Thompson sampling extension of GLOC is hash-amenable, its regret bound for $d$-dimensional arm sets scales with $d^{3/2}$, whereas GLOC's regret bound scales with $d$. Towards closing this gap, we propose a new hash-amenable algorithm whose regret bound scales with $d^{5/4}$. Finally, we propose a fast approximate hash-key computation (inner product) with a better accuracy than the state-of-the-art, which can be of independent interest. We conclude the paper with preliminary experimental results confirming the merits of our methods.

Identifying arbitrary topologies of power networks in real time is a computationally hard problem due to the number of hypotheses that grows exponentially with the network size. A new "Learning-to-Infer" variational inference method is developed for efficient inference of every line status in the network. Optimizing the variational model is transformed to and solved as a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. A major advantage of the developed Learning-to-Infer method is that the labeled data used for training can be generated in an arbitrarily large amount fast and at very little cost. As a result, the power of offline training is fully exploited to learn very complex classifiers for effective real-time topology identification. The proposed methods are evaluated in the IEEE 30, 118 and 300 bus systems. Excellent performance in identifying arbitrary power network topologies in real time is achieved even with relatively simple variational models and a reasonably small amount of data.

From Fukushima in Japan to Sellafield in the UK, the world is home to a number of sites that are contaminated with radioactive waste and require clean-up. The current techniques available to do this are expensive and time consuming – but a new'super hero' robot could help to cut both costs and time. The robot, called Avexis, is designed to fit through a 100mm access port in the flooded reactors at the Fukushima site, to locate and analyse melted fuel. Many areas around Fukushima are still being decontaminated, 58,000 people are still displaced from their homes and the local food industries have been crippled. Its designers hope that the robot will be ready to deploy at the Fukushima Daiichi Nuclear power plant by February 2018.

Last year the UAE got a Minister of Happiness, and now, in another world first, the country has a Minister of Artificial Intelligence – an acknowledgement by the Emirates that these are the technologies that are going to change the world around us, and quickly. H.H. Sheikh Mohammed bin Rashid Al Maktoum, Vice President of the UAE and ruler of Dubai, announced a full cabinet reshuffle today, and as part of that 27-year-old Omar Bin Sultan Al Olama has been announced as the Minister of AI. Al Olama has been working as the Deputy Director of the Future Department for just over a year now, and he has been on the Executive Committee of the World Government Summit since 2014. He has a BBA from the American University of Dubai, and a diploma of excellence and project management from the American University in Sharjah. Well, they plan to use AI to not only streamline costs, but to also bolster education and a desire to learn; to reduce accidents on the roads; and to create savings in the energy industry.