We examine the meaning and the complexity of probabilistic logic programs that consist of a set of rules and a set of independent probabilistic facts (that is, programs based on Sato's distribution semantics). We focus on two semantics, respectively based on stable and on well-founded models. We show that the semantics based on stable models (referred to as the "credal semantics") produces sets of probability measures that dominate infinitely monotone Choquet capacities; we describe several useful consequences of this result. We then examine the complexity of inference with probabilistic logic programs. We distinguish between the complexity of inference when a probabilistic program and a query are given (the inferential complexity), and the complexity of inference when the probabilistic program is fixed and the query is given (the query complexity, akin to data complexity as used in database theory). We obtain results on the inferential and query complexity for acyclic, stratified, and normal propositional and relational programs; complexity reaches various levels of the counting hierarchy and even exponential levels.

We propose a parallelizable sparse inverse formulation Gaussian process (SpInGP) for temporal models. It uses a sparse precision GP formulation and sparse matrix routines to speed up the computations. Due to the state-space formulation used in the algorithm, the time complexity of the basic SpInGP is linear, and because all the computations are parallelizable, the parallel form of the algorithm is sublinear in the number of data points. We provide example algorithms to implement the sparse matrix routines and experimentally test the method using both simulated and real data.

Abstract--We address the problem of assisting human dispatchers in operating power grids in today's changing context using machine learning, with the aim of increasing security and reducing costs. Power networks are highly regulated systems, which at all times must meet varying demands of electricity with a complex production system, including conventional power plants, less predictable renewable energies (such as wind or solar power), and the possibility of buying/selling electricity on the international market with more and more actors involved at a European scale. This problem is becoming ever more challenging in an aging network infrastructure. One of the primary goals of dispatchers is to protect equipment (e.g. Using years of historical data collected by the French Transmission Service Operator (TSO) "Réseau de Transport d'Electricité" (RTE), we develop novel machine learning techniques (drawing on "deep learning") to mimic human decisions to devise "remedial actions" to prevent any line to violate power flow limits (so-called "thermal limits"). The proposed technique is hybrid. It does not rely purely on machine learning: every action will be tested with actual simulators before being proposed to the dispatchers or implemented on the grid. Electricity is a commodity that consumers take for granted and, while governments relaying public opinion (rightfully) request that renewable energies be used increasingly, little is known about what this entails behind the scenes in additional complexity for the Transmission Service Operators (TSOs) to operate the power grid in security. Indeed, renewable energies such as wind and solar power are less predictable than conventional power sources (mainly thermal power plants).

A recent report asserts that the artificial intelligence (AI) industry will reach a compound annual growth rate of 17.2 percent by 2023. The market is set to swell to a whopping $14.2 billion over the next six years, up from just $525 million in 2015. Natural language processing technology is set to be a huge contributor to this growth. This tech is being adopted rapidly, particularly by financial institutions, because it can carry out customer service transactions and answer common questions in the place of human employees. As for geography, North America is expected hold the majority of the AI industry's market share by 2023, but Europe and the Asia-Pacific region will see significant growth thanks to the rapid pace of urbanization in some areas, increasing use of smartphones, and robust automotive sectors.

A French journalist has revealed how she discovered the dating app Tinder had 800 pages of personal data about her. Judith Duportail said she discovered the app had gathered massive amounts of data about her age, gender, interests, the people she had dated or spoken to, where she went and where she lived over a period of several years she used it. She said that with the help of a privacy activist group, personaldata.io, The US company is required under EU data protection rules to hand over any information it holds on any European citizen if they ask for it. The process involves an email to the privacyinquiries@gotinder.com email address with a clear and precise list of all the information you want with the subject line "Subject Access Request".

Do you know what makes poker the most popular casino game? Its unique blend of strategic decision making under pressure and possibility to play the players, not the cards, turn this simple game into an elaborate affair, which provides an unmatched adrenalin kick. Easy to learn, hard to master, poker comes in many flavours. Undoubtedly, the most popular variations are Texas Hold'em, Omaha and 7-Card Stud, and they figure at all the best online poker sites, UK and worldwide. Thanks to poker's immense popularity, there are so many online poker sites that it is difficult to pinpoint the absolute best place to play.

When science fiction critics Eric S. Rabkin and Robert E. Scholes argued in the 1970s that "no one would go through the trouble of building and maintaining a robot to hand wash clothes or pick up the telephone receiver," they were apparently unaware that Japanese researchers had already made a long-term commitment to develop humanoid robots that could do exactly that. The goal was to care for the elderly in the 21st century. To this end, throughout the 1980s and 1990s, industrial giants Honda, Mitsubishi, and Toyota, as well as university research labs around the world, began demonstrating humanoid prototypes. More recently, the desire to operate in disaster sites like Fukushima has motivated even more researchers to explore humanoid designs. But the dream of humanoid robots goes back much further than the 1970s.

A few years ago, Davide Scaramuzza's lab at the University of Zurich introduced us to the usefulness of a kind of dynamic vision sensor called an event camera. Event cameras are almost entirely unlike a normal sort of camera, but they're ideal for small and fast moving robots when you care more about not running into things than you do about knowing exactly what those things are. In a paper submitted to Robotics and Automation Letters, Antoni Rosinol Vidal, Henri Rebecq, Timo Horstschaefer, and Professor Scaramuzza present the very first time an event camera has been used to autonomously pilot a drone, and it promises to enable things that drones have never been able to do before. The absolute cheapest way to get a drone to navigate autonomously is with a camera. At this point, cameras cost next to nothing, and if you fuse them with an IMU and don't move very fast and the lighting is reliable, they can provide totally decent state estimation, which is very important.

Driverless tractors, combine harvesters and drones have grown a field of crops in Shropshire in a move that could change the face of farming. The autonomous vehicles followed a pre-determined path set by GPS to perform each task, while the field was monitored by scientists using self-driving drones. The project, called hands Free Hectare, began with autonomous tractors drilling channels to precise depths for the barley seeds to be planted. The tractor was also used to plant seeds and spray fungicides, herbicides, and fertilisers. An automated combine harvester then harvested the field of barley.

At a large wine chateau in the region of Bordeaux, France, busy robots use artificial intelligence and other technologies to pick and screen grapes as well as make wine. The chateau might be the world's first such facility to use AI to improve and optimize grape planting and wine brewing, said Zhou Jinting, chairman of Shanghai Hefu Holding (Group) Co Ltd, at the World Robot Conference in Beijing last month. Hefu bought the chateau last year. Zhou said he believes the rapid development of AI, robotics, intelligent machines, big data and cloud computing will bring revolutionary changes to the wine industry as well as a wide range of traditional industries. The chateau is a pilot project of Hefu's AI and robotic technologies that are applied in changing traditional production methods.