In a bid to stem hate speech, Facebook has revealed that its Artificial Intelligence (AI) systems are spotting more offensive photos than humans on its platform. According to a Tech Crunch report, nearly 25 percent of Facebook engineers now regularly use its internal AI platform to build features and do business but the best use is to check and find offensive photos. "One thing that is interesting is that today we have more offensive photos being reported by AI algorithms than by people. The higher we push that to 100 percent, the fewer offensive photos have actually been seen by a human," Joaquin Candela, Facebook's director of engineering for applied machine learning was quoted as saying. "This AI system helps rank News Feed stories, read aloud the content of photos to the vision impaired and automatically write closed captions for video ads that increase view time by 12 percent," he informed.

The Netherlands is not the only country concerned about drones near airports. Last month, the European Aviation Safety Agency said it is setting up a task force to examine the risk of collisions between drones and aircraft.

Researchers in the Netherlands claim to have created the world's first "robots that procreate." What does that mean exactly? Well, child, when two robots' fitness evaluation algorithms come to a successful conclusion, something beautiful happens. You'll know when you're older -- or if you scroll down. "This breakthrough is a significant first step in the Industrial Evolution and can play an important role in, for instance, the colonization of Mars," reads the press release for the "Robot Baby" project.

As banks, financial services providers and brands predict and plan for the way consumers will manage their money in the future, artificial intelligence (AI) is high on the business development strategy for 2016 and beyond. Gideon Hyde, co-founder of Market Gravity, explains how and why artificial intelligence (AI) could hold the key to standing out in banking and financial services. AI is already around us and used everyday within payments, money management and for robo-advice, particularly in the area of intelligent digital assistants that handle regular customer service enquiries and tasks. It can process "big data" far more efficiently than humans and can recognise speech, images, text, patterns of online behaviour, for example to detect fraud as well as appropriate advertisements for upselling. Smart machines and technology can turn data into customer insights and enhance service provisions, bringing the digital experience closer to the human interaction for consumers.

It's a weird feeling, cruising around Silicon Valley in a car driven by no one. I am in the back seat of one of Google's self-driving cars – a converted Lexus SUV with lasers, radar and low-res cameras strapped to the roof and fenders – as it manoeuvres the streets of Mountain View, California, not far from Google's headquarters. I grew up about eight kilometres from here and remember riding around on these same streets on a Schwinn Sting-Ray. Now, I am riding an algorithm, you might say – a mathematical equation, which, written as computer code, controls the Lexus. The car does not feel dangerous, nor does it feel like it is being driven by a human. It rolls to a full stop at stop signs, veers too far away from a delivery van, taps the brakes for no apparent reason as we pass a line of parked cars. I wonder if the flaw is in me, not the car: Is it reacting to something I can't see? The car is capable of detecting the motion of a cat, or a car crossing the street hundreds of metres away in any direction, day or night (snow and fog can be another matter). "It sees much better than a human being," Dmitri Dolgov, the lead software engineer for Google's self-driving-car project, says proudly. He is sitting behind the wheel, his hands on his lap. As we stop at the intersection, waiting for a left turn, I glance over at a laptop in the passenger seat that provides a real-time look at how the car interprets its surroundings. On it, I see a gridlike world of colourful objects – cars, trucks, bicyclists, pedestrians – drifting by in a video-game-like tableau. Each sensor offers a different view – the lasers provide three-dimensional depth, the cameras identify road signs, turn signals, colours and lights. The computer in the back processes all this information in real time, gauging the speed of oncoming traffic, making a judgment about when it is OK to make a left turn. Waiting for the car to make that decision is a spooky moment. I am betting my life that one of the coders who worked on the algorithm for when it's safe to make a left-hand turn in traffic had not had a fight with his girlfriend (or boyfriend) the night before and screwed up the code.

Users who don't want to pay the 180 for the Amazon Echo smart-speaker can now test the system's features in their web browser with Echoism, according to Mashable. Amazon's 9.25-inch cylindrical Echo speaker uses voice interaction, where users ask Alexa questions or request music. Browser testing of these features is just one way Amazon is trying to get Alexa into cars, homes and phones. Popular Science reported Amazon's release of two seperate toolkit packages: Alexa Voice Service and Alexa Skills Kit. These will allow companies to add new functionality and integrate Alexa into various devices including smart yard products and robotic vacuums.

Phylogenetic approaches are finding more and more applications outside the field of biology. Astrophysics is no exception since an overwhelming amount of multivariate data has appeared in the last twenty years or so. In particular, the diversification of galaxies throughout the evolution of the Universe quite naturally invokes phylogenetic approaches. We have demonstrated that Maximum Parsimony brings useful astrophysical results, and we now proceed toward the analyses of large datasets for galaxies. In this talk I present how we solve the major difficulties for this goal: the choice of the parameters, their discretization, and the analysis of a high number of objects with an unsupervised NP-hard classification technique like cladistics. 1. Introduction How do the galaxy form, and when? How did the galaxy evolve and transform themselves to create the diversity we observe? What are the progenitors to present-day galaxies? To answer these big questions, observations throughout the Universe and the physical modelisation are obvious tools. But between these, there is a key process, without which it would be impossible to extract some digestible information from the complexity of these systems. This is classification. One century ago, galaxies were discovered by Hubble. From images obtained in the visible range of wavelengths, he synthetised his observations through the usual process: classification. With only one parameter (the shape) that is qualitative and determined with the eye, he found four categories: ellipticals, spirals, barred spirals and irregulars. This is the famous Hubble classification. He later hypothetized relationships between these classes, building the Hubble Tuning Fork. The Hubble classification has been refined, notably by de Vaucouleurs, and is still used as the only global classification of galaxies. Even though the physical relationships proposed by Hubble are not retained any more, the Hubble Tuning Fork is nearly always used to represent the classification of the galaxy diversity under its new name the Hubble sequence (e.g. Delgado-Serrano, 2012). Its success is impressive and can be understood by its simplicity, even its beauty, and by the many correlations found between the morphology of galaxies and their other properties. And one must admit that there is no alternative up to now, even though both the Hubble classification and diagram have been recognised to be unsatisfactory. Among the most obvious flaws of this classification, one must mention its monovariate, qualitative, subjective and old-fashioned nature, as well as the difficulty to characterise the morphology of distant galaxies. The first two most significant multivariate studies were by Watanabe et al. (1985) and Whitmore (1984). Since the year 2005, the number of studies attempting to go beyond the Hubble classification has increased largely. Why, despite of this, the Hubble classification and its sequence are still alive and no alternative have yet emerged (Sandage, 2005)? My feeling is that the results of the multivariate analyses are not easily integrated into a one-century old practice of modeling the observations. In addition, extragalactic objects like galaxies, stellar clusters or stars do evolve. Astronomy now provides data on very distant objects, raising the question of the relationships between those and our present day nearby galaxies. Clearly, this is a phylogenetic problem. Astrocladistics 1 aims at exploring the use of phylogenetic tools in astrophysics (Fraix-Burnet et al., 2006a,b). We have proved that Maximum Parsimony (or cladistics) can be applied in astrophysics and provides a new exploration tool of the data (Fraix-Burnet et al., 2009, 2012, Cardone \& Fraix-Burnet, 2013). As far as the classification of galaxies is concerned, a larger number of objects must now be analysed. In this paper, I

We give a complete characterization of the complexity of best-arm identification in one-parameter bandit problems. We prove a new, tight lower bound on the sample complexity. We propose the `Track-and-Stop' strategy, which we prove to be asymptotically optimal. It consists in a new sampling rule (which tracks the optimal proportions of arm draws highlighted by the lower bound) and in a stopping rule named after Chernoff, for which we give a new analysis.

Black-box alpha (BB-$\alpha$) is a new approximate inference method based on the minimization of $\alpha$-divergences. BB-$\alpha$ scales to large datasets because it can be implemented using stochastic gradient descent. BB-$\alpha$ can be applied to complex probabilistic models with little effort since it only requires as input the likelihood function and its gradients. These gradients can be easily obtained using automatic differentiation. By changing the divergence parameter $\alpha$, the method is able to interpolate between variational Bayes (VB) ($\alpha \rightarrow 0$) and an algorithm similar to expectation propagation (EP) ($\alpha = 1$). Experiments on probit regression and neural network regression and classification problems show that BB-$\alpha$ with non-standard settings of $\alpha$, such as $\alpha = 0.5$, usually produces better predictions than with $\alpha \rightarrow 0$ (VB) or $\alpha = 1$ (EP).

The satisfiability problem for SPARQL patterns is undecidable in general, since the expressive power of SPARQL 1.0 is comparable with that of the relational algebra. The goal of this paper is to delineate the boundary of decidability of satisfiability in terms of the constraints allowed in filter conditions. The classes of constraints considered are bound-constraints, negated bound-constraints, equalities, nonequalities, constant-equalities, and constant-nonequalities. The main result of the paper can be summarized by saying that, as soon as inconsistent filter conditions can be formed, satisfiability is undecidable. The key insight in each case is to find a way to emulate the set difference operation. Undecidability can then be obtained from a known undecidability result for the algebra of binary relations with union, composition, and set difference. When no inconsistent filter conditions can be formed, satisfiability is efficiently decidable by simple checks on bound variables and on the use of literals. The paper also points out that satisfiability for the so-called `well-designed' patterns can be decided by a check on bound variables and a check for inconsistent filter conditions.