An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.

The in-game economies of massively multi-player online games (MMOGs) are complex systems that have to be carefully designed and managed. This paper presents the results of an analysis of auction house data from the MMOG Glitch, across a 14 month time period, the entire lifetime of the game. The data comprise almost 3 million data points, over 20,000 unique players and more than 650 products. Furthermore, an interactive visualization, based on Sankey flow diagrams, is presented which shows the proportion of the different clusters across each time bin, as well as the flow of players between clusters. The diagram allows evaluation of migration of players between clusters as a function of time, as well as churn analysis. The presented work provides a template analysis and visualization model for progression-based or temporal-based analysis of player behavior broadly applicable to games. Keywords: virtual economy, massively multi-player online game, game analytics, auction house, longitudinal analysis 1. Introduction Online games form a major component of the games industry, and have expanded strongly in terms of market share, variety and market penetration in recent years, notably due to the increasing availability of mobile platforms and the introduction of Free-to-Play (F2P) business models by the interactive entertainment industry [15,29,50,51]. Of the wide variety of online games, the Massively Multi-Player Online Game (MMOG) format, and its derivatives, is unique in that these games see thousands or more players interacting within the same virtual environment [21,22,42,46,64]. The games can support complex virtual societies that include ingame economies [3,8].

When researchers Frey and Osborne predicted in 2013 that 47% of US jobs were susceptible to automation by 2050, they set off a wave of dystopian concern. But the key word is "susceptible". The automation revolution is possible, but without a radical change in the social conventions surrounding work it will not happen. The real dystopia is that, fearing the mass unemployment and psychological aimlessness it might bring, we stall the third industrial revolution. Instead we end up creating millions of low skilled jobs that do not need to exist.

A good friend recently told me that it takes a special kind of nerd to appreciate what Google's AlphaGo did to international Go champion Lee Sedol: a nerd that is both a Go nerd and a computer nerd. For Go nerdiness, I am recently enamored with the massively complex game that has exponentially more outcomes and dimensions than chess. As for the tech nerdiness, many of us assumed that after DeepBlue beat Kasparov in chess, any other game was a foregone conclusion. But actually, it's taken twenty years for a computer to rise to the level of top-ranked Go players, because high-level Go incorporates less calculation of a limited set of future outcomes and far more intuition. Challenges like this are not just an interesting competition.

The concept of inhuman intelligence goes back to the deep prehistory of mankind. At first the province of gods, demons, and spirits, it transferred seamlessly into the interlinked worlds of magic and technology. Ancient Greek myths had numerous robots, made variously by gods or human inventors, while extant artefacts like the Antikythera calendrical computer show that even in 200 BCE we could build machinery that usefully mimicked human intellectual abilities. There has been no age or civilisation without a popular concept of artificial intelligence (AI). Ours, however, is the first where the genuine article--machinery that comfortably exceeds our own thinking skills--is not only possible but achievable.

We hear an awful lot about how artificial intelligence can be used to solve hard statistical challenges – but we hear much less about how it could solve emotional problems. But this field already has a name, affective computing, and one of its leading firms today is Affectiva. Don't miss our biggest TNW Conference yet! The startup is a spinout of MIT's Media Lab, where researchers were working on ways to create new technologies that would enhance emotional communication and, yes, it already started offering'Emotion As A Service' late last year. The company has what is thought to be the world's largest database of emotions, gathered using facial recognition technology to analyze over 3.8 million faces from 75 countries and collating over 40 billion different data points.

Minecraft has had its share of real-world crossovers, but nothing quite like this. Norway's annual The Gathering tech conference is hosting a live concert both in real life and in Minecraft tonight at 9PM local time (4PM Eastern), with volunteers mimicking the artists in Minecraft as they parade around the stage. And this isn't a small production, either -- AlunaGeorge, Broiler and Lemaitre are on deck, so you should be in for a good time whether you're looking at the real artists or their blocky avatars.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Are we looking for an algorithm that "If we all die, it would keep trading"? Should we be worried that electronic trading is mushrooming like airplane traffic, while we are not paying that much attention? Today, I'll look for AI pigments of incremental changes in algorithmic trading, first on Wall Street and then outside, in the Fintech startup world. I am not including the HFT space because it is a particular space driven by speed and merits a separate post because of its politically sensitive angle (Michael Lewis's babe). Renaissance Tech and Two Sigma, are probably the most recognizable names in old fashioned quant trading space.

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 maneuvers the streets of Mountain View, California, not far from Google's headquarters. I grew up about five miles 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 (something no Californian ever does), 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 yards 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 colorful 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, colors 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.