In recent years the e-sports environment around online digital games have gained immense momentum. SuperData [1] reported a worldwide audience of 71 million people who watch competitive gaming, with 31.4 million participation or viewership in the United States. On the company side, considerable resources are being allocated to support the e-sports environment from the main companies in the domain such as Riot Games, Wargaming, Valve, Ubisoft and Turbine. In 2013, prize money for the top three tournaments (Defense of the Ancients 2 (DotA 2), League of Legends (LoL) and Call of Duty (CoD) Championships) rose above 1 million USD. For DotA 2, the main tournament of the year, The International, contained a 10.9 million USD prize pool at the time of writing [2]. This is a tenfold increase in just 2 years and the largest in e-sports history. The prize increase was driven by an intiative by Valve, where players contributed to the prize pool by buying an in-game item The Compendium. In return, the compendium gave players additional ways to interact with the tournament (e.g.

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].

Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data provided by several U.S. power utilities. The results show accurate grouping of accounts similar in their energy usage patterns, and potential for the method to be utilized in energy efficiency programs.

We propose new metrics on sets, ontologies, and functions that can be used in various stages of probabilistic modeling, including exploratory data analysis, learning, inference, and result interpretation. These new functions unify and generalize some of the popular metrics on sets and functions, such as the Jaccard and bag distances on sets and Marczewski-Steinhaus distance on functions. We then introduce information-theoretic metrics on directed acyclic graphs drawn independently according to a fixed probability distribution and show how they can be used to calculate similarity between class labels for the objects with hierarchical output spaces (e.g., protein function). Finally, we provide evidence that the proposed metrics are useful by clustering species based solely on functional annotations available for subsets of their genes. The functional trees resemble evolutionary trees obtained by the phylogenetic analysis of their genomes.

Semidefinite programming has become a key optimization tool in many areas of applied mathematics, signal processing and machine learning. SDPs often arise naturally from the problem structure, or are derived as surrogate optimizations that are relaxations of difficult combinatorial problems [7, 1, 8]. In spite of the importance of SDPs in principle--promising efficient algorithms with polynomial runtime guarantees--it is widely recognized that current optimization algorithms based on interior point methods can handle only relatively small problems. Thus, a considerable gap exists between the theory and applicability of SDP formulations. Scalable algorithms for semidefinite programming, and closely related families of nonconvex programs more generally, are greatly needed. A parallel development is the surprising effectiveness of simple classical procedures such as gradient descent for large scale problems, as explored in the recent machine learning literature. In many areas of machine learning and signal processing such as classification, deep learning, and phase retrieval, gradient descent methods, in particular first order stochastic optimization, have led to remarkably efficient algorithms that can attack very large scale problems [3, 2, 10, 6]. In this paper we build on this work to develop first-order algorithms for solving the rank minimization problem under random measurements and a closely related family of semidefinite programs. Our algorithms are efficient and scalable, and we prove that they attain linear convergence to the global optimum under natural assumptions.

The post is by Ilan Reiter, Principal Data Science Manager at Microsoft. The past few years have witnessed dramatic changes to the energy sector. Renewable energy sources along with the emergence of IoT (Internet of Things) are creating exciting new opportunities. On the consumption side, utilities and indeed the entire energy sector have seen consumption flatten out, with consumers demanding better ways to monitor and control their energy usage. Furthermore, with many grids becoming outdated and expensive to maintain, utilities and smart grid companies are in ever greater need to innovate.

Google and Facebook aren't the only ones vying to be the standard bearer for the hottest AI technique around. China's leading Internet search company, Baidu, which is also investing heavily in a popular and powerful machine-learning technology called deep learning, today released some key code that it uses to make this AI software run very efficiently. Baidu's code was recently used to build an impressive speech-recognition system called Deep Speech 2. For some short sentences, this system is better than most humans at recognizing speech correctly (see "Baidu's Deep-Learning System Rivals People at Speech Recognition"). This is an especially useful technology for Baidu, because it offers a better way for the company's many millions of users to access its services, especially on mobile. Typing Chinese characters on a smartphone is tricky and complex, and many people in China already prefer to use their voice to send short messages or to search the Web for information.

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.