Microsoft's attempt at engaging millennials with artificial intelligence has backfired hours into its launch, with waggish Twitter users teaching its chatbot how to be racist. The company launched a verified Twitter account for "Tay" – billed as its "AI fam from the internet that's got zero chill" – early on Wednesday. The chatbot, targeted at 18- to 24-year-olds in the US, was developed by Microsoft's technology and research and Bing teams to "experiment with and conduct research on conversational understanding". Related: How much should we fear the rise of artificial intelligence? "Tay is designed to engage and entertain people where they connect with each other online through casual and playful conversation," Microsoft said.

Microsoft's attempt at engaging millennials with artificial intelligence has backfired hours into its launch, with waggish Twitter users teaching its chatbot how to be racist. The company launched a verified Twitter account for "Tay" – billed as its "AI fam from the internet that's got zero chill" – early on Wednesday. The chatbot, targeted at 18- to 24-year-olds in the US, was developed by Microsoft's technology and research and Bing teams to "experiment with and conduct research on conversational understanding". Related: How much should we fear the rise of artificial intelligence? "Tay is designed to engage and entertain people where they connect with each other online through casual and playful conversation," Microsoft said.

Hewlett Packard Enterprise last week announced the public availability of its HPE Haven OnDemand Machine Learning as a Service. The Microsoft Azure cloud-based platform provides more than 60 APIs and services that deliver deep learning analytics on a variety of data, including text, audio, images, social Web and video. Launched in beta in 2014, HPE Haven OnDemand has more than 12,750 registered developers generating millions of API calls per week, the company said. Usage- and SLA-based pricing for enterprise-class delivery to support production deployment also are available. "We're bringing a unique solution to the market built on almost a decade of experience in advanced analytics and machine learning that has been proven," said Jeff Veis, VP of marketing for big data at HPE. "We have leveraged this experience into both the design and approach that we have adopted for Haven OnDemand," he told the E-Commerce Times.

Researchers have developed a system to revolutionize future roadways, doing away with traffic lights to eliminate long waits and reduce emissions. The project, initiated by the MIT Senseable City Lab, proposes'slot-based intersections' for self-driving cars, which designate individual times for each vehicle to enter the roadway. To avoid traffic delays and short stops, autonomous vehicles would use sensors to communicate with those around them while maintaining a safe distance. Researchers have developed a system to revolutionize future roadways, doing away with traffic lights to eliminate long waits and reduce emissions. The project proposes'slot-based intersections' for self-driving cars, which designate individual times for each vehicle to enter the roadway Each autonomous car would be equipped with sensors to'communicate' with the cars around it.

While many people in the world are worrying that robots will take over human jobs once artificial intelligence (AI) is fully developed, it's a safe bet that no one put "author" at the top of the robot job list. Yet, now that a Japanese AI program has co-authored a short-form novel that passed the first round of screening for a national literary prize, it seems that no occupation is safe. The robot-written novel didn't win the competition's final prize, but who's to say it won't improve in its next attempt? The novel is actually called The Day A Computer Writes A Novel, or "Konpyuta ga shosetsu wo kaku hi" in Japanese. The meta-narrative wasn't enough to win first prize at the third Nikkei Hoshi Shinichi Literary Award ceremony, but it did come close.

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.

Maximizing the speed and precision of communication while minimizing power dissipation is a fundamental engineering design goal. Also, biological systems achieve remarkable speed, precision and power efficiency using poorly understood physical design principles. Powerful theories like information theory and thermodynamics do not provide general limits on power, precision and speed. Here we go beyond these classical theories to prove that the product of precision and speed is universally bounded by power dissipation in any physical communication channel whose dynamics is faster than that of the signal. Moreover, our derivation involves a novel connection between friction and information geometry. These results may yield insight into both the engineering design of communication devices and the structure and function of biological signaling systems.

An increasing number of people are using online social networking services (SNSs), and a significant amount of information related to experiences in consumption is shared in this new media form. Text mining is an emerging technique for mining useful information from the web. We aim at discovering in particular tweets semantic patterns in consumers' discussions on social media. Specifically, the purposes of this study are twofold: 1) finding similarity and dissimilarity between two sets of textual documents that include consumers' sentiment polarities, two forms of positive vs. negative opinions and 2) driving actual content from the textual data that has a semantic trend. The considered tweets include consumers opinions on US retail companies (e.g., Amazon, Walmart). Cosine similarity and K-means clustering methods are used to achieve the former goal, and Latent Dirichlet Allocation (LDA), a popular topic modeling algorithm, is used for the latter purpose. This is the first study which discover semantic properties of textual data in consumption context beyond sentiment analysis. In addition to major findings, we apply LDA (Latent Dirichlet Allocations) to the same data and drew latent topics that represent consumers' positive opinions and negative opinions on social media.

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].