The ultimate goals of serious education games (SEG) are to facilitate learning and maximizing enjoyment during playing SEGs. In SEG development, there are normally two spaces to be taken into account: knowledge space regarding learning materials and content space regarding games to be used to convey learning materials. How to deploy the learning materials seamlessly and effectively into game content becomes one of the most challenging problems in SEG development. Unlike previous work where experts in education have to be used heavily, we proposed a novel approach that works toward minimizing the efforts of education experts in mapping learning materials to content space. For a proof-of-concept, we apply the proposed approach in developing an SEG game, named \emph{Chem Dungeon}, as a case study in order to demonstrate the effectiveness of our proposed approach. This SEG game has been tested with a number of users, and the user survey suggests our method works reasonably well.

The decision has provided a solid legal foundation on which a rules-based approach for resolving disputes in the South China Sea can be built,

With the increasingly vast volumes of data generated by enterprises, relying on static rule-based decision systems is no longer competitive; instead, there is an unprecedented opportunity to optimize decisions, and adapt to changing conditions, by leveraging patterns in real-time and historical data. The very size of the data however makes it impossible for humans to find these patterns, and this has lead to an explosion of industry interest in the field of Machine Learning, which is the science and practice of designing computer algorithms that, broadly speaking, find patterns in large volumes of data. ML is particularly important in digital marketing: understanding how to leverage vast amounts of data about digital audiences and the media they consume can be the difference between success and failure for the world's largest brands. MediaMath's vision is for every addressable interaction between a marketer and a consumer to be driven by ML optimization against all available, relevant data at that moment, to maximize long-term marketer business outcomes. In this series of blog posts we will present a very basic, non-technical introduction to Machine Learning.

SoftBank Group Corp.'s former Chief Operating Officer Nikesh Arora, whose 8 billion package topped the list, hails from India. Higher wages in Japan were typically earned by sticking around, thanks to rigid corporate promotion systems based on tenure. In the U.S., executives have reaped the benefits of a shift from cash to equity-based compensation tied to their companies' performance -- a change that sent pay packages spiraling in recent decades as the stock market soared. Interlocking stock ownership between companies listed on the Tokyo Stock Exchange fell to 16 percent in 2015 from 50 percent in 1990, according to data from Nomura Holdings Inc. Last year's biggest pay packages for Japan executives born in the country were Fanuc Corp. CEO Yoshiharu Inaba's 690 million and Sony Corp. CEO Kazuo Hirai's 513 million, data compiled by Bloomberg show.

To predict and prevent fraud online even more quickly than cybercriminals adopt new tactics, Sift Science has raised 30 million in a Series C round of venture funding in a round led by Insight Venture Partners. According to the U.S. Internet Crime Complaint Center (IC3) 2015 annual report, reported internet crimes alone, ranging from personal and corporate data breaches to credit card fraud, phishing and identity, theft cost victims 1.07 billion. The financial losses to U.S. businesses as a result of such crimes go well beyond what is reported to IC3, of course. Certain types of sites and apps are under more frequent attacks than others, with digital gift card businesses, money transfer services and on-demand marketplaces rampant with fraud attempts. Sift Science uses machine learning and artificial intelligence to automatically surmise whether an attempted transaction or interaction with a business online is authentic or potentially problematic.

In the beginning, marketing automation platforms grew other channels and tools around their core of email marketing. The basic mode involved if/then rules: if a customer takes this action, show this response. But overlapping campaigns with if/then rules become very complicated very quickly, especially when you're talking about millions of customers, each one in a different frame of mind, and each expecting his/her own personalized experience. As a result, marketing platforms are evolving from their traditional if/then campaigns to the newer approach of customer journeys that are often guided by machine learning. It's the difference between setting up all the rules for the encounter on the one hand, B2B marketing startup YesPath CEO Jason Garoutte told me, and employing something like Netflix's recommendation engine, on the other. "Netflix doesn't write rules about what [movie] you should watch next," he pointed out.

Newspapers and politicians around the world have been reacting to Boris Johnson's appointment as UK foreign secretary. Many were surprised, citing his history of faux pas including insulting the president of Turkey and commenting on the US president's ancestry. Here we take a look at the response in countries where Mr Johnson will now represent the UK. The Washington Post publishes a round-up of "undiplomatic" things Mr Johnson has said during his time in public life. Washington Post writer Ishaan Tharoor also writes that Mr Johnson "has controversially bucked the Western trend and praised Syrian President Bashar al-Assad for battling the Islamic State, no matter its parallel campaign of violence on Syria's civilian population".

Online surveys have the potential to support adaptive questions, where later questions depend on earlier responses. Past work has taken a rule-based approach, uniformly across all respondents. We envision a richer interpretation of adaptive questions, which we call dynamic question ordering (DQO), where question order is personalized. Such an approach could increase engagement, and therefore response rate, as well as imputation quality. We present a DQO framework to improve survey completion and imputation. In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations. In another scenario, our goal is to provide a personalized prediction. Since it is possible to give reasonable predictions with only a subset of questions, we are not concerned with motivating users to answer all questions. Instead, we want to order questions to get information that reduces prediction uncertainty, while not being too burdensome. We illustrate this framework with an example of providing energy estimates to prospective tenants. We also discuss DQO for national surveys and consider connections between our statistics-based question-ordering approach and cognitive survey methodology.

AI and machine learning changes the software paradigm computers have been based on for many decades. In the traditional computing domain, providing an input, we feed it into an algorithm to produce the desired output. This is the rule-based framework the majority of the systems around us still work with. We set up our thermostat to a desire temperature (input) and a rule based programming (algorithm) will take care of reading a sensor and activating heating or AC machines to get to the room temperature we want (output). The industry has been working relentlessly for many years developing better hardware, software and apps to solve a gazillion problems and use cases around us with programmable solutions.

Danaher/Institute for Ethics and Emerging TechnologiesAlgorithms are everywhere, and in most ways they make our lives better. In the simplest terms, algorithms are procedures or formulas aimed at solving problems. Implemented on computers, they sift through big databases to reveal compatible lovers, products that please, faster commutes, news of interest, stocks to buy, and answers to queries. Dud dates or boring book recommendations are no big deal. But John Danaher, a lecturer in the law school at the National University of Ireland, warns that algorithmic decision-making takes on a very different character when it guides government monitoring and enforcement efforts.