In fruition the advances in artificial intelligence have been significant in many fields (and obviously in tech) such as medicine, education, the arts etc. The potential for artificial intelligence in the future is huge and the discourse surrounding its impact on society is hotly debated by governments small and large. Where Do I Think We Are Going? The potential for the Internet of Things will probably explode in the next five years. Medicine (bio medics) has just started to tap the potential for large scale analytics/pipeline. Let the discourse continue, but I personally am excited to see what artificial intelligence brings to the world in the next ten years.

IBM and Galvanize have launched the first online Cognitive Builder Course hosted on IBM Cloud and powered by IBM Watson. The course is aimed at enterprise developers and interested university students who seek to build on their fundamental Python programming skills, while gaining knowledge about machine learning and artificial intelligence. Currently, the technology industry is facing a shortage of experienced developers to address the growing demand for cognitive and AI development. Recently'Upskilling India' a study conducted by IBM Institute for Business Value indicates 60 percent of global executives expect that employees will need new and different skills to be successful. The study also highlights that 61 percent of the respondents believe that India's higher education system is slow in responding to the changing social demands and needs, followed by 59 percent of the respondents expressing the challenge in maintaining a relevant curriculum for students.

Pandas offers some methods to get information of a data structure: info, index, columns, axes, where you can see the memory usage of the data, information about the axes such as the data types involved, and the number of not-null values. To make a boolean query, you need to pass the DataFrame a True/False Series whose index aligns with the DataFrame being queried's index. Merging is another way of combining DataFrames, but unlike concat it combines them looking for matching values in columns of said DataFrames (you can merge by index too). The main method to perform merging in Pandas is merge which lives both in the main pandas namespace and in the DataFrame namespace (unlike concat).

I keep hearing Data Scientists say that'Statistics is Dead', and they even have big debates about it attended by the good and great of Data Science. Interestingly, there seem to be very few actual statisticians at these debates. So why do Data Scientists think that stats is dead? Where does the notion that there is no longer any need for statistical analysis come from? Is statistics dead or is it just pining for the fjords?

In this article, ten questions about MOOCs (crowdsourced from the recipients of the AAAI and SIGCSE mailing lists) were posed by editors Michael Wollowski, Todd Neller, James Boerkoel to Douglas H. Fisher, Charles Isbell Jr., and Michael Littman — educators with unique, relevant experiences to lend their perspective on those issues.

Markov Decision Processes (MDPs) are an effective model to represent decision processes in the presence of transitional uncertainty and reward tradeoffs. However, due to the difficulty in exactly specifying the transition and reward functions in MDPs, researchers have proposed uncertain MDP models and robustness objectives in solving those models. Most approaches for computing robust policies have focused on the computation of maximin policies which maximize the value in the worst case amongst all realisations of uncertainty. Given the overly conservative nature of maximin policies, recent work has proposed minimax regret as an ideal alternative to the maximin objective for robust optimization. However, existing algorithms for handling minimax regret are restricted to models with uncertainty over rewards only and they are also limited in their scalability. Therefore, we provide a general model of uncertain MDPs that considers uncertainty over both transition and reward functions. Furthermore, we also consider dependence of the uncertainty across different states and decision epochs. We also provide a mixed integer linear program formulation for minimizing regret given a set of samples of the transition and reward functions in the uncertain MDP. In addition, we provide two myopic variants of regret, namely Cumulative Expected Myopic Regret (CEMR) and One Step Regret (OSR) that can be optimized in a scalable manner. Specifically, we provide dynamic programming and policy iteration based algorithms to optimize CEMR and OSR respectively. Finally, to demonstrate the effectiveness of our approaches, we provide comparisons on two benchmark problems from literature. We observe that optimizing the myopic variants of regret, OSR and CEMR are better than directly optimizing the regret.

In recent years, AI-based applications have increasingly been used in real-world domains. For example, game theory-based decision aids have been successfully deployed in various security settings to protect ports, airports, and wildlife. This article describes our unique problem-to-project educational approach that used games rooted in real-world issues to teach AI concepts to diverse audiences. Specifically, our educational program began by presenting real-world security issues, and progressively introduced complex AI concepts using lectures, interactive exercises, and ultimately hands-on games to promote learning. We describe our experience in applying this approach to several audiences, including students of an urban public high school, university undergraduates, and security domain experts who protect wildlife. We evaluated our approach based on results from the games and participant surveys.

The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses. As instructors we want to develop curriculum that not only prepares students to be artificial intelligence practitioners, but also to understand the moral, ethical, and philosophical impacts that artificial intelligence will have on society. In this article we provide practical case studies and links to resources for use by AI educators. We also provide concrete suggestions on how to integrate AI ethics into a general artificial intelligence course and how to teach a stand-alone artificial intelligence ethics course.

The program will complementary areas of interactive Smith (University of California, Santa include invited speakers, research and digital entertainment.

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. AI Magazine also maintains a calendar listing that includes nonaffiliated conferences at www.aaai.org/Magazine/calendar.php. Digital Entertainment will be held on Web and Social Media. LPN-ICAPS-17 will be held June 18-Québec City, Canada. MR'17 will be held July 3-6, 2017 in 23, 2017 in Pittsburgh, PA USA.