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.

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.

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.

In fall 2014, we launched a foundational course in artificial intelligence (CS7637: Knowledge-Based AI) as part of the Georgia Institute of Technology's Online Master of Science in Computer Science program. We incorporated principles and practices from the cognitive and learning sciences into the development of the online AI course. We also integrated AI techniques into the instruction of the course, including embedding 100 highly focused intelligent tutoring agents in the video lessons. By now, more than 2000 students have taken the course. Evaluations have indicated that OMSCS students enjoy the course compared to traditional courses, and more importantly, that online students have matched residential students' performance on the same assessments. In this article, we present the design, delivery, and evaluation of the course, focusing on the use of AI for teaching AI. We also discuss lessons we learned for scaling the teaching and learning of AI.

Different subfields of AI (such as vision, learning, reasoning, planning, and others) are often studied in isolation, both in individual courses and in the research literature. This promulgates the idea that these different AI capabilities can easily be integrated later, whereas, in practice, developing integrated AI systems remains an open challenge for both research and industry. Interdisciplinary project-driven courses can fill this gap in AI education, providing challenging problems that require the integration of multiple AI methods. This article explores teaching integrated AI through two project-driven courses: a capstone-style graduate course in advanced robotics, and an undergraduate course on computational sustainability and assistive computing. In addition to studying the integration of AI techniques, these courses provide students with practical applications experience and exposure to social issues of AI and computing. My hope is that other instructors find these courses as useful examples for constructing their own project-driven courses to teach integrated AI.

Additional landmark events in the past 20 or so years that looked at the challenges of AI education have included the AI Education Workshop held at the 2008 AAAI conference and the Improving Instruction of Introductory Artificial Intelligence symposium held at the 1994 AAAI Fall Symposium. To quote Marti Hearst, the organizer of the 1994 symposium (Hearst 1994): "This symposium was motivated by the desire to address an oft-voiced complaint that introductory artificial intelligence is a notoriously difficult course to teach well." With the regular progression of the field and recent successes such as autonomous cars, deep learning, and IBM's Watson system, this situation has not become easier. At the same time, recent innovations in pedagogical technologies, such as massive open online courses (MOOCs), smartphones, and smart classrooms, have revolutionized how we view the art of teaching. We believe that now is a good time to take stock of state-of-the-art practices in the teaching of AI, as well as propose a vision for AI education in the future. This issue of AI Magazine includes five articles at the cutting edge of AI education. Each covers a subject of current concern to the AI education community. We note that the subject area expertise of the authors covers a wide range including robotics, knowledge-based systems, ethics, machine learning, and game theory. The article Ask Me Anything About MOOCs by Douglas Fisher, Charles Isbell, and Michael Littman was a unique project.

The Robot Academy is a new learning resource from Professor Peter Corke and the Queensland University of Technology (QUT), the team behind the award-winning Introduction to Robotics and Robotic Vision courses. There are over 200 lessons available, all for free. The lessons were created in 2015 for the Introduction to Robotics and Robotic Vision courses. We describe our approach to creating the original courses in the article, An Innovative Educational Change: Massive Open Online Courses in Robotics and Robotic Vision. The courses were designed for university undergraduate students but many lessons are suitable for anybody, as you can easily see the difficulty rating for each lesson.

Implementing Enterprise AI is a unique and limited edition course that is focussed on AI Engineering / AI for the Enterprise. The course is launched for the first time and has limited spaces. Created in partnership with H2O.ai, the course uses Open Source technology to work with AI use cases. Successful participants will receive a certificate of completion and also validation of their project from H2O.ai. The course targets developers and Architects who want to transition their career to Enterprise AI.

The goal of this tutorial is to introduce key models, algorithms, and open questions related to the use of optimization methods for solving problems arising in machine learning. It is written with an INFORMS audience in mind, specifically those readers who are familiar with the basics of optimization algorithms, but less familiar with machine learning. We begin by deriving a formulation of a supervised learning problem and show how it leads to various optimization problems, depending on the context and underlying assumptions. We then discuss some of the distinctive features of these optimization problems, focusing on the examples of logistic regression and the training of deep neural networks. The latter half of the tutorial focuses on optimization algorithms, first for convex logistic regression, for which we discuss the use of first-order methods, the stochastic gradient method, variance reducing stochastic methods, and second-order methods. Finally, we discuss how these approaches can be employed to the training of deep neural networks, emphasizing the difficulties that arise from the complex, nonconvex structure of these models.