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. 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.
Lally, Adam (Information Technology and Services) | Bagchi, Sugato (IBM Research) | Barborak, Michael A. (IBM T. J. Watson Research Center) | Buchanan, David W. (IBM T. J. Watson Research Center) | Chu-Carroll, Jennifer (IBM Research) | Ferrucci, David A. (Bridgewater) | Glass, Michael R. (IBM Research) | Kalyanpur, Aditya (IBM T. J. Watson Research Center) | Mueller, Erik T. (Capital One) | Murdock, J. William (IBM T. J. Watson Research Center) | Patwardhan, Siddharth (IBM T. J. Watson Research Center) | Prager, John M. (IBM T. J. Watson Research Center)
We present WatsonPaths, a novel system that can answer scenario-based questions. These include medical questions that present a patient summary and ask for the most likely diagnosis or most appropriate treatment. WatsonPaths builds on the IBM Watson question answering system. WatsonPaths breaks down the input scenario into individual pieces of information, asks relevant subquestions of Watson to conclude new information, and represents these results in a graphical model.
Open Access Subscription Access Deploying Constraint Programming for Testing ABB's Painting Robots Morten Mossige, Arnaud Gotlieb, Hein Meling Abstract This report explores the use of constraint programming for the validation of ABB Robotics' painting robots. This report explores the use of constraint programming for the validation of ABB Robotics' painting robots.
Education in AI is critical to the success of the scientific and technological enterprise of AI. Today, the AI community is exploring new pedagogies and technologies to help make AI education more accessible, affordable, and achievable. This issue of AI Magazine on AI Education, coedited by Michael Wollowski, Todd Neller, and James Boerkoel, presents articles that explore some of these goals.
Open Access Subscription Access Artificial Intelligence Education: Editorial Introduction Michael Wollowski, Todd Neller, James Boerkoel Abstract This issue of AI Magazine include five articles covering subjects of current concern to the AI education community. This editorial introduces those five articles. This issue of AI Magazine include five articles covering subjects of current concern to the AI education community. This editorial introduces those five articles.
The first human versus computer no-limit Texas hold'em competition took place from April 24–May 8, 2015 at River's Casino in Pittsburgh, PA. In this article I present my thoughts on the competition design, agent architecture, and lessons learned. Several problematic hands from the competition are highlighted that reveal the most glaring weaknesses of the agent. The research underlying the agent is placed within a broader context in the AI research community, and several avenues for future study are mapped out.
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.