The Turing Test in the Classroom

AAAI Conferences

This paper discusses the Turing Test as an educational activity for undergraduate students. It describes in detail an experiment that we conducted in a first-year non-CS course. We also suggest other pedagogical purposes that the Turing Test could serve.

Leveraging the Singularity: Introducing AI to Liberal Arts Students

AAAI Conferences

In recent years, the notion that computers and robots will attain superhuman levels of intelligence in the next few decades, ushering in a new "posthuman" era in evolutionary history, has gained widespread attention among technology enthusiasts, thanks in part to books such as Ray Kurzweil's The Singularity Is Near. This paper describes an introductory-level AI course designed to examine this idea in an objective way by exploring the field of AI as it currently is, in addition to what it might become in the future. An important goal of the course is to place these ideas within the broader context of human and cosmic evolution. The course is aimed at undergraduate liberal arts students with no prior background in science or engineering.

Robots Can Wear Multiple Hats in the Computer Science Curriculum at Liberal Arts Colleges

AAAI Conferences

Faculty at liberal arts colleges are often challenged to offer a quality education to their students, complete with opportunities for undergraduate research. To guard against a curriculum that is too theoretical, students want to see applications of their course work and tangible results of their efforts. Like all computer science educators, we want to attract students to our discipline. The use of robotics can often be part of the answer in each of these realms.

Using Science Fiction in Teaching Artificial Intelligence

AAAI Conferences

Many factors are blamed for the decreasing enrollments in computer science and engineering programs in the U.S., including the dot-com economic bust and the increase in the use of "offshore" programming labor. One major factor is also the lack of bold new vision and excitement about computer science, which thus results in a view of computer science as a field wedded to routine programming. To address this concern, we have focused on science fiction as a means to generate excitement about Artificial Intelligence, and thus in turn in Computer Science and Engineering. In particular, since the Fall of 2006, we have used science fiction in teaching Artificial Intelligence to undergraduate students at the University of Southern California (USC), in teaching activities ranging from an undergraduate upper division class in computer science to a semester-long freshman seminar for nonengineering students to micro-seminars during the welcome week. As an interdisciplinary team of scholar/instructors, our goal has been to use science fiction not only in motivating students to learn about AI, but also to use science fiction in understanding fundamental issues that arise at the intersection of technology and culture, as well as to provide students with a more creative and well-rounded course that provided a big picture view of computer science. This paper outlines the courses taught using this theme, provides an overview of our classroom teaching techniques in using science fiction, and discusses some of the lectures in more detail as exemplars. We conclude with feedback received, lessons learned and impact on both the computer science students and noncomputer-science (and non-engineering) students. "Science fiction like Star Trek is not only good fun, but serves a serious purpose, that of expanding human imagination" Physicist Stephen Hawking (from (Krauss 1995))

Computational Neuroscience Coursera


This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.