The field of AI has changed significantly in the past couple of years and will likely continue to do so. Driven by a desire to expose our students to relevant and modern materials, we conducted two surveys, one of AI instructors and one of AI practitioners. The surveys were aimed at gathering infor-mation about the current state of the art of introducing AI as well as gathering input from practitioners in the field on techniques used in practice. In this paper, we present and briefly discuss the responses to those two surveys.

We would like to understand the meaning of content on the web. Bit where should that meaning come from? From markup language annotations created by the authors of the content? Crowdsourced from readers of the content? Automatically extracted by machine learning algorithms? This talk investigates the possibilities.

There is great enthusiasm for the idea that massive amounts of data from online interactions of learners with material can lead to a rapid improvement cycle, driven by analysis of the data, experimentation, and intervention to do more of what works and less of what doesn't. This talk discusses techniques for working with massive amounts of data.

The software platforms that mediate online learning experiences are the common ground where learning science and computer science intersect. This panel will discuss the affordances of current online learning platforms and lessons learned in using them with students. The goal of the panel is to help learning scientists and computer scientists understand each others' needs and how they might be effectively addressed in these platforms. The panelists, who have experience creating/using these platforms and interacting with learning scientists, will discuss how current platforms for learning at scale might evolve to better serve the community.

The IPython Notebook is an interactive browser-based environment where you can combine code execution, text, mathematics, plots, and rich media into a single document. Originally designed for use as an electronic lab notebook for computational science, it is increasingly being used in teaching as well, and a rich ecosystem of open source plugins and extensions for teaching is growing around it. The first half of this hands-on workshop will introduce the Notebook and present examples of lessons and instructional materials built around it. In the second half, attendees will explore future directions for the Notebook as a teaching platform. For more information, please view our GitHub repository online at https://github.com/gvwilson/sigcse2014-ipython-workshop.

This talk covers techniques for analyzing data sets with up to trillions of examples with billions of features, using thousands of computers. To operate at this scale requires an understanding of an increasing complex hardware hierarchy (e.g. cache, memory, SSD, another machine in the rack, disk, a machine in another data center, ...); a model for recovering from inevitable hardware and software failures; a machine learning model that allows for efficient computation over large, continuously updated data sets; and a way to visualize and share the results.

Abstract The unification of two patterns both containing variables is a ubiquitous operation in logic programming and in many artificial intelligence applications. Thus, many texts present unification algorithms. Unfortunately, at least seven of these presentations are incorrect. The common error occurs when logic variables are represented as binding lists; implementations that destructively update variable cells do not manifest the error. This note gives the examples that uncover the error and presents a correction.