Teaching artificial intelligence (AI) is challenging. It is a fast moving field and therefore difficult to keep people updated with the state-of-the-art. Educational offerings for students are ever increasing, beyond university degree programs where AI education traditionally lay. In this paper, we present an experience report of teaching an AI course to business executives in the United Arab Emirates (UAE). Rather than focusing only on theoretical and technical aspects, we developed a course that teaches AI with a view to enabling students to understand how to incorporate it into existing business processes. We present an overview of our course, curriculum and teaching methods, and we discuss our reflections on teaching adult learners, and to students in the UAE.

By their nature, the composition of black box models is opaque. This makes the ability to generate explanations for the response to stimuli challenging. The importance of explaining black box models has become increasingly important given the prevalence of AI and ML systems and the need to build legal and regulatory frameworks around them. Such explanations can also increase trust in these uncertain systems. In our paper we present RICE, a method for generating explanations of the behaviour of black box models by (1) probing a model to extract model output examples using sensitivity analysis; (2) applying CNPInduce, a method for inductive logic program synthesis, to generate logic programs based on critical input-output pairs; and (3) interpreting the target program as a human-readable explanation. We demonstrate the application of our method by generating explanations of an artificial neural network trained to follow simple traffic rules in a hypothetical self-driving car simulation. We conclude with a discussion on the scalability and usability of our approach and its potential applications to explanation-critical scenarios.

We present a novel, real-time system for exploring harmonic spaces of musical styles, to generate music in collaboration with human performers utilizing gesture devices (such as the Kinect) together with MIDI and OSC instruments / controllers. This corpus-based environment incorporates statistical and evolutionary components for exploring potential flows through harmonic spaces, utilizing power-law (Zipf-based) metrics for fitness evaluation. It supports visual exploration and navigation of harmonic transition probabilities through interactive gesture control. These probabilities are computed from musical corpora (in MIDI format). Herein we utilize the Classical Music Archives 14,000+ MIDI corpus, among others. The user interface supports real-time exploration of the balance between predictability and surprise for musical composition and performance, and may be used in a variety of musical contexts and applications.

Abstracts of the invited talks presented at the 2013 FLAIRS conference. Talks include Robotics and Assistive Technologies: Their Emerging Role in Healthcare (Howard); Crossing the Data Science Chasm: The Perception of What Data Science Is and What It Needs to Be (Johnson); Who Are My Users and How I Can Help Them? The Quest of User-Adaptive Interaction (Conati); Neural Networks in Satellite-Based Atmospheric Remote Sensing (Chen); and The Use of Automated Reasoning for Software Verification of Microsoft Products (Bjorner).

Metric learning makes it plausible to learn distances for complex distributions of data from labeled data. However, to date, most metric learning methods are based on a single Mahalanobis metric, which cannot handle heterogeneous data well. Those that learn multiple metrics throughout the space have demonstrated superior accuracy, but at the cost of computational efficiency. Here, we take a new angle to the metric learning problem and learn a single metric that is able to implicitly adapt its distance function throughout the feature space. This metric adaptation is accomplished by using a random forest-based classifier to underpin the distance function and incorporate both absolute pairwise position and standard relative position into the representation. We have implemented and tested our method against state of the art global and multi-metric methods on a variety of data sets. Overall, the proposed method outperforms both types of methods in terms of accuracy (consistently ranked first) and is an order of magnitude faster than state of the art multi-metric methods (16x faster in the worst case).

We report on our development of a high-performance system for neural network and other signal processing applications. We have designed and implemented a vector microprocessor and packaged it as an attached processor for a conventional workstation.

We report on our development of a high-performance system for neural network and other signal processing applications. We have designed and implemented a vector microprocessor and packaged itas an attached processor for a conventional workstation.