Much has been made of the so-called'robot revolution' with Artificial Intelligence (AI) technologies forecast to transform many industries beyond all recognition. Once the stuff of science fiction movies, these emerging trends are fast becoming serious discussion points for organisations seeking to save time and money through automated processes and data crunching. For the UK public sector, tasked with protecting critical services on limited budgets whilst delivering value for money to taxpayers, such initiatives appear to be an obvious benefit. This week for the first time we have heard a prediction around what a truly digital public sector might look like. A new research paper from the Reform think tank has predicted that around 250,000 public sector jobs could be taken by robots in the next 15 years.

Successful Student has compiled the 25 Best Artificial Intelligence Colleges in the United States. Artificial Intelligence (AI), also known as machine learning, is a discipline within computer science. Artificial Intelligence is usually conceived of as doing more than just computing numbers (such as a calculator), but is more conceptual in nature (such as describing subjective qualities, or giving meanings to different contexts). An example of AI would be speech recognition and communicating, such as Apple's Siri, or Amazon's Alexa. Amazon has announced three new AI tools for anyone wanting to build apps on Amazon Web Services: Amazon Lex, Amazon Polly, and Amazon Rekognition. According to Amazon "This frees developers to focus on defining and building an entirely new generation of apps that can see, hear, speak, understand, and interact with the world around them." For those interested in developing apps, see our 20 Best App Development Colleges article. Google, Facebook, Amazon, Apple and Microsoft are all working on AI. Facebook's FAIR (Facebook Artificial Intelligence Research) program engages with academia to assist in solving long term problems in AI. Facebook is hiring AI experts around the world to assist in their project.

Latent tree analysis seeks to model the correlations amonga set of random variables using a tree of latent variables. It was proposed as an improvement to latent class analysis—a method widely used in social sciences and medicine to identify homogeneous subgroups in a population. It provides new and fruitful perspectives on a number of machine learningareas, including cluster analysis, topic detection, and deep probabilistic modeling. This paper gives an overview of the research on latent tree analysis and various ways it is used inpractice.

I present a robotics localization challenge based on the inexpensive Neato XV robotic vacuum cleaner platform. The challenge teaches skills such as computational modeling, probabilistic inference, efficiency vs. accuracy tradeoffs, debugging, parameter tuning, and benchmarking of algorithmic performance. Rather than allowing students to pursue any localization algorithm of their choosing, here, I propose a challenge structured around the particle filter family of algorithms. This additional scaffolding allows students at all levels to successfully implement one approach to the challenge, while providing enough flexibility and richness to enable students to pursue their own creative ideas. Additionally, I provide infrastructure for automatic evaluation of systems through the collection of ground truth robot location data via ceiling-mounted location tags that are automatically scanned using an upward facing camera attached to the robot. The robot and supporting hardware can be purchased for under $400 dollars, and the challenge can even be run without any robots at all using a set of recorded sensor traces.

The 13th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming (CPAIOR 2016), was held in Banff, Canada, May 29 - June 1, 2016. In order to trigger exchanges between the constraint programming and the operations research community, CPAIOR was co-located with CORS 2016, the Canadian Operational Research society's conference.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2017 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs.

Prerequisite relations among concepts play an important role in many educational applications such as intelligent tutoring system and curriculum planning. With the increasing amount of educational data available, automatic discovery of concept prerequisite relations has become both an emerging research opportunity and an open challenge. Here, we investigate how to recover concept prerequisite relations from course dependencies and propose an optimization based framework to address the problem. We create the first real dataset for empirically studying this problem, which consists of the listings of computer science courses from 11 U.S. universities and their concept pairs with prerequisite labels. Experiment results on a synthetic dataset and the real course dataset both show that our method outperforms existing baselines.

The Robotics Program at Oregon State University has beenrunning an NSF-funded summer Research Experiences forUndergraduates (REU) site since 2014. Over twenty studentsper year (on average) have participated in the site, spendingten weeks embedded in the OSU Robotics Program. Our mainfocus with this REU Site is to give the participants a com-plete research experience, from problem definition to the fi-nal presentation of results, "in miniature". Our secondary ed-ucational objectives are: 1) Teach basic non-technical skillsneeded for graduate work, such as time management and lit-erature review, 2) Provide details on how to apply to gradu-ate school and for funding, 3) Clarify what we look for in agraduate student, and 4) Detail what to expect from the grad-uate student experience. In this paper, we describe the over-all structure of the participants’ summer experience, outlinesome of the training materials that we use, describe the moti-vations for our approach, and discuss the lessons that we havelearned after running the program for a number of years.

ARTY is a week-long program for middle school students to teach them programming of robots and allow them to express themselves artistically. It was started in 2013 and ran its fourth edition in 2016. We describe the ideas behind the inception of this program, its curriculum, our experiences during the 2016 workshop and challenges/future directions for the program. Our primary intent in this paper is to convey the program curriculum and its design, including the way in which robots can be viewed as vehicles for artistic expression. Some results from a brief attitudinal survey that was administered before and after the workshop are also included along with a discussion of outcomes assessment and issues.

We contribute a learning from demonstration approach for robots to acquire skills from multi-modal high-dimensional data. Both latent representations and associations of different modalities are proposed to be jointly learned through an adapted variational auto-encoder. The implementation and results are demonstrated in a robotic handwriting scenario, where the visual sensory input and the arm joint writing motion are learned and coupled. We show the latent representations successfully construct a task manifold for the observed sensor modalities. Moreover, the learned associations can be exploited to directly synthesize arm joint handwriting motion from an image input in an end-to-end manner. The advantages of learning associative latent encodings are further highlighted with the examples of inferring upon incomplete input images. A comparison with alternative methods demonstrates the superiority of the present approach in these challenging tasks.