The three competitions--(1) AAAI Mobile Robot, (2) AUVS Unmanned Ground Robotics, and (3) IJCAI RoboCup--were used in different years for an introductory undergraduate robotics course, an advanced graduate robotics course, and an undergraduate practicum course. Based on these experiences, a strategy is presented for incorporating competitions into courses in such a way as to foster intellectual maturation as well as learn lessons in organizing courses and fielding teams. The article also provides a classification of the major robot competitions and discusses the relative merits of each for educational projects, including the expected course level of computer science students, equipment needed, and costs. The sponsorship of such competitions ranges from local clubs of enthusiasts to large professional organizations, such as the American Association for Artificial Intelligence (AAAI), which sponsors the annual AAAI Mobile Robot Competition and Exhibition as part of its annual ...

We are excited to announce the general availability of Graphic Processing Unit (GPU) and deep learning support on Databricks! This blog post will help users get started via a tutorial with helpful tips and resources, aimed at data scientists and engineers who need to run deep learning applications at scale. Databricks now offers a simple way to leverage GPUs to power image processing, text analysis, and other machine learning tasks. Users can create GPU-enabled clusters with EC2 P2 instance types. Databricks includes pre-installed NVIDIA drivers and libraries, Apache Spark deployments configured for GPUs, and material for getting started with several popular deep learning libraries.

In this article, we introduce Mechanix, a sketch-based deployed tutoring system for engineering students enrolled in statics courses. Our system not only allows students to enter planar truss and free-body diagrams into the system, just as they would with pencil and paper, but our system also checks the student's work against a hand-drawn answer entered by the instructor, and then returns immediate and detailed feedback to the student. Students are allowed to correct any errors in their work and resubmit until the entire content is correct and thus all of the objectives are learned. Since Mechanix facilitates the grading and feedback processes, instructors are now able to assign more free-response questions, increasing teacher's knowledge of student comprehension. Furthermore, the iterative correction process allows students to learn during a test, rather than simply display memorized information.

The R language is best suited for statistical computations and visualization. Even if you do not have any prior experience in programming or statistical software, this Learning Path will help you get you up and running not only with the basics of R but also statistically modeling. This learning journey begin by introducing R and setting things up so that you are ready to go using RStudio, the associated IDE. Then, you will look at R as a programming language and see how the standard things are done in it. You will obtain a dataset and then learn how to clean the dataset.

CMRoboBits is a course offered at Carnegie Mellon University that introduces students to all the concepts needed to create a complete intelligent robot. In particular, the course focuses on the areas of perception, cognition, and action by using the Sony AIBO robot as the focus for the programming assignments. This course shows how an AIBO and its software resources make it possible for students to investigate and work with an unusually broad variety of AI topics within a single semester. While material presented in this article describes using AI-BOs as the primary platform, the concepts presented in the course are not unique to the AIBO and can be applied on different kinds of robotic hardware. Our experience runs across several generations of these four-legged robots, and we have met with increasing success every year.

The eight sections are (1) Artificial Intelligence (introductory material); (2) Problem-Solving (search and game playing); (3) Knowledge and Reasoning (propositional and predicate logic, inference techniques, knowledge representation); (4) Acting Logically (planning); (5) Uncertain Knowledge and Reasoning (probabilistic reasoning, Bayesian nets, decision-theoretic techniques); (6) Learning (inductive learning, neural nets, reinforcement learning); (7) Communicating, Perceiving, and Acting (natural language processing, computer vision, robotics); and (8) Conclusions (philosophical foundations and summary). What makes this textbook so good? First, it is remarkably comprehensive. In the preface, the authors suggest several alternative paths through the book that could serve as the basis of a one-semester course. At the University of Pittsburgh, my colleagues and I cover roughly the first half of the book (Sections 1-4) in the firstsemester introductory graduate AI course, covering most of Sections 5 through 8 in a second-semester course.

Column n The Educational Advances in Artificial Intelligence column discusses and shares innovative educational approaches that teach or leverage AI and its many subfields at all levels of education (K-12, undergraduate, and graduate levels). By restructuring the course into a format that was roughly half lecture and half small-group problem solving, I was able to significantly increase student engagement, their understanding and retention of difficult concepts, and my own enjoyment in teaching the class. The ACTIVE Center's design was based on research on the power of collaborative learning to promote student success and retention, particularly for women, underrepresented minorities, and transfer students, who benefit greatly from building stronger connections with their peers through shared active learning experiences (Zhao, Carini, and Kuh 2006; Rypisi, Malcolm, and Kim 2009; Kahveci, Southerland, and Gilmer 2006). The ACTIVE Center, a 40-student classroom, includes movable furniture (20 trapezoidal tables and 40 lightweight rolling chairs) that is typically grouped into 10 hexagonal table clusters but that can also be arranged into lecture-style rows, a boardroom or seminar-style rectangular layout, or individual pair-activity tables. The room also has an Epson Brightlink "smart projector" at the front of the room, four flat-panel displays (which can be driven centrally by the instructor's laptop or individually through HDMI ports), and 10 rolling 4 x 6 foot whiteboards for use during group problem-solving activities, as well as smaller, portable tabletop whiteboards.

Einfach nerdig, a Youtuber with currently only one video up, started a livestream of an AI learning to play "Super Mario Bros." 4 days ago. It's still running, and watching it is amazing. The AI, MarI/O, comes courtesy of creator SethBling, who despite his own huge following, isn't the one streaming the training session. The account streaming the video has disabled embedding, but you can watch it learn to play the game here on YouTube. SethBling, a world-record holder for "Super Mario World" speedruns, previously trained the MariI/O AI to play "Super Mario World" by feeding it footage of his own gameplay.

At Knowm, we are building a new and exciting type of computer processor to accelerate machine learning (ML) and artificial intelligence applications. The goal of Thermodynamic-RAM (kT-RAM) is to run general ML operations, traditionally deployed to CPUs and GPUs, to a physically-adaptive analog processor based on memristors which unites memory and processing. If you haven't heard yet, we call this new way of computing "AHaH Computing", which stands for Anti-Hebbian and Hebbian Computing, and it provides a universal computing framework for in-memory reconfigurable logic, memory, and ML. While we have shown a long time ago that AHaH Computing is capable of solving problems across many domains of ML, we only recently figured out how to use the kT-RAM instruction set and low precision/noisy memristors to build supervised and unsupervised compositional (deep) ML systems. Our method does not require the propagation of error algorithm (Backprop) and is easy to attain with realistic analog hardware, including but not limited to memristors.

R B. Abhyankar Emphasizing theory and implementation issues more than specific applications and Prolog programming techniques, Computing with Logic Logic Programming with Prolog (The Benjamin Cummings Publishing Company, Menlo Park, Calif., 1988, 535 pp., $27 95) by David Maier and David S. Warren, respected researchers in logic programming, is a superb book Offering an in-depth treatment of advanced topics, the book also includes the necessary background material on logic and automatic theorem proving, making it self-contained. The only real prerequisite is a first course in data structures, although it would be helpful if the reader has also had a first course in program translation. The book has a wealth of exercises and would make an excellent textbook for advanced undergraduate or graduate students in computer science; it is also appropriate for programmers interested in the implementation of Prolog The book presents the concepts of logic programming using theory presentation, implementation, and application of Proplog, Datalog, and Prolog, three logic programming languages of increasing complexity that are based on horn clause subsets of propositional, predicate, and functional logic, respectively This incremental approach, unique to this book, is effective in conveying a thorough understanding of the subject The book consists of 12 chapters grouped into three parts (Part 1 chapters 1 to 3, Part 2. chapters 4 to 6, and Part 3 chapters 7 to 12), an appendix, and an index The three parts, each dealing with one of these logic programming languages, are organized the same First, the authors informally present the language using examples; an interpreter is also presented. Then the formal syntax and semantics for the language and logic are presented, along with soundness and completeness results for the logic and the effects of various search strategies Next, they give optimization techniques for the interpreter Each chapter ends with exercises, brief comments regarding the material in the chapter, and a bibliography Chapter I presents top-down and bottom-up interpreters for Proplog Chapter 2 offers a good discussion of the related notions: negation as failure, closed-world assumption, minimal models, and stratified programs Chapter 3 considers clause indexing and lazy concatenation as optimization techniques for the Proplog interpreter in chapter 1 Chapter 4 explains the connection between Datalog and relational algebra. Chapter 5 contains a proof of Herbrand's theorem for predicate logic.