The Knoxville R Users Group is presenting a workshop on text analysis using R by Bob Muenchen. The workshop is free and open to the public. A description of the workshop follows. When analyzing text using R, it's hard to know where to begin. There are 37 packages available and there is quite a lot of overlap in what they can do.

The "virtuous circle" comprising RPA, machine learning and analytics was the central theme of this month's BotVisions webinar series. Joining me were Kelly Coupe, Principal Product Manager, and Abhijit Kakhandiki, VP of Products, to share their insights into how business users are integrating the execution capabilities of RPA with the cognitive capabilities of machine learning to take the business benefits of automation to the next level. While extremely good at executing specifically defined tasks, RPA tools are limited in the sense that they cannot adjust to new conditions or learn from experience. Machine learning, meanwhile, applies Artificial Intelligence (AI) capabilities to lend business context to the tasks executed by RPA systems, enabling the latter to make better decisions and be more productive. For example, RPA systems can effectively perform many tasks associated with loan origination or account management.

Ben Rossi writes in Information Age about the emergence of educational technology, and how colleges and universities could become more effective with Integrated Learning Systems by using them for more than regurgitating old styles of instruction on new equipment. He writes that ed tech should be more than just an innovative way of educational delivery, but part of the education itself by allowing students and teachers to create their own questions, answers and theories on a variety of elements on a given subject. Technology has the capacity for education to replace the currency of grades and test scores with imagination and creation in action, a necessity for an industry which spent more than $6 billion on teaching technology in 2015. Several colleges and universities are working to reform higher education into spaces for innovation and commercial development. The University of Connecticut, Arizona State University, and Princeton University are among a handful of schools encouraging students to find entrepreneurial niches and to take learning and career passions beyond the classroom.

What makes one intervention work in a school when another seemingly similar one falls flat? Increasingly detailed computer models of student behavior and learning may help researchers avoid such setbacks by better pinpointing interventions before taking them to schools. "In education research, I get a great idea, apply for funding, … then I spend a few months in schools taking time from students and teachers, and often find out it doesn't work," said Richard L. Lamb, an assistant professor of science education and educational measurement at Washington State University in Pullman. "That's great that we have that data," he said, "but it's not the most efficient way to do [research and development]." Instead, Mr. Lamb and colleagues are working to pair education technology and neuroscience to mimic how students learn in a classroom and provide an additional means of testing and honing interventions.

If you've never used an object-oriented programming language before, you'll need to learn a few basic concepts before you can begin writing any code. This lesson will introduce you to objects, classes, inheritance, interfaces, and packages. Each discussion focuses on how these concepts relate to the real world, while simultaneously providing an introduction to the syntax of the Java programming language. An object is a software bundle of related state and behavior. Software objects are often used to model the real-world objects that you find in everyday life.

On a summer day four years ago, a Stanford University computer-science professor named Andrew Ng held an unusual air show on a field near the campus. His fleet of small helicopter drones flew under computer control, piloted by artificial-intelligence software that could teach itself to fly after watching a human operator. By the end of the day, the copters were hot-dogging--flipping, rolling, even hovering upside down. It was a milestone for the field of "machine learning," the same area of artificial intelligence that lets Amazon recommend books based on a shopper's previous habits and helps Google tailor search results to a user's behavior. Mr. Ng and his team of graduate students showed that artificial-intelligence software could control one of the hardest-to-maneuver vehicles and keep it stable while flying at 45 miles an hour.

Free Online AI course, Berkeley's CS 188, offered through edX. Free Online AI course, Berkeley's CS 188, offered through edX.

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This lecture will cover recurrent neural networks, the key ingredient in the deep learning toolbox for handling sequential computation and modelling sequences. It will start by explaining how gradients can be computed (by considering the time-unfolded graph) and how different architectures can be designed to summarize a sequence, generate a sequence by ancestral sampling in a fully-observed directed model, or learn to map a vector to a sequence, a sequence to a sequence (of the same or different length) or a sequence to a vector. The issue of long-term dependencies, why it arises, and what has been proposed to alleviate it will be core subject of the discussion in this lecture. This includes changes in the architecture and initialization, as well as how to properly characterize the architecture in terms of recurrent or feedforward depth and its ability to create shortcuts or fast propagation of gradients in the unfolded graph. Open questions regarding the limitations of training by maximum likelihood (teacher forcing) and ideas towards towards making learning online (not requiring backprop through time) will also be discussed.

As the artificial intelligence space in Montreal heats up, a group of McGill student entrepreneurs are set to launch the McGill Artificial Intelligence Society. The McGill AI Society describes themselves as "a community of passionate students [who are] all about learning, practicing and sharing knowledge of the increasingly interesting field of AI." Led by Théo Szymkowiak, a third year McGill Computer Science student and former CTO of Fractal (McGill X-1 Cohort 2016), the McGill AI Society aims to provide introductory and advanced classes on Machine Learning and Artificial Intelligence where members will learn how to build robots, code up image classifiers, learn about speech recognition, and much more. McGill AI Society members will also have the opportunity to present their work and teach other students as well. This society is open to all McGill students from every faculty. Interested McGill students can register to be part of the McGill AI Society here.