TutorSpace, as it has been named by multimedia analytics scientists at Xerox Research Centre India, is intended to turn instructional videos into "next-generation" textbooks. As Om Deshmuk, a Xerox senior research scientist in multimedia analytics, explained in a video of the project, right now, the amount of instructional content available in video form online can be overwhelming to students. Through machine learning TutorSpace also makes it possible to find content tailored to a student's learning patterns. Now Xerox has licensed TutorSpace to education technology company Impartus for use in its e-learning products.

Deep Q-networks (DQNs) [1] have reignited interest in neural networks for reinforcement learning, proving their abilities on the challenging Arcade Learning Environment (ALE) benchmark [2]. The ALE is a reinforcement learning interface for over 50 video games for the Atari 2600; with a single architecture and choice of hyperparameters the DQN was able to achieve superhuman scores on over half of these games. The original work has now been superseded with several advancements, several of which can be found on GitHub. As training on the ALE can take over a week on a GPU, the code is also set up to learn how to play a simpler game of catch in a couple of hours on a CPU. Most recent deep learning research has focused around supervised learning, which involves finding a mapping from input data \(x\) to target data \(y\).

My mother enrolled in a high school physics course in 1968. This wouldn't be especially notable except for the fact that it was the first time in her school's history that girls were permitted to take physics. In prior years, boys were allowed to study physics while girls were expected to enroll in home economics. While my mother acknowledges she was not destined for a career in physics, there were women of her generation that did aspire to enter the scientific field: Dr. France Córdova, Director of the National Science Foundation; Shirley Ann Jackson, President of Rensselaer Polytechnic Institute; and Persis Drell, former director of the SLAC National Accelerator Laboratory, are just a few of the women who not only studied physics, but excelled and built their careers in the field despite the barriers of their generation. Women have progressed significantly since 1968.

One day in January, Eric Wilson dashed off a message to the teaching assistants for an online course at the Georgia Institute of Technology. "I really feel like I missed the mark in giving the correct amount of feedback," he wrote, pleading to revise an assignment. Thirteen minutes later, the TA responded. "Unfortunately, there is not a way to edit submitted feedback," wrote Jill Watson, one of nine assistants for the 300-plus students. Last week, Mr. Wilson found out he had been seeking guidance from a computer.

If you enjoy Google Play Music's recommendations based on what you listen to, you can thank researcher Douglas Eck. The former University of Montreal computer science professor used machine learning principles on that project, and is now experimenting with it to see if he can teach computers to make art and music on their own. Eck, along with a handful of Google Brain team members, is gearing up to launch Magenta on June 1. The project will involve the use of Google's open-source AI platform TensorFlow to create algorithms that can generate music. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May.

Most Machine Learning courses are given from the perspective of a researcher/academic and focus on the theory and mathematics of the machine learning models. This workshop takes the perspective of learning by working on real machine learning problems using open source tools and platforms. We'll go all the way from data preparation to the integration of predictive models in applications and their deployment in production. "Just like development where you don't need to know a thing about computability or big-O notation to write code and ship useful and reliable software, you can work machine learning problems end-to-end without a background in statistics, probability and linear algebra." The workshop is agnostic and features the best open source Python libraries (Pandas, scikit-learn, SKLL), APIs and ML-as-a-Service platforms (Microsoft Azure ML & Cortana Intelligence Suite, Amazon ML, BigML) for developers getting started in Machine Learning.

We study an online learning framework introduced by Mannor and Shamir (2011) in which the feedback is specified by a graph, in a setting where the graph may vary from round to round and is \emph{never fully revealed} to the learner. We show a large gap between the adversarial and the stochastic cases. In the adversarial case, we prove that even for dense feedback graphs, the learner cannot improve upon a trivial regret bound obtained by ignoring any additional feedback besides her own loss. In contrast, in the stochastic case we give an algorithm that achieves $\widetilde \Theta(\sqrt{\alpha T})$ regret over $T$ rounds, provided that the independence numbers of the hidden feedback graphs are at most $\alpha$. We also extend our results to a more general feedback model, in which the learner does not necessarily observe her own loss, and show that, even in simple cases, concealing the feedback graphs might render a learnable problem unlearnable.

During Kuwait's third National Competition for Robotics held at the Nusaibah Bet Kaab School for girls on April 18, one question continually came to the fore: can robotics play a role in improving education? Robotics competitions offer a chance to encourage students to build their own solutions to real-world problems using science and maths. Building a robot is a meticulous and difficult process that requires collaborative efforts from all team members. "The role of these competitions is important to develop the abilities and potential of students and to encourage innovation in scientific sectors, which might see great leaps today," said Kuwait's minister of education and higher education Bader Al-Issa. Around 20 projects developed by elementary and middle school students were exhibited at the competition.

The late seventies are considered as prehistoric times by most data scientists. Yet it was the beginning of a new era, with people getting their first personal computer, or at least programmable calculators like the one pictured below. The operating system was called DOS, and later became MSdos, for Microsoft Disk Operating System. You could use your TV set as a monitor, and tapes and a tape recorder (then later floppy disks) to record data. Memory was limited to 64KB.

Meet Jill Watson, a first-time teaching assistant at Georgia Tech assigned to moderate an online forum for a computer science class. Jill was 1 of 9 TAs assigned to help answer questions about coursework and projects from the 300 students enrolled in the advanced course. During the first few weeks in January, Jill really struggled. This was Knowledge-Based Artificial Intelligence, after all, a course with the goal to "build AI agents capable of human-level intelligence and gain insights into human cognition." It was also a requirement for graduate students to earn their master's degree. It's no surprise then that she needed some coaching, especially since feedback is so critical to student success.