We consider a scenario where an agent has multiple available strategies to explore an unknown environment. For each new interaction with the environment, the agent must select which exploration strategy to use. We provide a new strategy-agnostic method that treat the situation as a Multi-Armed Bandits problem where the reward signal is the diversity of effects that each strategy produces. We test the method empirically on a simulated planar robotic arm, and establish that the method is both able discriminate between strategies of dissimilar quality, even when the differences are tenuous, and that the resulting performance is competitive with the best fixed mixture of strategies.

Reinforcement learning approaches have long appealed to the data management community due to their ability to learn to control dynamic behavior from raw system performance. Recent successes in combining deep neural networks with reinforcement learning have sparked significant new interest in this domain. However, practical solutions remain elusive due to large training data requirements, algorithmic instability, and lack of standard tools. In this work, we introduce LIFT, an end-to-end software stack for applying deep reinforcement learning to data management tasks. While prior work has frequently explored applications in simulations, LIFT centers on utilizing human expertise to learn from demonstrations, thus lowering online training times. We further introduce TensorForce, a TensorFlow library for applied deep reinforcement learning exposing a unified declarative interface to common RL algorithms, thus providing a backend to LIFT. We demonstrate the utility of LIFT in two case studies in database compound indexing and resource management in stream processing. Results show LIFT controllers initialized from demonstrations can outperform human baselines and heuristics across latency metrics and space usage by up to 70%.

A recent article in Bloomberg magazine called data science "America's hottest job." In response to increasing demand by industry, government and academia for highly trained data scientists, the University of Virginia's Data Science Institute is launching an online version of its Master of Science in Data Science program next summer. Through a collaboration with Noodle Partners, a company that provides online education management support, the degree can be earned entirely online, and will mirror the curriculum of the Data Science Institute's residential M.S.D.S. program. Currently, 49 students are enrolled in UVA's residential program and 20 more are working toward joint MBA/M.S. in Data Science degrees. The online M.S.D.S. program initially will enroll about 30 students, and that number is likely to grow each semester as the program modestly expands.

Amazon is eager to get more bright minds working on voice technology, so it's expanding the Alexa Fellowship program to 14 more universities, including Massachusetts Institute of Technology, Texas A&M and Cambridge. The fellowships are financed through Amazon's $200 million Alexa Fund. Other schools joining the roster include University of Texas at Austin, Dartmouth, Arizona State and the International Institute of Information Technology in Hyderabad, India. The latest additions take the number of schools in the program to 18, joining the previous quartet of Carnegie Mellon, University of Southern California, Johns Hopkins and Waterloo. The program comprises two different fellowships: Alexa Graduate Fellowship and Alexa Innovation Fellowship.

English-speaking artificial intelligence (AI) robots will be helping out in some 500 Japanese classrooms from next year as the country seeks to improve its English skills, particularly among children and teachers. The education ministry is planning a pilot project costing around 250 million yen (US$227,000) to improve Japanese students' notoriously weak oral and written English, an official said. "AI robots already on the market have various functions. For example, they can check the pronunciation of each student's English, which is difficult for teachers to do," said the official in charge of international education, who asked not to be named. AI robots "are just one example of the trial and we are planning other measures" such as using tablet apps and having online lessons with native speakers, he said.

Researchers at the University of Wisconsin-Madison have built a robot, named Minnie, to serve as a reading buddy to middle school kids, and Minnie's new friends grew more excited about books and more attached to the robot over two weeks of reading together. "After one interaction, the kids were generally telling us that, sure, it was nice to have someone to read with," says Joseph Michaelis, a UW-Madison graduate student studying educational psychology. "But by the end of two weeks, they're talking about how the robot was funny and silly and afraid, and how they'd come home looking forward to seeing it again." Michaelis and computer sciences professor Bilge Mutlu published their work with Minnie on Wednesday (Aug. Research shows that social learning -- pairing up with a peer to complete math problems or read a chapter in a textbook -- is a powerful way to help students develop skills and interests, according to Michaelis.

One of the questions I get asked most frequently is how I so quickly changed from a "plain old software engineer" to "certified Data Scientist" as quickly as I did. While I do plan to write a book/shoot a video on the topic. In the meantime, enjoy this video from Siraj Raval where he shows the techniques he uses to study machine learning. That includes living a healthy lifestyles, optimizing your learning environment, creating a personalized learning path, prioritizing effectively, and being an active learner. He demos the FAST technique, which you can use to help learn faster and more efficiently.

Earlier this year, I witnessed a scene out of science fiction, had it not been reality. A video shown at the NewSchools Venture Fund annual conference showed a young African-American girl answering a teacher's questions with confidence, enthusiastic to get them right. The girl was real, but the teacher was not. She was a virtual instructor named "ALEX," powered by Artificial Intelligence (AI). The virtual being was programmed to use an African-American dialect.

The University of Hong Kong (HKU) announced that it had signed a memorandum of understanding (MoU) with Microsoft Hong Kong in May 2018 to form an Artificial Intelligence (AI) research partnership to incubate talent and encourage breakthroughs in various sectors by accelerating scientific progress and enabling data-driven research. Commenting on the memorandum, Professor Andy Hor, Vice-President, and Pro-Vice-Chancellor (Research), University of Hong Kong stated HKU was glad to join hands with Microsoft in this AI partnership to enable the professionals and researchers at HKU to apply AI in research projects. Prof Andy noted that Healthcare and Fintech are the key industries in Hong Kong and that through this collaboration with the company, HKU students have the chance to equip themselves with knowledge of Data Science and AI. This will enable researchers to accelerate their professional development, she added. Cally Chan, General Manager of Microsoft Hong Kong said that the company's mission is to empower their partners in different industries to take a significant leap forward in accelerating digital transformation and achieve more.

It's hard to make a case for the "sudden" development of something that has been a formal topic of research for more than 60 years. Attending presentations on Artificial Intelligence (AI) at three recent conferences and following that up with extensive reading does, however, suggest that all of us engaged in teaching-training-learning need to be familiar with recent AI developments and projects that are rapidly changing our world. The April 2018 Unconference for Dreamers, Doers, and Drivers Shaping the Future of Learning, held on an Arizona State University campus and previously discussed here on the Top of Mind blog by The Tambellini Group Senior Analyst Alex Freeman, paid ample attention to AI. Developments in all three areas are continuing to open doors to potential collaborations between learning facilitators, learners, and machines in ways that may foster more dynamic, engaging, and results-oriented learning experiences for those we serve in our lifelong-learning environments. Examples cited by Keller include customizable learning experiences, student-persistence prediction, unbiased grading systems, more sophisticated and useful teacher-student performance analytics, suggested learning paths, better ways of matching students and teachers in learners' moments of need, and detecting fraudulent activity in the learning process. Unconference participants walked away from Keller's presentation and their subsequent explorations of the topic with a commitment to "design blueprints for an AI-enabled virtual assistant for teaching, learning and advising, factoring in expanded functionalities over the next 5, 10, and 20 years," the ShapingEDU "10 Actions to Shape the Future of Education" communique confirms.