Now that a new school year is upon us, it's the perfect time to get your dorm room set up with some sweet home tech. To make the decision easier, Amazon has discounted three of its best-selling Echo devices: the Fire TV with 4K Ultra HD is $40 (43 percent off), the 2nd-gen Echo is $85 (15 percent off), and the Fire TV Cube is $90 (25 percent off). You don't need to be a math major to see that those are fantastic prices. The Fire TV stick, Amazon's pendant-style streaming device, is compatible with 4K Ultra HD and High Dynamic Range TV. You'll be able to search through all your apps, from streaming services to social networks, with just your voice via Alexa, built in to the included remote. You can also connect to an existing Echo for hands-free control.

Artificial Intelligence means a lot of things to a lot of different people. It's difficult to nail down what exactly it is; it's easier to start with what it is not. AI is not a Jetsons-style robot that lives in your home and does all your domestic duties for you. It won't cook your bacon and eggs, nor will it serve you breakfast in the morning. AI also is not a Terminator-type robot that is intent on destroying the human race.

This new study is much more interested in the benefits of robot-assisted long-term coaching. As well as offering the benefit of bypassing any baggage associated with human interactions, this autonomous robot intervention allows for a system that supports and augments any work with other clinicians and teachers. Scassellati suggests further longer-term study will be necessary to better understand the benefits of the program but these results from just one month of work bodes well for future robot-assisted interventions helping autistic children develop social skills.

Artificial intelligence promises to make hiring an unbiased utopia. Employee referrals, a process that tends to leave underrepresented groups out, still make up a bulk of companies' hires. Recruiters and hiring managers also bring their own biases to the process, studies have found, often choosing people with the "right-sounding" names and educational background. Across the pipeline, companies lack racial and gender diversity, with the ranks of underrepresented people thinning at the highest levels of the corporate ladder. Fewer than five per cent of chief executive officers at Fortune 500 companies are women, and that number will shrink further in October when Pepsi CEO Indra Nooyi steps down.

Google appears to be preparing an AI-powered fitness coach for WearOS as it continues its mission to revitalise the platform. Fitness remains a key reason why people buy smartwatches. Beyond that, they're still really just an expensive place to see our smartphone notifications. WearOS' main competitor, the Apple Watch, has produced some clever features with regards to fitness. Among the most impressive is the ability to tap some gym equipment to bring up the relevant info and tracking for that specific machine.

For years, multiple-choice tests have allowed scanners to score results without human intervention. Now technology is coming directly into the exam hall. Coursera has patented a system to take images of students and verify their identity against scanned documents. There are plagiarism detectors that can scan essay answers and search the Web--or the work of other students--to identify copying. Webcams can monitor exam locations to spot malpractice.

Many companies are experimenting with AI on a small scale, and a few have made a commitment that their organizations will be "AI first" or "AI-driven." But what does this mean? What is AI doing or leading, and, in particular, what is the role of leadership in making organizations AI-driven? We see a lot of confusion around opportunity and action. In the 2018 Deloitte Global Human Capital Trends survey and report of business and HR leaders, 72% indicated that AI, robots, and automation are important -- but only 31% felt their organizations were prepared to address strategy to implement these technologies.

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.