After a summer of test runs, the full version of Minecraft: Education Edition will officially launch on November 1st. When it goes live, the service will require a 5 yearly membership per user or a district-wide license, but the Early Access edition is still free until November. According to the MinecraftEdu team, over 35,000 students and teachers around the world have been playing around in Minecraft's sandbox since the program went live at the beginning of the summer. With the official release, the team has built out a few new education-focused features like a "Classroom Mode" that offers a top-down look at the Minecraft world via a companion app. In the app, teachers can manage world settings, talk to students in-game, give out items or teleport their kids around the map from a single interface.

A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed. To enable persistent sensing and estimation in such a setting, it is beneficial to have a time-varying underlying environmental model. Here we present a planning and learning method that enables an autonomous marine vehicle to perform persistent ocean monitoring tasks by learning and refining an environmental model. To alleviate the computational bottleneck caused by large-scale data accumulated, we propose a framework that iterates between a planning component aimed at collecting the most information-rich data, and a sparse Gaussian Process learning component where the environmental model and hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. Our simulations with ground-truth ocean data shows that the proposed method is both accurate and efficient.

We consider sequential or active ranking of a set of n items based on noisy pairwise comparisons. Items are ranked according to the probability that a given item beats a randomly chosen item, and ranking refers to partitioning the items into sets of pre-specified sizes according to their scores. This notion of ranking includes as special cases the identification of the top-k items and the total ordering of the items. We first analyze a sequential ranking algorithm that counts the number of comparisons won, and uses these counts to decide whether to stop, or to compare another pair of items, chosen based on confidence intervals specified by the data collected up to that point. We prove that this algorithm succeeds in recovering the ranking using a number of comparisons that is optimal up to logarithmic factors. This guarantee does not require any structural properties of the underlying pairwise probability matrix, unlike a significant body of past work on pairwise ranking based on parametric models such as the Thurstone or Bradley-Terry-Luce models. It has been a long-standing open question as to whether or not imposing these parametric assumptions allows for improved ranking algorithms. For stochastic comparison models, in which the pairwise probabilities are bounded away from zero, our second contribution is to resolve this issue by proving a lower bound for parametric models. This shows, perhaps surprisingly, that these popular parametric modeling choices offer at most logarithmic gains for stochastic comparisons.

Approximately 8 of the 319 million people in the United States read the Wall Street Journal, good for 2 percent of the population. If you look at the language -- standardized English -- being fed into many natural language processing units, it's based on the language of that 2 percent. And many machines literally use the venerable, business-focused newspaper to better understand English language. It might seem like an obvious choice. Standardized English is taught in schools, it's used in legal documents, and it sets the basis for formal society.

LinkedIn wants to become more useful to workers by adding personalized news feeds, helpful messaging "bots" and recommendations for online training courses, as the professional networking service strives to be more than just a tool for job-hunting. The new services will arrive just as LinkedIn itself gains a new boss -- Microsoft -- which is paying 26 billion to acquire the Silicon Valley company later this year. LinkedIn said the new features, which it showed off to reporters Thursday, were in the works before the Microsoft takeover was announced in June. But LinkedIn CEO Jeff Weiner said his company hopes to incorporate some of Microsoft's technology as it builds more things like conversational "chat bots," or software that can carry on limited conversations, answer questions and perform tasks like making reservations. Chat bots are a hot new feature in the consumer tech world, where companies like Facebook, Apple and Google are already racing to offer useful services based on artificial intelligence. As a first step, LinkedIn says it will soon introduce a bot that could help someone schedule a meeting with another LinkedIn user, by comparing calendars and suggesting a convenient time and meeting place.

Earlier this year, the president included in his fiscal 2017 budget proposal a 4 billion request to make computer science a new "basic skill." The Education Department is prioritizing coding programs through a new experiment that allows students to tap federal financial aid to help pay for them. The private sector is prioritizing the issue as well, pouring hundreds of millions of dollars into increased access to computers, computer science and broadband internet. Even toy manufacturers are getting in on it, dreaming up toys aimed at teaching children as young as 3 to think like a computer coder.

On the island of Santorini, Greece, a group of AIs has been facing off in an epic battle of Doom. This is VizDoom, a contest born from one man's idea: To improve the state of artificial intelligence by teaching computers the art of fragging. That simple notion then spiraled into a battle between tech giants, universities and coders. Over the past few months they've all been honing their bots (known as "agents"), building up to one, final death match. Okay, it was a lot more than one match.

IBM Research announced a multi-year collaboration with the Department of Brain & Cognitive Sciences at MIT to advance the scientific field of machine vision, a core aspect of artificial intelligence. The new IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension's (BM3C) goal will be to develop cognitive computing systems that emulate the human ability to understand and integrate inputs from multiple sources of audio and visual information into a detailed computer representation of the world that can be used in a variety of computer applications in industries such as healthcare, education, and entertainment. The BM3C will address technical challenges around both pattern recognition and prediction methods in the field of machine vision that are currently impossible for machines alone to accomplish. For instance, humans watching a short video of a real-world event can easily recognize and produce a verbal description of what happened in the clip as well as assess and predict the likelihood of a variety of subsequent events, but for a machine, this ability is currently impossible. Beginning in September 2016 in Cambridge, the BMC3 collaboration will bring together leading brain, cognitive, and computer scientists to conduct research in the field of unsupervised machine understanding of audio-visual streams of data, using insights from next-generation models of the brain to inform advances in machine vision.

Billions of dollars are being poured into building sophisticated artificial intelligence algorithms. But they could all be snatched away if even a tiny door is left open. Researchers have shown that given access to only an API, a way to remotely use software without having it on your computer, it's possible to reverse-engineer machine learning algorithms with up to 99% accuracy. In the real world, this would mean being able to steal AI products from companies like Microsoft and IBM, and use them for free. Small companies built around a single machine learning API could lose any competitive advantage.

We eat, breathe and sleep artificial intelligence; it truly is running the world as we know it! Now, it's the turn of education; an industry that has been somewhat left behind in recent years. This however, is set to change. We've all read countless articles on how artificial intelligence is stealing our jobs, so surely something must be done to counteract this? Artificial intelligence and automation are now beginning to enter the workplace at a more graduate level and so it's about time that universities rethink what they're doing and how they're doing it; that is to equip graduates with the ability to work effectively alongside artificial intelligence.