After Microsoft signed a deal to test Windows 10 on Xiaomi devices in 2015 and then Xiaomi bought a trove of patents to help run other Microsoft services on its devices in 2016, today the two companies announced another chapter in its collaboration. Xiaomi and Microsoft have signed a Strategic Framework Memorandum of Understanding (MoU) to work more closely in the areas of cloud computing, AI (including Microsoft's Cortana business) and hardware. To date, Xiaomi has largely focused its mobile phone strategy in Asia Pacific, where Gartner revealed yesterday that it (and Huawei) were the only two vendors to increase their market shares at a time of general decline. This deal could point to how Xiaomi is looking to raise its game in the West, specifically in the US, especially as it eyes up an IPO that has been reported to potentially value the company at $100 billion. On the side of Microsoft, it's particularly interesting given that the company has largely pulled back on a lot of its hardware efforts, and has visibly had some major stumbles in this area especially in mobile -- most recently with its failure to take on and grow the Nokia mobile business and Windows Mobile. Understanding that mobile isn't an area that Microsoft can quite step away from altogether, it seems that the company is going to have one more go now on with a different partner (one that is still on the rise) and a different approach.

This paper proposes an agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for students learning performance evaluation and educational applications, and the proposed agent is according to the response data from a conventional test and an item response theory. First, we apply a GS-based parameter estimation mechanism to estimate the items parameters according to the response data, and then to compare its results with those of an IRT-based Bayesian parameter estimation mechanism. In addition, we propose a static-IRT test assembly mechanism to assemble a form for the conventional test. The presented FML-based dynamic assessment mechanism infers the probability of making a correct response to the item for a student with various abilities. Moreover, this paper also proposes a novel PFML learning mechanism for optimizing the parameters between items and students. Finally, we adopt a K-fold cross validation mechanism to evaluate the performance of the proposed agent. Experimental results show that the novel PFML learning mechanism for the parameter estimation and learning optimization performs favorably. We believe the proposed PFML will be a reference for education research and pedagogy and an important co-learning mechanism for future human-machine educational applications.

We consider the task of learning the parameters of a {\em single} component of a mixture model, for the case when we are given {\em side information} about that component, we call this the "search problem" in mixture models. We would like to solve this with computational and sample complexity lower than solving the overall original problem, where one learns parameters of all components. Our main contributions are the development of a simple but general model for the notion of side information, and a corresponding simple matrix-based algorithm for solving the search problem in this general setting. We then specialize this model and algorithm to four common scenarios: Gaussian mixture models, LDA topic models, subspace clustering, and mixed linear regression. For each one of these we show that if (and only if) the side information is informative, we obtain parameter estimates with greater accuracy, and also improved computation complexity than existing moment based mixture model algorithms (e.g. tensor methods). We also illustrate several natural ways one can obtain such side information, for specific problem instances. Our experiments on real data sets (NY Times, Yelp, BSDS500) further demonstrate the practicality of our algorithms showing significant improvement in runtime and accuracy.

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The Papers Please movie finished up its paperwork and nabbed a release date, Overwatch had puppies play Capture the Flag, Warcraft III is hosting a tournament, you'll soon (probably) be able to buy Geralt's bathtub figurine, and Final Fantasy XV is doing a Half-Life crossover. Yes, it's certainly been a weird week in video game news. Let's recap February 19 to 23. As far as bang-for-your-buck goes, the new Humble Classics Return Bundle (that's a mouthful) might be the best the company's ever featured. More than the average gets you Shadowrun: Hong Kong, Age of Wonders III, Xenonauts, and our 2014 Game of the Year Wasteland 2. Pay more than $15 and Humble throws in last year's Torment: Tides of Numenera and Dreamfall Chapters too.

GraphGrail Ai, the world's first AI-based natural language processing platform with a DApps marketplace is passing through its TGE stage and has already garnered the acclaim of several prominent rating agencies with high rankings. Such results were made possible thanks to the extensive experience that the GraphGrail Ai development team has in implementing AI solutions for businesses and government agencies. To demonstrate and consolidate the experience of the GraphGrail Ai team for our followers, we have compiled a list of prominent cases that the development team had participated in over the last few years prior to undertaking the implementation of their solutions on blockchain systems. The GraphGrail Ai team was involved in the development and launch of a service that searches for extremist statements based on the legislation of the Russian Federation for the Rostov Center for Forensic Expertise. The system was used extensively and had successfully detected dangerous publications.

KUMIKO HIRANO has noticed a disquieting change when she goes to her neighbourhood konbini, one of Japan's ubiquitous convenience stores. "No one is around and I have to use a loud voice to get someone to serve me," says the 48-year-old worker in Tokyo. This might not seem a big problem, but Japan prides itself on the standard of customer service, which approaches the level of bespoke attention elsewhere. Taxi drivers, who often wear white gloves, sometimes get out to bow when they drop off a passenger. Staff in shops and restaurants are unfailingly polite.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. An extra special thank-you to Boston Dynamics this week for posting another video of SpotMini that includes a nice, detailed explanation of what's actually going on: A test of SpotMini's ability to adjust to disturbances as it opens and walks through a door. A person (not shown) drives the robot up to the door, points the hand at the door handle, then gives the'GO' command, both at the beginning of the video and again at 42 seconds.

These past months have given us some news about developments in artificial intelligence. OpenAI was launched, and Tech Insider reported some interesting statistics on the future of robotics and artificial intelligence, citing research from the 300-page Merrill Lynch Report on what the future holds. Makes you stop and think for a moment, huh? However, these findings make me wonder about what's not being said, questioned or discussed. Personally, I come from the place of an observer.