Japanese video game publisher Square Enix has announced that it is now parting ways with IO Interactive, the studio behind the "Hitman" franchise. Square Enix plans to sell IO to interested investors, but if an agreement isn't reached, the Danish studio might be shut down. "To maximize player satisfaction as well as market potential going forward, we are focusing our resources and energies on key franchises and studios," Square Enix said in its statement. "As a result, the Company has regrettably decided to withdraw from the business of IO Interactive A/S. a wholly-owned subsidiary and a Danish corporation, as of March 31, 2017." Square Enix assumed ownership of IO Interactive when it acquired it from Eidos Interactive back in 2009.

Pokémon Go is a very popular game with kids and now it could become a learning tool. The game, which incentivizes players to get up and about by letting them capture and battle virtual pocket monsters, might be used as a classroom learning tool, in accordance with common core learning standards, as per Emily Howell, an assistant professor at the Iowa State University School of Education. "It is important to give students authentic choices that really have meaning in their lives. We need to encourage them to develop questions, research the answers and then share that information in writing," Howell said in the press release. Howell is working with school teachers on the use of Pokémon Go as a digital tool to help students learn.

In this series, I will try to give you an overview of the MXnet Deep Learning library: we'll look at its main features and its Python API (which I suspect will be the #1 choice). Later on, we'll explore some of the MXNet tutorials and notebooks available online, and we'll hopefully manage to understand every single line of code! If you'd like learn more about the rationale and the architecture of MXNet, you should read this paper, named "MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems". We'll cover most of the concepts presented in the paper, but hopefully in a more accessible way. I'll go as slow and explain as much as I need to.

Ikea recently launched a survey to gauge how consumers feel about artificial intelligence, and what they are looking for in AI and virtual assistant capabilities, a move which may be a precursor to the retailer launching its own such solution. The survey, called "Do You Speak Human?," was created by Ikea's Space10 innovation and design lab and asks questions such as whether consumers want your AI to be human-like, if it should be male, female or gender-neutral, and even if it should be religious, among other questions. An Ikea official stressed that the retailer remains at an information-gathering stage, New Atlas reports. The company recognizes that AI presents a "tremendous opportunity," but for now it is simply curious how people feel about AI. Consumers can see how their answers stack up to others immediately after taking the survey and have the option to submit an e-mail address to be kept in the loop as the survey progresses.

What are the most interesting recent videos on YouTube about artificial intelligence (AI)? We save your time filtering mega-hours of videos uploaded each day to select the most relevant and popular ones, by view-count as of 1 May 2017. The description is as appeared at YouTube. This video shows that GeForce GTX G-Assist takes advantage of cutting-edge NVIDIA artificial intelligence to bring you the next revolution in gaming. This is a video for the first-ever entire songs composed by Artificial Intelligence: "Daddy's Car" and "Mister Shadow", created by scientists at SONY CSL Research Lab.

Australian Securities Exchange-listed Internet of Things (IoT) company Xped has announced that it is purchasing fellow Adelaide-based artificial intelligence (AI) company Jemsoft for AU$200,000 in cash and 50 million Xped shares. The total value of the deal comes to AU$900,000, with Xped shares priced at 14 cents per share. In addition to acquiring all of Jemsoft's intellectual property -- including its computer vision machine-learning technology, Monocular API -- Xped will also gain ownership of 51 percent of Jemsoft's partially owned subsidiary Media Intelligence, which offers media measurement technologies and real-time research solutions. The acquisition will allow Xped -- which offers a platform that enables consumers of all technical capabilities to connect, monitor, and control everyday devices and appliances through their smartphones -- to provide an in-house AI solution to clients looking to enhance their IoT products using visual sensors. Monocular's account management and dashboard components, as well as its user-trainable functionality, will become "integral components" of Xped's smart home and other IoT offerings moving forward, the companies said.

Swarm robotics has experienced a rapid expansion in recent years, primarily fueled by specialized multi-robot systems developed to achieve dedicated collective actions. These specialized platforms are in general designed with swarming considerations at the front and center. Key hardware and software elements required for swarming are often deeply embedded and integrated with the particular system. However, given the noticeable increase in the number of low-cost mobile robots readily available, practitioners and hobbyists may start considering to assemble full-fledged swarms by minimally retrofitting such mobile platforms with a swarm-enabling technology. Here, we report one possible embodiment of such a technology designed to enable the assembly and the study of swarming in a range of general-purpose robotic systems. This is achieved by combining a modular and transferable software toolbox with a hardware suite composed of a collection of low-cost and off-the-shelf components. The developed technology can be ported to a relatively vast range of robotic platforms with minimal changes and high levels of scalability. This swarm-enabling technology has successfully been implemented on two distinct distributed multi-robot systems, a swarm of mobile marine buoys and a team of commercial terrestrial robots. We have tested the effectiveness of both of these distributed robotic systems in performing collective exploration and search scenarios, as well as other classical cooperative behaviors. Experimental results on different swarm behaviors are reported for the two platforms in uncontrolled environments and without any supporting infrastructure. The design of the associated software library allows for a seamless switch to other cooperative behaviors, and also offers the possibility to simulate newly designed collective behaviors prior to their implementation onto the platforms.

Question Answering websites are popular repositories of expert knowledge and cover areas as diverse as linguistics, computer science, or mathematics. Knowledge is commonly organized via user defined tags which implicitly create population folksonomies. However, the interplay between latent knowledge structures and the answering behavior of users has not been fully explored yet. Here, we propose a model of a dynamical tagging process guided by taxonomies, devise a robust algorithm that allow us to uncover hidden topic hierarchies, apply our method to analyze several Stack Exchange websites. Our results show that the dynamics of the system strongly correlate with uncovered taxonomies.

Online health support groups are places for people to compare themselves with others and obtain informational and emotional support about their disease. To do so, they generally need to reveal private information about themselves and in many support sites, they can do this in public or private channels. However, we know little about how the publicness of the channels in health support groups influence the amount of self-disclosure people provide. Our work examines the extent members self-disclose in the private and public channels of an online cancer support group. We first built machine learning models to automatically identify the amount of positive and negative self-disclosure in messages exchanged in this community, with adequate validity r>0.70. In contrast to findings from non-health-related sites, our results show that people generally self-disclose more in the public channel than the private one and are especially likely to reveal their negative thoughts and feelings publicly. We discuss theoretical and practical implications of our work.

Online marketplaces, search engines, and databases employ aggregated social information to rank their content for users. Two ranking heuristics commonly implemented to order the available options are the average review score and item popularity — that is, the number of users who have experienced an item. These rules, although easy to implement, only partly reflect actual user preferences, as people may assign values to both average scores and popularity and trade off between the two. How do people integrate these two pieces of social information when making choices? We present two experiments in which we asked participants to choose 200 times among options drawn directly from two widely used online venues: Amazon and IMDb. The only information presented to participants was the average score and the number of reviews, which served as a proxy for popularity. We found that most people are willing to settle for items with somewhat lower average scores if they are more popular. Yet, our study uncovered substantial diversity of preferences among participants, which indicates a sizable potential for personalizing ranking schemes that rely on social information.