Japanese gaming company Nintendo has unveiled the Nintendo Switch console at a presentation in Tokyo. The new gaming platform wants to do everything at once, executives said, from the old-school fantasy of its flagship launch title, Zelda: Breath of the Wild, to party games played face-to-face rather than in front of a screen. The Switch "inherited all of Nintendo's entertainment DNA" across the company's broad line of products, said Shinya Takahashi, Nintendo's head of software development. The presentation ended with a surprise: the long-delayed Breath of the Wild, originally planned as an exclusive to the Wii U platform, will go on sale with the new console. The Switch will go on sale on 3 March in Europe, Japan and the US, at a cost of $299.99.

The first shoe dropped in October when Nintendo showed its mystery console--an edgy, millennial-angled home/mobile hybrid called "Switch." The second--a jumbo-sized boot full of facts and quirky usage scenarios--landed late Jan. 12, ending months of speculation about the new platform's price and launch window. Here's everything you need to know about the upcoming Nintendo Switch: Nintendo president Tatsumi Kimishima took the stage at the outset to reveal that Switch will launch globally on March 3, 2017 for $299.99--earlier Company director Shinya Takahashi followed with a history of Nintendo's platforms illustrating how Switch is in effect a composite of everything the company's been working toward for decades: a game system you can dock with a TV, but also removed to go mobile, and attach a pair of left and right controllers--called Joy-Cons--that let you play anywhere. "TV Mode" is your classic set-top with a big screen.

There's never any shortage of buzzwords in the IT world, but when it comes to A.I., they can be hard to tell apart. Here are five things you need to understand. Artificial intelligence refers to "a broad set of methods, algorithms and technologies that make software'smart' in a way that may seem human-like to an outside observer," said Lynne Parker, director of the division of Information and Intelligent Systems for the National Science Foundation. Machine learning, computer vision, natural language processing, robotics and related topics are all part of A.I., in other words. "Some people may come up with distinctions between the two, but there is not a universal view that the two terms mean anything different," Parker said.

Selena Gomez and rumored boyfriend The Weeknd were certified newsmakers this week. But what's more interesting about this developing story was the latest report highlighting the sex life and nontraditional views on love and marriage of Gomez's new lover. In a recent interview with GQ magazine, the publication that labeled The Weeknd as the "king of sex pop," the 26-year-old "Starboy" singer candidly spoke about his own views on love and marriage. The Grammy Award-winning musician said his popularity does not affect his ego and his dating game, saying most girls want to get involve with him because of his talents instead of appearance. "The reason why they want to [have sex] with me is because of what I do [in the studio]," The Weeknd said in an interview for GQ's February 2017 issue, Us Weekly quoted.

Europe is preparing for a robot revolution. European lawmakers have proposed that robots be equipped with emergency "kill switches" to prevent them from causing excessive damage. Legislators have also suggested that robots be insured and even be made to pay taxes. The proposal on robot governance was approved by the European Parliament's legal affairs committee on Thursday. The issue will now be considered by the European Commission, which is the bloc's top regulator.

It is a route that first brought the Pilgrims from England to Plymouth in 1620. Now, the route of the Mayflower is set to be followed again - by a entirely autonomous high tech ship. Called the Mayflower Autonomous Ship (MAS), the unmanned ship runs entirely on renewable energy, and will sail on the 400th anniversary of the pilgrims' voyage from England to America. Researches have revived the Mayflower for another journey across the Atlantic, but its design has a modern twist. The Mayflower Autonomous Ship (MAS) is set to sail in 2020 and take the same route as the pilgrims did in 1620.

MEPs requested for an implementation of inclusive policies about how humans will interact with robots and other artificial intelligence. The report clearly considers that the world is on the verge of a'new industrial' robotic innovation. It also contemplates whether to give robots a legal category as electronic individuals. Furthermore, the report dictates designers to create a'kill switch' for all robots. This will allow all functions to be shut down if the situation calls for it.

What Can I Do Now? Guiding Users in a World of Automated Decisions Abstract More and more processes governing our lives use in some part an automatic decision step, where - based on a feature vector derived from an applicant - an algorithm has the decision power over the final outcome. Here we present a simple idea which gives some of the power back to the applicant by providing her with alternatives which would make the decision algorithm decide differently. It is based on a formalization reminiscent of methods used for evasion attacks, and consists in enumerating the subspaces where the classifiers decides the desired output. This has been implemented for the specific case of decision forests (ensemble methods based on decision trees), mapping the problem to an iterative version of enumerating k-cliques. We live in a world where more and more of decision affecting our lives are taken by automatic systems.

We study large-scale kernel methods for acoustic modeling in speech recognition and compare their performance to deep neural networks (DNNs). We perform experiments on four speech recognition datasets, including the TIMIT and Broadcast News benchmark tasks, and compare these two types of models on frame-level performance metrics (accuracy, cross-entropy), as well as on recognition metrics (word/character error rate). In order to scale kernel methods to these large datasets, we use the random Fourier feature method of Rahimi and Recht (2007). We propose two novel techniques for improving the performance of kernel acoustic models. First, in order to reduce the number of random features required by kernel models, we propose a simple but effective method for feature selection. The method is able to explore a large number of non-linear features while maintaining a compact model more efficiently than existing approaches. Second, we present a number of frame-level metrics which correlate very strongly with recognition performance when computed on the heldout set; we take advantage of these correlations by monitoring these metrics during training in order to decide when to stop learning. This technique can noticeably improve the recognition performance of both DNN and kernel models, while narrowing the gap between them. Additionally, we show that the linear bottleneck method of Sainath et al. (2013) improves the performance of our kernel models significantly, in addition to speeding up training and making the models more compact. Together, these three methods dramatically improve the performance of kernel acoustic models, making their performance comparable to DNNs on the tasks we explored.

An important problem for HCI researchers is to estimate the parameter values of a cognitive model from behavioral data. This is a difficult problem, because of the substantial complexity and variety in human behavioral strategies. We report an investigation into a new approach using approximate Bayesian computation (ABC) to condition model parameters to data and prior knowledge. As the case study we examine menu interaction, where we have click time data only to infer a cognitive model that implements a search behaviour with parameters such as fixation duration and recall probability. Our results demonstrate that ABC (i) improves estimates of model parameter values, (ii) enables meaningful comparisons between model variants, and (iii) supports fitting models to individual users. ABC provides ample opportunities for theoretical HCI research by allowing principled inference of model parameter values and their uncertainty.