A game developer said it would pull the planned'Active Shooter' video game after backlash. A game developer and digital distribution company has announced, after facing harsh criticism, that it will pull a video game that allows players to simulate a school shooter. Valve Inc. announced Tuesday it would pull "Active Shooter" from its online gaming platform, Steam, after learning that the game's publisher and developer has a "history of customer abuse" that was uncovered as a result of the controversy surrounding the game, Variety and other news outlets reported. "This developer and publisher is, in fact, a person calling himself Ata Berdiyev, who had previously been removed last fall when he was operating as '[bc]Interactive' and'Elusive Team,'" Valve said. His subsequent return under new business names was a fact that came to light as we investigated the controversy around his upcoming title.

Machine Learning (ML) has become massively popular over the last several years. And why… well simply because it works! The latest research has achieved record breaking results, even surpassing human performance on some tasks. Of course as a result many people are rushing to get into this field; and why not. It's well funded, the technology is exciting and interesting, and there's lots of room for growth.

WASHINGTON – An upcoming video game that lets players simulate a school shooting is coming in for criticism from parents of shooting victims and from politicians. The game, "Active Shooter," is to be released by Valve Corp. of Bellevue, Washington, on June 6 for between $5 and $10. A trailer on the website of Valve's digital distributor Steam opens with the player's character as a SWAT team member entering a school to tackle a shooter, before switching over to the perspective of the attacker, with the action set to a pounding heavy metal score. It ends with a trail of students' bodies littering an auditorium room as a stats box keeps count of the numbers of police and civilians killed. In addition to allowing players to pick sides, the game boasts a multiplayer mode and the ability to play as an unarmed student trying to survive.

We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.

Abstract--In this paper, we train a recurrent neural network to learn dynamics of a chaotic road environment and to project the future of the environment on an image. Future projection can be used to anticipate an unseen environment for example, in autonomous driving. Road environment is highly dynamic and complex due to the interaction among traffic participants such as vehicles and pedestrians. Even in this complex environment, a human driver is efficacious to safely drive on chaotic roads irrespective of the number of traffic participants. The proliferation of deep learning research has shown the efficacy of neural networks in learning this human behavior . In the same direction, we investigate recurrent neural networks to understand the chaotic road environment which is shared by pedestrians, vehicles (cars, trucks, bicycles etc.), and sometimes animals as well. We propose Foresee, a unidirectional gated recurrent units (GRUs) network with attention to project future of the environment in the form of images. We have collected several videos on Delhi roads consisting of various traffic participants, background and infrastructure differences (like 3D pedestrian crossing) at various times on various days. We train Foresee in an unsupervised way and we use online training to project frames up to 0 . We show that our proposed model performs better than state of the art methods (prednet [20], Enc. Dec. LSTM [28]) and finally, we show that our trained model generalizes to a public dataset for future projections. Environment anticipation is an important task for situation awareness and decision making.

After all, the term Machine Learning was coined based on the way the human (or animal) brain learns, meaning that somehow, machines could also benefit from a similar kind of learning. But human beings, successful ones for sure, know how to un-learn. In my case, while I was always fascinated by mathematics since my very early years, the school system's training (as in training an algorithm in ML) failed on me. It failed not because I did not succeed at school (I ended up at Cambridge University) but because I was fed (the way an ML learning algorithm is fed with a training set) with the most boring, least valuable kind of mathematics when attending high school. Later on, during my academic years, I can say the same about the way I was trained to write academic articles: emphasis was on delivering esoteric content that few could read or leverage.

Parents of school shooting victims, and other survivors of mass shootings, are decrying an upcoming video game that is set to allow players to commit a school shooting--as well as stop one. Active Shooter, which is scheduled for a June 6 release, is a "dynamic SWAT simulator," where players can choose whether to be an armed officer who is responding to a shooting, the shooter, or a victim trying to escape. The game will be sold for $5 to $10 in the online marketplace Steam, which is run by the Valve Corporation. The game comes with a disclaimer: "Please do not take any of this seriously. This is only meant to be the simulation and nothing else. If you feel like hurting someone or people around you, please seek help from local psychiatrists or dial 911 (or applicable). "It's disgusting that Valve Corp. is trying to profit from the glamorization of tragedies affecting our schools across the country," Ryan Petty, whose daughter Alaina died in the February shooting in Parkland, Florida, said in a statement. "Keeping our kids safe is a real issue affecting our communities and is in no way a'game.'" The father of another Parkland victim, 14-year-old Jaime Guttenberg, called for a boycott on Twitter. "I have seen and heard many horrific things over the past few months since my daughter was the victim of a school shooting and is now dead in real life," Fred Gutenberg wrote. "This game may be one of the worst." Andrew Pollack, the father of 18-year-old Meadow Pollack, who was also killed in Parkland said the game would desensitize young people to potential school shootings. "The last thing we need is a simulated training on school shootings," he said. "Video game designers should think of the influence they hold.

R is a data analysis software as well as a programming language. Data scientists, statisticians and analysts use R for statistical analysis, data visualization and predictive modeling. R is open source and allows integration with other applications and systems. Compared to other data analysis platforms, R has an extensive set of data products. Problems faced with data are cleared with R's excellent data visualization feature.

This course will introduce students to the basics of the Structured Query Language (SQL) as well as basic database design for storing data as part of a multi-step data gathering, analysis, and processing effort. The course will use SQLite3 as its database. We will also build web crawlers and multi-step data gathering and visualization processes. We will use the D3.js library to do basic data visualization. This course will cover Chapters 14-15 of the book "Python for Everybody".

We love new usability until it becomes routine, then we hate it again. The problem isn't that the software is clunky and too hard to use -- we just don't want to use it. No matter how much we gamify the experience and add cute little icons and progress bars, we still must point and click our way to learning activities, training plans, and history. We must manipulate a mouse and keyboard and search through web pages, menus, and unending lists when we would rather be doing creative work or interacting with colleagues or customers.