Publishing deals in the video game industry are generally kept secret, with terms hidden behind non-disclosure agreements and the threat of legal fallout. However, in the realm of AAA publishing, it's common for independent developers to sign contracts granting them less than 10 percent of a game's lifetime revenue, in exchange for marketing and financial assistance from a multibillion-dollar organization. In some cases, the developer also signs away their intellectual property rights, losing creative control over the game entirely. Or, a huge company will simply buy the smaller studio outright, devouring its existing library and creative talent, and overseeing all of its future products. In late March, Epic Games launched a multiplatform publishing initiative touting "the most developer-friendly terms in the industry." Under this deal, developers are guaranteed 50 percent of a game's revenue once production costs are recouped, and they retain full creative control over their own titles. Epic also promises to cover up to 100 percent of a game's development costs, including salaries, advertising and publishing fees. "We're building the publishing model we always wanted for ourselves," said Epic founder and CEO Tim Sweeney. Epic Games has been experimenting with publishing models since the early '90s, decades before the launch of Fortnite, The Epic Games Store or the Unreal Engine. We're talking about the days of BBS, back when Sweeney was building ZZT out of his parents' house and the World Wide Web was just flickering to life.

As the recently released GPT-3 and several recent studies demonstrate, racial bias, as well as bias based on gender, occupation, and religion, can be found in popular NLP language models. But a team of AI researchers wants the NLP bias research community to more closely examine and explore relationships between language, power, and social hierarchies like racism in their work. Published last week, the work, which includes analysis of 146 NLP bias research papers, also concludes that the research field generally lacks clear descriptions of bias and fails to explain how, why, and to whom that bias is harmful. "Although these papers have laid vital groundwork by illustrating some of the ways that NLP systems can be harmful, the majority of them fail to engage critically with what constitutes'bias' in the first place," the paper reads. "We argue that such work should examine the relationships between language and social hierarchies; we call on researchers and practitioners conducting such work to articulate their conceptualizations of'bias' in order to enable conversations about what kinds of system behaviors are harmful, in what ways, to whom, and why; and we recommend deeper engagements between technologists and communities affected by NLP systems."

Google will postpone the unveiling of its Android 11 update after declaring that "now is not the time". The announcement came as protests continued across US cities, following the death of George Floyd in policy custody. It is one of a range of measures taken by the company to show its support for the Americans protesting against racial inequality. It also added a message to its search page that reads: "We stand in support of racial equality, and all those who search for it". YouTube, which is owned by the same company, has committed $1 million to non-profit Center for Policing Equity in a move it said demonstrated "solidarity against racism and violence".

This week, host Isaac Butler talks to documentary theater makers Jessica Blank and Erik Jensen, whose plays include The Exonerated, about the criminal justice system, and Coal Country, about the Upper Big Branch mine disaster in West Virginia. Blank and Jensen explain how documentary theater works, from interviews with subjects to the final product, where actors perform interview excerpts verbatim. After the interview, Isaac and co-host June Thomas discuss why documentary theater is such a great way to communicate important information to an audience. Send your questions about creativity and any other feedback to working@slate.com.

You may even be using one to read this article. Wi-Fi has become essential to our personal and professional lives. The smartphone and the internet we use today wouldn't have been possible without wireless communication technologies such as Wi-Fi. In 1995 if you wanted to "surf" the internet at home, you had to chain yourself to a network cable like it was an extension cord. In 1997, Wi-Fi was invented and released for consumer use.

Three Overwatch League franchises have publicly condemned the Minneapolis police this week for the death of George Floyd, who died Monday after a white police officer pressed his knee on Floyd's neck in a prolonged manner while detaining him. In a widely-shared video of the incident, Floyd can be heard gasping: "I can't breathe." It's the first time teams in the league have spoken publicly on racism and police brutality, and a rare instance of a franchise commenting on an event with sociopolitical implications.

The time series classification literature has expanded rapidly over the last decade, with many new classification approaches published each year. Prior research has mostly focused on improving the accuracy and efficiency of classifiers, with interpretability being somewhat neglected. This aspect of classifiers has become critical for many application domains and the introduction of the EU GDPR legislation in 2018 is likely to further emphasize the importance of interpretable learning algorithms. Currently, state-of-the-art classification accuracy is achieved with very complex models based on large ensembles (COTE) or deep neural networks (FCN). These approaches are not efficient with regard to either time or space, are difficult to interpret and cannot be applied to variable-length time series, requiring pre-processing of the original series to a set fixed-length. In this paper we propose new time series classification algorithms to address these gaps. Our approach is based on symbolic representations of time series, efficient sequence mining algorithms and linear classification models. Our linear models are as accurate as deep learning models but are more efficient regarding running time and memory, can work with variable-length time series and can be interpreted by highlighting the discriminative symbolic features on the original time series. We show that our multi-resolution multi-domain linear classifier (mtSS-SEQL+LR) achieves a similar accuracy to the state-of-the-art COTE ensemble, and to recent deep learning methods (FCN, ResNet), but uses a fraction of the time and memory required by either COTE or deep models. To further analyse the interpretability of our classifier, we present a case study on a human motion dataset collected by the authors. We release all the results, source code and data to encourage reproducibility.

The author of Robots Are People Too responds to Tobias Buckell's "Scar Tissue." "Scar Tissue" is an emotionally resonant tale about healing through robot rearing--but it was only half of the story about Rob's creator, Advent Robotics. What we read was the part Cory could see: Rob as a growing child, nurtured by Cory. But in the story, Tobias Buckell offers us a little hint about everything happening at Advent: "Every time [Advent's robots] get on that charger, they're not just powering up their onboard battery--they're taking in their experiences and uploading data to our servers to have it examined." That's the part that worries me, as artificial intelligence applications may be able to leverage the data to manipulate Cory and other people--just as technology, PR, and marketing companies try to do in our lives today.

A plaintiff in the lawsuit is the Chicago Alliance Against Sexual Exploitation, a nonprofit that advocates for the rights of survivors of sexual violence and exploitation. The group's legal director, Mallory Littlejohn, said Clearview's technology makes survivors fear being tracked by abusers.

Amazon's controversial facial recognition technology has incorrectly matched more than 100 photos of politicians in the UK and US to police mugshots, new tests have revealed. Amazon Rekognition uses artificial intelligence software to identify individuals from their facial structure. Customers include law enforcement and US government agencies like Immigration and Custome Enforcement (ICE). It is not the first time the software's accuracy has been called into question. In July 2018, the American Civil Liberties Union (ACLU) found 28 false matches between US Congress members and pictures of people arrested for a crime.