It's been a wild ride this year, but you can always rely on Assassin's Creed to lighten the mood. Let's see what those zany historians at Ubisoft have cooked up for us in the excitingly named Assassin's Creed Valhalla … Peterborough, is it? I have nothing against our beautiful cathedral cities, rolling plains and park-and-ride services, but after 12 months of Brexit, Covid-19 and forest fires, plus the cancellation of the Eurovision song contest, I was hoping for something a little less Tough Mudder from this giddy, quasi-historical, action-adventure series, which previously had us gallivanting around Atlantis. For the first few hours, you're thrown into the icy political drama of ninth-century Norway, where Viking warrior Eivor runs around snow-blasted islands having stern conversations about the future of her clan. I went with female Eivor.)

When Charles Rosen, the A.I. pioneer who founded SRI International's Artificial Intelligence Center, was asked to come up with a name for the world's first general -purpose mobile robot, he thought for a moment and then said: "Well, it shakes like hell when it moves. Let's just call it Shakey." Some variation of this idea has pervaded for much of the history of modern robotics. Robots, we often assume, are clunky machines with as much grace as an atheist's Sunday lunch. Even science fiction movies have repeatedly imagined robots as ungainly creations that walk with slow, halting steps. Recently, a group of researchers from the Dynamic Robotics Laboratory at Oregon State took one of the university's Cassie robots, a pair of walking robot legs that resembles the lower extremities of an ostrich, to a sports field to try out the lab's latest "bipedal gait" algorithms.

The Global Artificial Intelligence (AI) Hardware Market is one of the most promising market segments in the past decade.

Fox News contributor Joe Concha weighs in on the mainstream media's coverage of election celebrations vs. Trump rallies on'America's Newsroom.' Washington Post media columnist Margaret Sullivan suggested that her journalist peers "never fully learned how to cover" President Trump but "might have saved democracy" following his projected defeat against Joe Biden. "Over the past four or five years, I've been sharply critical of the media, including that subset I like to call the'reality-based press,'" Sullivan wrote on Sunday. "My continuing complaint has been that mainstream journalism never quite figured out how to cover President Trump, the master of distraction and insult who craved media attention and knew exactly how to get it, regardless of what it meant for the good of the nation." Sullivan indicated that the press was too obedient of the "deeply abnormal president," writing "When he said'jump,' journalists all too often said'how high?'" and that the media "constantly sought to normalize him, treating his deranged tweets like legitimate news and piously forecasting, every time he sounded the least bit calm, that he was becoming'presidential.'"

Increasingly the convergence of quantum science with machine learning will produce AI technology certainly beyond our understanding and possibly …

Autonomous systems are revolutionizing our lives, but they present clear international security concerns.

The weaponization of digital communications and social media to conduct disinformation campaigns at immense scale, speed, and reach presents new challenges to identify and counter hostile influence operations (IO). This paper presents an end-to-end framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and a novel network causal inference approach to quantify the impact of individual actors in spreading IO narratives. We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections, and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007-February 2020), over 50 thousand accounts, 17 countries, and different account types including both trolls and bots. Our system detects IO accounts with 96% precision, 79% recall, and 96% area-under-the-PR-curve, maps out salient network communities, and discovers high-impact accounts that escape the lens of traditional impact statistics based on activity counts and network centrality. Results are corroborated with independent sources of known IO accounts from U.S. Congressional reports, investigative journalism, and IO datasets provided by Twitter.

Social media have been deliberately used for malicious purposes, including political manipulation and disinformation. Most research focuses on high-resource languages. However, malicious actors share content across countries and languages, including low-resource ones. Here, we investigate whether and to what extent malicious actors can be detected in low-resource language settings. We discovered that a high number of accounts posting in Tagalog were suspended as part of Twitter's crackdown on interference operations after the 2016 US Presidential election. By combining text embedding and transfer learning, our framework can detect, with promising accuracy, malicious users posting in Tagalog without any prior knowledge or training on malicious content in that language. We first learn an embedding model for each language, namely a high-resource language (English) and a low-resource one (Tagalog), independently. Then, we learn a mapping between the two latent spaces to transfer the detection model. We demonstrate that the proposed approach significantly outperforms state-of-the-art models, including BERT, and yields marked advantages in settings with very limited training data-the norm when dealing with detecting malicious activity in online platforms.

Recommender systems are one of the most widely used services on several online platforms to suggest potential items to the end-users. These services often use different machine learning techniques for which fairness is a concerning factor, especially when the downstream services have the ability to cause social ramifications. Thus, focusing on the non-personalised (global) recommendations in news media platforms (e.g., top-k trending topics on Twitter, top-k news on a news platform, etc.), we discuss on two specific fairness concerns together (traditionally studied separately)---user fairness and organisational fairness. While user fairness captures the idea of representing the choices of all the individual users in the case of global recommendations, organisational fairness tries to ensure politically/ideologically balanced recommendation sets. This makes user fairness a user-side requirement and organisational fairness a platform-side requirement. For user fairness, we test with methods from social choice theory, i.e., various voting rules known to better represent user choices in their results. Even in our application of voting rules to the recommendation setup, we observe high user satisfaction scores. Now for organisational fairness, we propose a bias metric which measures the aggregate ideological bias of a recommended set of items (articles). Analysing the results obtained from voting rule-based recommendation, we find that while the well-known voting rules are better from the user side, they show high bias values and clearly not suitable for organisational requirements of the platforms. Thus, there is a need to build an encompassing mechanism by cohesively bridging ideas of user fairness and organisational fairness. In this abstract paper, we intend to frame the elementary ideas along with the clear motivation behind the requirement of such a mechanism.

There are Docker images by Nvidia, and other resources such as the Lambda stack that work with ampere cards. And it won't be long before support will be added There is a problem if you want to use tensorflow 1.x neural networks such as Stylegan2. Supposedly, this can also be solved using an Nvidia docker image, but I haven't seen anybody post any results using this method.