We model here an epistemic bias we call \textit{interpretive blindness} (IB). IB is a special problem for learning from testimony, in which one acquires information only from text or conversation. We show that IB follows from a co-dependence between background beliefs and interpretation in a Bayesian setting and the nature of contemporary testimony. We argue that a particular characteristic contemporary testimony, \textit{argumentative completeness}, can preclude learning in hierarchical Bayesian settings, even in the presence of constraints that are designed to promote good epistemic practices.

In media news today, NBC fact-checks Anthony Fauci's COVID superspreader comments, Jon Stewart says the media is making a'mistake' casting Trump as a'supervillain,' and CNN's Brian Stelter frets that Katie Couric's editing scandal further damages the media's reputation Sen. Bill Cassidy, R-La., told Axios that he favored cognitive tests for aging government leaders in order to make sure their ability to serve the American people remained intact. On Sunday's "Axios on HBO," Cassidy cited past U.S. Senators he said were senile by the end of their times in office to argue a cognitive test applying across all three branches of government would be "a reasonable plan." "The Speaker of the House is 81. Wisdom comes with age, but the science is also clear that we aren't who we were, that we do lose things with age. As a medical professional, is that something we should be thinking about?"

Apple thinks it has a simple way to boost Apple Music adoption: limit control in return for a lower fee. The company has introduced an Apple Music Voice Plan that offers access to the full song catalog for just $5 per month, so long as you're willing to rely solely on Siri control. It's pitched as ideal for HomePod and AirPod owners and others who are more likely to use a voice assistant than tap their phone. The new tier will be available later in the fall in 17 countries, including the US, UK and Canada. You can start a trial by asking Siri to "start my Apple Music Voice trial."

VENICE LIDO, Italy – When the highly anticipated remake of Frank Herbert's influential '60s novel world premiered in September at the Venice Film Festival, it was "Dune -- Part 1." Now as Denis Villeneuve's lauded adaptation opens nationwide, it's simply "Dune" -- maybe because no one knows if there will be a concluding Part 2. "The biggest challenge," said Villeneuve ("Sicario," "Arrival") "is that the book is so rich and its strength is all in its details. I had to find equilibrium for someone who doesn't know the book at all and be as cinematic as possible. So that they will need to understand the movie without crushing them with exposition. So the ideas could follow the story." "Dune" is set far into a future where Oscar Isaac's Duke rules the kingdom of Atreides.

It has been argued that fake news and the spread of false information pose a threat to societies throughout the world, from influencing the results of elections to hindering the efforts to manage the COVID-19 pandemic. To combat this threat, a number of Natural Language Processing (NLP) approaches have been developed. These leverage a number of datasets, feature extraction/selection techniques and machine learning (ML) algorithms to detect fake news before it spreads. While these methods are well-documented, there is less evidence regarding their efficacy in this domain. By systematically reviewing the literature, this paper aims to delineate the approaches for fake news detection that are most performant, identify limitations with existing approaches, and suggest ways these can be mitigated. The analysis of the results indicates that Ensemble Methods using a combination of news content and socially-based features are currently the most effective. Finally, it is proposed that future research should focus on developing approaches that address generalisability issues (which, in part, arise from limitations with current datasets), explainability and bias.

To this day, there has not yet been an attempt at exploring the use of neural networks for the synthesis of footsteps sounds although Footsteps are among the most ubiquitous sound effects in multimedia there is substantial literature exploring neural synthesis of broadband applications. There is substantial research into understanding impulsive sounds, such as drums samples, which have some the acoustic features and developing synthesis models for footstep similarities to footsteps. One of the first attempts was in [15], where sound effects. In this paper, we present a first attempt at adopting Donahue et al. developed WaveGAN - a generative adversarial network neural synthesis for this task. We implemented two GAN-based architectures for unconditional audio synthesis. Another example of neural and compared the results with real recordings as well as synthesis of drums is [16], where the authors used a Progressive six traditional sound synthesis methods. Our architectures reached Growing GAN. Variational autoencoders [17] and U-Nets [18] realism scores as high as recorded samples, showing encouraging have also been used for the same task.

Media bias and its extreme form, fake news, can decisively affect public opinion. Especially when reporting on policy issues, slanted news coverage may strongly influence societal decisions, e.g., in democratic elections. Our paper makes three contributions to address this issue. First, we present a system for bias identification, which combines state-of-the-art methods from natural language understanding. Second, we devise bias-sensitive visualizations to communicate bias in news articles to non-expert news consumers. Third, our main contribution is a large-scale user study that measures bias-awareness in a setting that approximates daily news consumption, e.g., we present respondents with a news overview and individual articles. We not only measure the visualizations' effect on respondents' bias-awareness, but we can also pinpoint the effects on individual components of the visualizations by employing a conjoint design. Our bias-sensitive overviews strongly and significantly increase bias-awareness in respondents. Our study further suggests that our content-driven identification method detects groups of similarly slanted news articles due to substantial biases present in individual news articles. In contrast, the reviewed prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets.

Slanted news coverage strongly affects public opinion. This is especially true for coverage on politics and related issues, where studies have shown that bias in the news may influence elections and other collective decisions. Due to its viable importance, news coverage has long been studied in the social sciences, resulting in comprehensive models to describe it and effective yet costly methods to analyze it, such as content analysis. We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis to reveal person-targeting biases in news articles reporting on policy issues. In a large-scale user study, we find very promising results regarding this interdisciplinary research direction. Our recommender detects and reveals substantial frames that are actually present in individual news articles. In contrast, prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets. Further, our study shows that recommending news articles that differently frame an event significantly improves respondents' awareness of bias.

Facebook Inc. executives have long said that artificial intelligence would address the company's chronic problems keeping what it deems hate speech and excessive violence as well as underage users off its platforms. That future is farther away than those executives suggest, according to internal documents reviewed by The Wall Street Journal. Facebook's AI can't consistently identify first-person shooting videos, racist rants and even, in one notable episode that puzzled internal researchers for weeks, the difference between cockfighting and car crashes.

It follows an approach where you read an explanation for a programming concept and then write the code character by character. One of the best Python books to start your Python Journey. This is a reliable companion to the Python documentation. It gives a rich view of the language and many of its most useful modules, whilst still being concise.