Open Access (OA) facilitates access to articles. But, authors or funders often must pay the publishing costs preventing authors who do not receive financial support from participating in OA publishing and citation advantage for OA articles. OA may exacerbate existing inequalities in the publication system rather than overcome them. To investigate this, we studied 522,411 articles published by Springer Nature. Employing correlation and regression analyses, we describe the relationship between authors affiliated with countries from different income levels, their choice of publishing model, and the citation impact of their papers. A machine learning classification method helped us to explore the importance of different features in predicting the publishing model. The results show that authors eligible for APC waivers publish more in gold-OA journals than others. In contrast, authors eligible for an APC discount have the lowest ratio of OA publications, leading to the assumption that this discount insufficiently motivates authors to publish in gold-OA journals. We found a strong correlation between the journal rank and the publishing model in gold-OA journals, whereas the OA option is mostly avoided in hybrid journals. Also, results show that the countries' income level, seniority, and experience with OA publications are the most predictive factors for OA publishing in hybrid journals.

Although media bias detection is a complex multi-task problem, there is, to date, no unified benchmark grouping these evaluation tasks. We introduce the Media Bias Identification Benchmark (MBIB), a comprehensive benchmark that groups different types of media bias (e.g., linguistic, cognitive, political) under a common framework to test how prospective detection techniques generalize. After reviewing 115 datasets, we select nine tasks and carefully propose 22 associated datasets for evaluating media bias detection techniques. We evaluate MBIB using state-of-the-art Transformer techniques (e.g., T5, BART). Our results suggest that while hate speech, racial bias, and gender bias are easier to detect, models struggle to handle certain bias types, e.g., cognitive and political bias. However, our results show that no single technique can outperform all the others significantly. We also find an uneven distribution of research interest and resource allocation to the individual tasks in media bias. A unified benchmark encourages the development of more robust systems and shifts the current paradigm in media bias detection evaluation towards solutions that tackle not one but multiple media bias types simultaneously.

For many business applications that require the processing, indexing, and retrieval of professional documents such as legal briefs (in PDF format etc.), it is often essential to classify the pages of any given document into their corresponding types beforehand. Most existing studies in the field of document image classification either focus on single-page documents or treat multiple pages in a document independently. Although in recent years a few techniques have been proposed to exploit the context information from neighboring pages to enhance document page classification, they typically cannot be utilized with large pre-trained language models due to the constraint on input length. In this paper, we present a simple but effective approach that overcomes the above limitation. Specifically, we enhance the input with extra tokens carrying sequential information about previous pages - introducing recurrence - which enables the usage of pre-trained Transformer models like BERT for context-aware page classification. Our experiments conducted on two legal datasets in English and Portuguese respectively show that the proposed approach can significantly improve the performance of document page classification compared to the non-recurrent setup as well as the other context-aware baselines.

Subword tokenization has been widely successful in text-based natural language processing (NLP) tasks with Transformer-based models. As Transformer models become increasingly popular in symbolic music-related studies, it is imperative to investigate the efficacy of subword tokenization in the symbolic music domain. In this paper, we explore subword tokenization techniques, such as byte-pair encoding (BPE), in symbolic music generation and its impact on the overall structure of generated songs. Our experiments are based on three types of MIDI datasets: single track-melody only, multi-track with a single instrument, and multi-track and multi-instrument. We apply subword tokenization on post-musical tokenization schemes and find that it enables the generation of longer songs at the same time and improves the overall structure of the generated music in terms of objective metrics like structure indicator (SI), Pitch Class Entropy, etc. We also compare two subword tokenization methods, BPE and Unigram, and observe that both methods lead to consistent improvements. Our study suggests that subword tokenization is a promising technique for symbolic music generation and may have broader implications for music composition, particularly in cases involving complex data such as multi-track songs.

Duke law and philosophy professor and author Nita Farahany says the challenge for humans with quickly developing artificial intelligence is the ethical and legal constraints around it. As controversy swirls around the use of famous artists' vocals for AI-generated music, Grimes seems to be embracing the use of artificial intelligence in the music industry. In a tweet Sunday, the 33-year-old Canadian singer, whose real name is Claire Elise Boucher, said she is happy to have her voice featured on AI-simulated music tracks as long as she is compensated with royalties for successful songs. "I'll split 50 [percent] royalties on any successful AI generated song that uses my voice," Grimes, who shares two children with Elon Musk, tweeted. Feel free to use my voice without penalty.

"I'll split 50% royalties on any successful AI generated song that uses my voice. "Feel free to use my voice without penalty. I have no label and no legal bindings." The musician's declaration comes in the wake of streaming platforms removing an AI-generated song using simulated voices of Drake and The Weeknd. Universal Music Group (UMG), which represents both artists, called for the purge after "Heart on My Sleeve" garnered over 15 million listens on TikTok and 600,000 on Spotify.

In his afterword to the short murder mystery Death of an Author, the writer Stephen Marche invokes a concept called Moravec's paradox. Hans Moravec, a robotics scientist, observed that tasks human beings find challenging, such as playing chess, are easy for computers, while many of the actions human beings effortlessly perform without conscious thought, such as perception or oriented movement through space, are extremely difficult for the machines. Moravec's paradox is a useful way to think about the surprising ways that Death of an Author, described by its publisher as a "groundbreaking experiment" in artificial intelligence, succeeds. Jacob Weisberg, the head of podcast production company Pushkin Industries (and a former Slate editor in chief), asked Marche, a journalist who writes about artificial intelligence, to make Death of an Author earlier this year. The goal was a novella whose text was to be 95 percent computer-generated.

Voice assistants are everywhere now – on your phone, in your TV, possibly even in your kitchen appliances. But one of the most common ways that people interact with Siri, Alexa and the Google Assistant is through a smart speaker, and there are now a wide variety of such devices available at a wide variety of price points. There are downsides to having a smart home device that's always listening for a wake word, as giving more personal information to Amazon, Apple and Google can be a questionable decision. That said, all these companies have made it easier to manage how your data is used -- you can opt out of humans reviewing some of your voice queries, and it's also less complicated to manage and erase your history with various digital assistants, too. The good news is that there's never been a better time to get a smart speaker, particularly if you're a music fan. For all their benefits, the original Amazon Echo and Google Home devices did not sound good. Sonos, on the other hand, made great sounding WiFi-connected speakers, but they lacked any voice-controlled smarts. Sonos released its own voice assistant in 2022 and also supports Alexa on its latest speakers.

Companies clamor to use Remie Michelle Clarke's voice. An award-winning vocal artist, her smooth, Irish accent backs ads for Mazda and Mastercard and is the sound of Microsoft's search engine, Bing, in Ireland. But in January, her sound engineer told Michelle Clarke he'd found a voice that sounded uncannily like hers someplace unexpected: on Revoicer.com, For a modest monthly fee, Revoicer customers can access hundreds of different voices and, through an artificial intelligence-backed tool, morph them to say anything -- to voice commercials, recite corporate trainings or narrate books. Revoicer advertised "Olivia" with a photo of a gray-haired woman, who appeared to be of Asian descent, and a blurb: "A deep, calm and kind voice. A 38-year-old brunette, Michelle Clarke looked nothing like "Olivia." But when she hit play, she was greeted with the jarring sound of what could only be her own voice: "Hello my dear ones, my name is Olivia," it said. "I have a soft and caring voice."

This article was co-published with Understanding AI, a newsletter that explores how A.I. works and how it's changing our world. In 2011, venture capitalist Marc Andreessen published an essay that became a kind of manifesto for Silicon Valley during the 2010s. "Software is eating the world," Andreessen declared. Computers and the internet had already revolutionized a bunch of information-oriented businesses: books, movies, music, photography, telecommunications, and so forth. Software also played a major supporting role in more tangible industries. New cars had dozens of computer chips in them, for example, and the oil and gas industry made heavy use of software to discover new drilling sites. But Andreessen, co-founder of the venture capital firm Andreessen Horowitz, argued that the software revolution was only getting started.