Arcadia Publishing built its empire on small-town storytellers. Now it wants to sell their words to an A.I. company no one will name. Enter your email to receive alerts for this author. You can manage your newsletter subscriptions at any time. You're already subscribed to the aa_Nitish_Pahwa newsletter. You can manage your newsletter subscriptions at any time.

Xynapse Traces is an experimental publishing imprint created via a fusion of human and algorithmic methods using a configuration-driven architecture and a multi-model AI integration framework. The system achieved a remarkable 90% reduction in time-to-market (from a typical 6-12 months to just 2-4 weeks), with 80% cost reduction compared to traditional imprint development, while publishing 52 books in its first year and maintaining exceptional quality metrics, including 99% citation accuracy and 100% validation success after initial corrections. Key technical innovations include a continuous ideation pipeline with tournament-style evaluation, a novel codex design for transcriptive meditation practice, comprehensive automation spanning from ideation through production and distribution, and publisher personas that define and guide the imprint's mission. The system also integrates automated verification with human oversight, ensuring that gains in speed do not compromise publishing standards. This effort has significant implications for the future of book publishing, suggesting new paradigms for human-AI collaboration that democratize access to sophisticated publishing capabilities and make previously unviable niche markets accessible.

The rise and growing popularity of accessible large language models have raised questions about their impact on various aspects of life, including how scientists write and publish their research. The primary objective of this paper is to analyze a dataset consisting of all abstracts posted on arXiv.org between 2010 and June 7th, 2024, to assess the evolution of their readability and determine whether significant shifts occurred following the release of ChatGPT in November 2022 . Four standard readability for mulas are used to calculate individual readability scores for each paper, classifying their level of readability. These scores are then aggregated by year and across the eight primary categories covered by the platform. The results show a steady annual decrease in readability, suggesting that abstracts are likely becoming increasingly complex. Additionally, following the release of ChatGPT, a significant change in readability is observed for 2023 and the analyzed months of 2024. Similar trends are found acr oss categories, with most experiencing a notable change in readability during 2023 and 2024. These findings offer insights into the broader changes in readability and point to the likely influence of AI on scientific writing.

When tech companies first rolled out generative-AI products, some critics immediately feared a media collapse. Every bit of writing, imagery, and video became suspect. But for news publishers and journalists, another calamity was on the horizon. Chatbots have proved adept at keeping users locked into conversations. They do so by answering every question, often through summarizing articles from news publishers.

EXCLUSIVE: First lady Melania Trump is launching an audiobook of her memoir using artificial intelligence (AI) audio technology in multiple languages, Fox News Digital has learned. The first lady released her first memoir, "Melania," last year. This week, she is breaking new ground by releasing "Melania, the Audiobook," which has been "created entirely" with AI. "I am proud to be at the forefront of publishing's new frontier – the intersection of artificial intelligence technology and audio," Trump told Fox News Digital. The first lady said ElevenLabs AI developed "an AI-generated replica of my voice under strict supervision, which will establish an unforgettable connection with my personal story, in multiple languages for listeners worldwide." ElevenLabs AI CEO Mati Staniszewski told Fox News Digital that they are "excited that Melania Trump trusted our technology to power this first-of-its-kind audiobook project."

In the summer of 1985, I made the long pilgrimage from my home in Cheadle Hulme to London's glamorous Hammersmith Novotel for the Commodore computer show. As a 14-year-old gamer, this was a chance to play the latest titles and see some cool new joysticks, but I was also desperate to visit one particular exhibitor: the publisher Newsfield, home of the wildly popular games mags Crash and Zzap!64. By the time I arrived there was already a long queue of kids at the small stand and most of them were waiting to have their show programmes signed by reigning arcade game champion and Zzap reviewer, Julian Rignall. As an ardent subscriber, I can still remember the thrill of standing in that line, the latest copy of the mag clutched in my sweaty hands. I wouldn't feel this starstruck again until I met Sigourney Weaver a quarter of a century later.

This study proposes a data-driven framework for enhancing the accuracy and efficiency of scientific peer review through an open, bottom-up process that estimates reviewer quality. Traditional closed peer review systems, while essential for quality control, are often slow, costly, and subject to biases that can impede scientific progress. Here, we introduce a method that evaluates individual reviewer reliability by quantifying agreement with community consensus scores and applying Bayesian weighting to refine paper quality assessments. We analyze open peer review data from two major scientific conferences, and demonstrate that reviewer-specific quality scores significantly improve the reliability of paper quality estimation. Perhaps surprisingly, we find that reviewer quality scores are unrelated to authorship quality. Our model incorporates incentive structures to recognize high-quality reviewers and encourage broader coverage of submitted papers, thereby mitigating the common "rich-get-richer" pitfall of social media. These findings suggest that open peer review, with mechanisms for estimating and incentivizing reviewer quality, offers a scalable and equitable alternative for scientific publishing, with potential to enhance the speed, fairness, and transparency of the peer review process.

Kate Mosse and Richard Osman have hit back at Labour's plan to give artificial intelligence companies broad freedoms to mine artistic works for data, saying it could destroy growth in creative fields and amount to theft. It is seen as a way of supercharging the growth of AI companies in the UK. Last month Paul McCartney warned that AI "could just take over", and Kate Bush joined Stephen Fry and Hugh Bonneville in signing a petition warning that the "unlicensed use of creative works for training generative AI is a major, unjust threat to the livelihoods of the people behind those works, and must not be permitted". Mosse told the Guardian: "Using AI responsibly and well and being a world leader – all of this I agree with. It just cannot be at the expense of the creative industries … It is supporting one type of growth and destroying another part of growth. And it cannot be on the basis of theft of our work."

As model parameter sizes reach the billion-level range and their training consumes zettaFLOPs of computation, components reuse and collaborative development are become increasingly prevalent in the Machine Learning (ML) community. These components, including models, software, and datasets, may originate from various sources and be published under different licenses, which govern the use and distribution of licensed works and their derivatives. However, commonly chosen licenses, such as GPL and Apache, are software-specific and are not clearly defined or bounded in the context of model publishing. Meanwhile, the reused components may also have free-content licenses and model licenses, which pose a potential risk of license noncompliance and rights infringement within the model production workflow. In this paper, we propose addressing the above challenges along two lines: 1) For license analysis, we have developed a new vocabulary for ML workflow management and encoded license rules to enable ontological reasoning for analyzing rights granting and compliance issues. 2) For standardized model publishing, we have drafted a set of model licenses that provide flexible options to meet the diverse needs of model publishing. Our analysis tool is built on Turtle language and Notation3 reasoning engine, envisioned as a first step toward Linked Open Model Production Data. We have also encoded our proposed model licenses into rules and demonstrated the effects of GPL and other commonly used licenses in model publishing, along with the flexibility advantages of our licenses, through comparisons and experiments.

Blumhouse wasn't going to publish a game in 2024. The studio, one of the leading names in horror films, announced in February 2023 that it was launching a video game publishing business and executives were scouting projects from independent teams with budgets under 10 million. The goal of Blumhouse Games was to support a few rad horror titles per year, with a tentative plan to start publishing them in 2025. But then, in September 2023, the Blumhouse folks stumbled across Fear the Spotlight. It was a moody, voxelized horror game about two friends sneaking around their haunted high school and communing with the ghosts of students that died in a fire in the '90s.