Generative AI has the potential to create a new form of interactive media: AI-bridged creative language arts (CLA), which bridge the author and audience by personalizing the author's vision to the audience's context and taste at scale. However, it is unclear what the authors' values and attitudes would be regarding AI-bridged CLA. To identify these values and attitudes, we conducted an interview study with 18 authors across eight genres (e.g., poetry, comics) by presenting speculative but realistic AI-bridged CLA scenarios. We identified three benefits derived from the dynamics between author, artifact, and audience: those that 1) authors get from the process, 2) audiences get from the artifact, and 3) authors get from the audience. We found how AI-bridged CLA would either promote or reduce these benefits, along with authors' concerns. We hope our investigation hints at how AI can provide intriguing experiences to CLA audiences while promoting authors' values.

We present a novel framework for user representation in large-scale recommender systems, aiming at effectively representing diverse user taste in a generalized manner. Our approach employs a two-stage methodology combining representation learning and transfer learning. The representation learning model uses an autoencoder that compresses various user features into a representation space. In the second stage, downstream task-specific models leverage user representations via transfer learning instead of curating user features individually. We further augment this methodology on the representation's input features to increase flexibility and enable reaction to user events, including new user experiences, in Near-Real Time. Additionally, we propose a novel solution to manage deployment of this framework in production models, allowing downstream models to work independently. We validate the performance of our framework through rigorous offline and online experiments within a large-scale system, showcasing its remarkable efficacy across multiple evaluation tasks. Finally, we show how the proposed framework can significantly reduce infrastructure costs compared to alternative approaches.

Unmanned aerial vehicles are becoming common and have many productive uses. However, their increased prevalence raises safety concerns -- how can we protect restricted airspace? Knowing the type of unmanned aerial vehicle can go a long way in determining any potential risks it carries. For instance, fixed-wing craft can carry more weight over longer distances, thus potentially posing a more significant threat. This paper presents a machine learning model for classifying unmanned aerial vehicles as quadrotor, hexarotor, or fixed-wing. Our approach effectively applies a Long-Short Term Memory (LSTM) neural network for the purpose of time series classification. We performed experiments to test the effects of changing the timestamp sampling method and addressing the imbalance in the class distribution. Through these experiments, we identified the top-performing sampling and class imbalance fixing methods. Averaging the macro f-scores across 10 folds of data, we found that the majority quadrotor class was predicted well (98.16%), and, despite an extreme class imbalance, the model could also predicted a majority of fixed-wing flights correctly (73.15%). Hexarotor instances were often misclassified as quadrotors due to the similarity of multirotors in general (42.15%). However, results remained relatively stable across certain methods, which prompted us to analyze and report on their tradeoffs. The supplemental material for this paper, including the code and data for running all the experiments and generating the results tables, is available at https://osf.io/mnsgk/.

Music popularity prediction has garnered significant attention in both industry and academia, fuelled by the rise of data-driven algorithms and streaming platforms like Spotify. This study aims to explore the predictive power of various machine learning models in forecasting song popularity using a dataset comprising 30,000 songs spanning different genres from 1957 to 2020. Methods: We employ Ordinary Least Squares (OLS), Multivariate Adaptive Regression Splines (MARS), Random Forest, and XGBoost algorithms to analyse song characteristics and their impact on popularity. Results: Ordinary Least Squares (OLS) regression analysis reveals genre as the primary influencer of popularity, with notable trends over time. MARS modelling highlights the complex relationship between variables, particularly with features like instrumentalness and duration. Random Forest and XGBoost models underscore the importance of genre, especially EDM, in predicting popularity. Despite variations in performance, Random Forest emerges as the most effective model, improving prediction accuracy by 7.1% compared to average scores. Despite the importance of genre, predicting song popularity remains challenging, as observed variations in music-related features suggest complex interactions between genre and other factors. Consequently, while certain characteristics like loudness and song duration may impact popularity scores, accurately predicting song success remains elusive.

Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an explainer discusses a concept or phenomenon of interest with an explainee. Leaving the explainee with a clear understanding is not straightforward due to the knowledge gap between the two participants. Previous research looked at the interaction of explanation moves, dialogue acts, and topics in successful dialogues with expert explainers. However, daily-life explanations often fail, raising the question of what makes a dialogue successful. In this work, we study explanation dialogues in terms of the interactions between the explainer and explainee and how they correlate with the quality of explanations in terms of a successful understanding on the explainee's side. In particular, we first construct a corpus of 399 dialogues from the Reddit forum Explain Like I am Five and annotate it for interaction flows and explanation quality. We then analyze the interaction flows, comparing them to those appearing in expert dialogues. Finally, we encode the interaction flows using two language models that can handle long inputs, and we provide empirical evidence for the effectiveness boost gained through the encoding in predicting the success of explanation dialogues.

Last year Stack Overflow became one of the first websites to announce it would charge AI giants for access to content used to train chatbots. Now the popular Q&A service for coders has signed up its first customer--Google--in what CEO Prashanth Chandrasekar says is the start of a "meaningful" new stream of revenue. The deal is significant, because it remains unclear how broadly Google and other AI developers will pay for content needed for AI projects. Millions of books and websites have fueled the development of AI systems, but most publishers have not been compensated, and some are suing over what they allege is misuse. Many publishers, including Stack Overflow, appear threatened by ChatGPT and other generative AI products, which can answer queries that would have previously sent coders their way.

It used to be that you bought a new phone and got a fresh start. Now we just copy over all the junk from the old model onto the new one. Sure, it's faster and shinier, but it's packed with files you don't need, contacts you haven't talked to in years and photos you don't remember taking -- or want to see again. It doesn't take much effort to clear things out if you know what to do. You'll thank yourself (and me) later.

Apple TV has announced it's adapting William Gibson's Neuromancer into a 10-episode series. The novel debuted in 1984 and is largely thought to mark the birth of cyberpunk, which includes creations like The Matrix and Robocop. In fact, it's crazy that it has taken four decades for it to get the Hollywood treatment. Neuromancer follows "a damaged, top-rung super-hacker named Case who is thrust into a web of digital espionage and high stakes crime with his partner Molly, a razor-girl assassin with mirrored eyes aiming to pull a heist on a corporate dynasty with untold secrets," a release states. The story is being brought to the small screen by Graham Roland (Dark Winds, Tom Clancy's Jack Ryan) and JD Dillard (Devotion, Sweetheart), who will act as showrunner and director, respectively.

Who is behind the most notorious "deepfake" app on the internet? Trying to answer that question these past few months, for a new Guardian podcast series, Black Box, has been like wandering through a hall of mirrors. The app, ClothOff, has hundreds of thousands of followers and has already been used in a least two cases to generate dozens of images of underage girls – pictures that have left the girls traumatised, their parents outraged and the police baffled at how to stop it. Producers Josh Kelly, Alex Atack and I have followed ClothOff's trail to nondescript addresses in central London that appear to be unoccupied. We have encountered sham businesses, distorted voices and photographs of fake employees.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A schoolboy has completed one of his largest portraits yet by using over 1,500 Rubik's Cubes to resemble the prime minister of the United Kingdom. Henil Soni is an 11-year-old from Harwich, Essex, England, who began his infatuation with the handheld puzzle when he was just five years old, according to SWNS, the British news service. Soni, who can now solve the well-known puzzle in mere seconds, is taking his talents to a larger scale by making portraits out of the colors on the cube.