CNET, the venerable tech site which began publication nearly 30 years ago, has become the latest digital media company whose staff have chosen to band together and demand more. The CNET Media Worker's Union (CMWU) today sent a letter to Red Ventures, the private equity concern which purchased CNET in 2020, seeking recognition of a bargaining unit of nearly 100 workers including editors, writers, and video producers. According to CMWU, a supermajority of those in the unit signed union authorization cards. Like the overwhelming majority of other organized digital publications, the workers who make up CMWU are responding in large part to an increasingly hostile financial climate in the industry. CNET has not been spared the same tumult that has led to the shuttering of Buzzfeed News and VICE's decision to file for bankruptcy: the company went through three brutal rounds of layoffs over recent months, the most recent of which stripped approximately a dozen staffers from the masthead.

When we first reviewed Apple's HomePod mini in 2020, we had some reservations. While it was a much better value than the original HomePod, it still had some of the same limitations of its bigger sibling. Siri wasn't as bright as Alexa or Google Assistant, the HomeKit ecosystem was limited and there were no real alternatives to Apple Music for on-demand tunes. You bought a mini for Apple's tight integration, and not much else. Apple has significantly expanded the HomePod mini's functionality.

Artificial intelligence (AI) has proved a controversial matter in the music industry – resulting in legal tussles, job losses and a decline in musical quality. However, British pop icons the Pet Shop Boys argue that the technology can be used in a positive way in the creative process. The group's singer, Neil Tennant, said AI could'fill in the blanks' if a song has been left unfinished, such as when the composer is suffering from writer's block. Tennant and his bandmate Chris Lowe said they are looking at new technology as they prepare their'Dreamworld' greatest hits tour in Europe this summer. 'There's a song that we wrote a chorus for in 2003 and we never finished because I couldn't think of anything for the verses,' Tennant told the Radio Times.

"Blurb writing is a mini art form," Iris Murdoch once wrote in a letter to former Penguin blurb writer Elizabeth Buchan. And like many other art forms, companies have been experimenting with the idea that it could be created without an artist. A German company that provides digital book distribution and marketing services to publishers has announced it will integrate ChatGPT, a chatbot that answers questions by drawing on publicly available internet data, into its software. "During the beta phase, publishers can test the benefits of the artificial intelligence tool for their digital book marketing," states Bookwire, adding that it will only use ChatGPT if a publisher agrees and the disclaimer that the company "does not assume any responsibility for the content created by ChatGPT". But there is also another question that needs to be asked.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' Artificial intelligence has its own idea of what the perfect human body should look like. A new study by The Bulimia Project, a Brooklyn, New York-based website that publishes content and research related to eating disorders, investigated how AI perceived the "ideal" body based on social media data. The results, produced by AI-generated imaging tools such as Dall-E 2, Stable Diffusion and Midjourney, showed widely "unrealistic" body structures, as reported in a discussion of the findings on The Bulimia Project's website. Forty percent of the overall images depicted "unrealistic" body types of muscular men and women -- 37% for women and 43% for men -- according to the study.

In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction. It has been shown highly dependent on the provided demonstrations and thus promotes the research of demonstration retrieval: given a test input, relevant examples are retrieved from the training set to serve as informative demonstrations for in-context learning. While previous works focus on training task-specific retrievers for several tasks separately, these methods are often hard to transfer and scale on various tasks, and separately trained retrievers incur a lot of parameter storage and deployment cost. In this paper, we propose Unified Demonstration Retriever (\textbf{UDR}), a single model to retrieve demonstrations for a wide range of tasks. To train UDR, we cast various tasks' training signals into a unified list-wise ranking formulation by language model's feedback. Then we propose a multi-task list-wise ranking training framework, with an iterative mining strategy to find high-quality candidates, which can help UDR fully incorporate various tasks' signals. Experiments on 30+ tasks across 13 task families and multiple data domains show that UDR significantly outperforms baselines. Further analyses show the effectiveness of each proposed component and UDR's strong ability in various scenarios including different LMs (1.3B - 175B), unseen datasets, varying demonstration quantities, etc.

Modeling long-term dependencies for audio signals is a particularly challenging problem, as even small-time scales yield on the order of a hundred thousand samples. With the recent advent of Transformers, neural architectures became good at modeling dependencies over longer time scales, but they suffered from quadratic constraints to scale them. We propose a generative auto-regressive architecture that can model audio waveforms over quite a large context, greater than 500,000 samples. Our work is adapted to learn time dependencies by learning a latent representation by a CNN front-end, and then learning dependencies over these representations using Transformer encoders, fully trained end-to-end: thereby allowing to learn representations as it deems fit for the next sample. Unlike previous works that compared different time scales to show improvement, we use a standard dataset, with the same number of parameters/context to show improvements. We achieve a state-of-the-art performance as compared to other approaches such as Wavenet, SaSHMI, and Sample-RNN on a standard dataset for modeling long-term structure. This work gives very exciting direction for the field, given improvements in context modeling that can be scaled with more data, as well as potentially better results by using billions/trillions of parameters.

In the current landscape of ever-increasing levels of digitalization, we are facing major challenges pertaining to scalability. Recommender systems have become irreplaceable both for helping users navigate the increasing amounts of data and, conversely, aiding providers in marketing products to interested users. The growing awareness of discrimination in machine learning methods has recently motivated both academia and industry to research how fairness can be ensured in recommender systems. For recommender systems, such issues are well exemplified by occupation recommendation, where biases in historical data may lead to recommender systems relating one gender to lower wages or to the propagation of stereotypes. In particular, consumer-side fairness, which focuses on mitigating discrimination experienced by users of recommender systems, has seen a vast number of diverse approaches for addressing different types of discrimination. The nature of said discrimination depends on the setting and the applied fairness interpretation, of which there are many variations. This survey serves as a systematic overview and discussion of the current research on consumer-side fairness in recommender systems. To that end, a novel taxonomy based on high-level fairness interpretation is proposed and used to categorize the research and their proposed fairness evaluation metrics. Finally, we highlight some suggestions for the future direction of the field.

As people's aesthetic preferences for images are far from understood, image aesthetic assessment is a challenging artificial intelligence task. The range of factors underlying this task is almost unlimited, but we know that some aesthetic attributes affect those preferences. In this study, we present a multi-task convolutional neural network that takes into account these attributes. The proposed neural network jointly learns the attributes along with the overall aesthetic scores of images. This multi-task learning framework allows for effective generalization through the utilization of shared representations. Our experiments demonstrate that the proposed method outperforms the state-of-the-art approaches in predicting overall aesthetic scores for images in one benchmark of image aesthetics. We achieve near-human performance in terms of overall aesthetic scores when considering the Spearman's rank correlations. Moreover, our model pioneers the application of multi-tasking in another benchmark, serving as a new baseline for future research. Notably, our approach achieves this performance while using fewer parameters compared to existing multi-task neural networks in the literature, and consequently makes our method more efficient in terms of computational complexity.

In response to disinformation and propaganda from Russian online media following the Russian invasion of Ukraine, Russian outlets including Russia Today and Sputnik News were banned throughout Europe. To maintain viewership, many of these Russian outlets began to heavily promote their content on messaging services like Telegram. In this work, we study how 16 Russian media outlets interacted with and utilized 732 Telegram channels throughout 2022. Leveraging the foundational model MPNet, DP-means clustering, and Hawkes Processes, we trace how narratives spread between news sites and Telegram channels. We show that news outlets not only propagate existing narratives through Telegram, but that they source material from the messaging platform. Across the sites in our study, between 2.3% (ura.news) and 26.7% (ukraina.ru) of articles discuss content that originated/resulted from activity on Telegram. Finally, tracking the spread of individual topics, we measure the rate at which news websites and their Telegram channels disseminate content within the Russian media ecosystem.