Condé Nast, the media conglomerate that owns publications like The New Yorker, Vogue and Wired, has announced a multi-year partnership OpenAI to display content from Condé Nast titles in ChatGPT as well as SearchGPT, the company's prototype AI-powered search engine. The partnership comes amid growing concerns over the unauthorized use of publishers' content by AI companies. Last month, Condé Nast sent a cease-and-desist letter to AI search startup Perplexity, accusing it of plagiarism for using its content to generate answers. "Over the last decade, news and digital media have faced steep challenges as many technology companies eroded publishers' ability to monetize content, most recently with traditional search," Condé Nast CEO Roger Lynch wrote to employees in a memo that was first reported by Semafor's Max Tani. "Our partnership with OpenAI begins to make up for some of that revenue, allowing us to continue to protect and invest in our journalism and creative endeavors."

Condé Nast and OpenAI have struck a multi-year deal that will allow the AI giant to use content from the media giant's roster of properties--which includes the New Yorker, Vogue, Vanity Fair, Bon Appetit, and, yes, WIRED. The deal will allow OpenAI to surface stories from these outlets in both ChatGPT and the new SearchGPT prototype. "It's crucial that we meet audiences where they are and embrace new technologies while also ensuring proper attribution and compensation for use of our intellectual property," Condé Nast CEO Roger Lynch wrote in a company-wide email. Lynch pointed to ongoing turmoil within the publishing industry while discussing the deal, noting that technology companies have made it harder for publishers to make money, most recently with changes to traditional search. "Our partnership with OpenAI begins to make up for some of that revenue, allowing us to continue to protect and invest in our journalism and creative endeavors," he wrote.

Once upon a time in the tail-end of the last century, there was something called the Y2K bug. This bit of computer code was supposed to herald a global robot apocalypse at the stroke of midnight when 1999 became the year 2000 because of, uh, clock dates or something. The film imagines a New Year's Eve of 1999 in which the computers really did turn on humanity. It's written and directed by SNL alum Kyle Mooney, who made the fantastic and underrated Brigsby Bear. As you can see from the trailer, it's a 1990s teen party comedy, like Can't Hardly Wait, but also an apocalyptic horror film.

Lucasfilm has decided not to renew The Acolyte for a second season, according to Deadline and Variety. Fans won't get to see how the show was supposed to end and won't get to know how the plotlines its creator, Leslye Headland (Russian Doll), teased at the end of the first season would unravel. Engadget Senior Editor Devindra Hardawar called The Acolyte "Star Wars at its best" in his review, discussed how unique its premise was, and drew parallels between the series and Crouching Tiger, Hidden Dragon. Deadline says the show had a strong start and garnered 4.8 million views in the first day it became available for streaming, reaching 11.1 million views after five days. However, viewership fell in the coming weeks, and its finale was reportedly the poorest performing finale for a Star Wars series.

In the era dominated by information overload and its facilitation with Large Language Models (LLMs), the prevalence of misinformation poses a significant threat to public discourse and societal well-being. A critical concern at present involves the identification of machine-generated news. In this work, we take a significant step by introducing a benchmark dataset designed for neural news detection in four languages: English, Turkish, Hungarian, and Persian. The dataset incorporates outputs from multiple multilingual generators (in both, zero-shot and fine-tuned setups) such as BloomZ, LLaMa-2, Mistral, Mixtral, and GPT-4. Next, we experiment with a variety of classifiers, ranging from those based on linguistic features to advanced Transformer-based models and LLMs prompting. We present the detection results aiming to delve into the interpretablity and robustness of machine-generated texts detectors across all target languages.

Researchers and practitioners interested in computational politics rely on automatic content analysis tools to make sense of the large amount of political texts available on the Web. Such tools should provide objective and subjective aspects at different granularity levels to make the analyses useful in practice. Existing methods produce interesting insights for objective aspects, but are limited for subjective ones, are often limited to national contexts, and have limited explainability. We introduce a text analysis framework which integrates both perspectives and provides a fine-grained processing of subjective aspects. Information retrieval techniques and knowledge bases complement powerful natural language processing components to allow a flexible aggregation of results at different granularity levels. Importantly, the proposed bottom-up approach facilitates the explainability of the obtained results. We illustrate its functioning with insights on news outlets, political orientations, topics, individual entities, and demographic segments. The approach is instantiated on a large corpus of French news, but is designed to work seamlessly for other languages and countries.

While the classic Prospect Theory has highlighted the reference-dependent and comparative nature of consumers' product evaluation processes, few models have successfully integrated this theoretical hypothesis into data-driven preference quantification, particularly in the realm of recommender systems development. To bridge this gap, we propose a new research problem of modeling reference-dependent preferences from a data-driven perspective, and design a novel deep learning-based framework named Attributed Reference-dependent Choice Model for Recommendation (ArcRec) to tackle the inherent challenges associated with this problem. ArcRec features in building a reference network from aggregated historical purchase records for instantiating theoretical reference points, which is then decomposed into product attribute specific sub-networks and represented through Graph Neural Networks. In this way, the reference points of a consumer can be encoded at the attribute-level individually from her past experiences but also reflect the crowd influences. ArcRec also makes novel contributions to quantifying consumers' reference-dependent preferences using a deep neural network-based utility function that integrates both interest-inspired and price-inspired preferences, with their complex interaction effects captured by an attribute-aware price sensitivity mechanism. Most importantly, ArcRec introduces a novel Attribute-level Willingness-To-Pay measure to the reference-dependent utility function, which captures a consumer's heterogeneous salience of product attributes via observing her attribute-level price tolerance to a product. Empirical evaluations on both synthetic and real-world online shopping datasets demonstrate ArcRec's superior performances over fourteen state-of-the-art baselines.

Retrieval augmented generation (RAG) systems augment how knowledge language models are by integrating external information sources such as Wikipedia, internal documents, scientific papers, or the open internet. RAG systems that rely on the open internet as their knowledge source have to contend with the complexities of human-generated content. Human communication extends much deeper than just the words rendered as text. Intent, tonality, and connotation can all change the meaning of what is being conveyed. Recent real-world deployments of RAG systems have shown some difficulty in understanding these nuances of human communication. One significant challenge for these systems lies in processing sarcasm. Though the Large Language Models (LLMs) that make up the backbone of these RAG systems are able to detect sarcasm, they currently do not always use these detections for the subsequent processing of text. To address these issues, in this paper, we synthetically generate sarcastic passages from Natural Question's Wikipedia retrieval corpus. We then test the impact of these passages on the performance of both the retriever and reader portion of the RAG pipeline. We introduce a prompting system designed to enhance the model's ability to interpret and generate responses in the presence of sarcasm, thus improving overall system performance. Finally, we conduct ablation studies to validate the effectiveness of our approach, demonstrating improvements in handling sarcastic content within RAG systems.

It has been a long-standing research goal to endow robot hands with human-level dexterity. Bi-manual robot piano playing constitutes a task that combines challenges from dynamic tasks, such as generating fast while precise motions, with slower but contact-rich manipulation problems. Although reinforcement learning based approaches have shown promising results in single-task performance, these methods struggle in a multi-song setting. Our work aims to close this gap and, thereby, enable imitation learning approaches for robot piano playing at scale. To this end, we introduce the Robot Piano 1 Million (RP1M) dataset, containing bi-manual robot piano playing motion data of more than one million trajectories. We formulate finger placements as an optimal transport problem, thus, enabling automatic annotation of vast amounts of unlabeled songs. Benchmarking existing imitation learning approaches shows that such approaches reach state-of-the-art robot piano playing performance by leveraging RP1M.

There is a wide variety of methods, tools and resources for processing text in the English language. However this is not the case for the Greek language even though it has a long documented history spanning at least 3,400 years of written records (including texts in syllabic script), and 28 centuries (Archaic period - new) of written text with alphabet [1, 2]. The over 2500 years literary tradition of Greek is also notable. To aid those that are interested in using, developing or advancing the techniques for Greek processing, in this paper we survey related works and resources organized in categories. We hope this collection and categorization of works to be useful for students and researchers interested in NLP tasks, Information Retrieval and Knowledge Management for the Greek language.