Last weekend, Hollie Mengert woke up to an email pointing her to a Reddit thread, the first of several messages from friends and fans, informing the Los Angeles-based illustrator and character designer that she was now an AI model. The day before, a Redditor named MysteryInc152 posted on the Stable Diffusion subreddit, "2D illustration Styles are scarce on Stable Diffusion, so I created a DreamBooth model inspired by Hollie Mengert's work." Using 32 of her illustrations, MysteryInc152 fine-tuned Stable Diffusion to recreate Hollie Mengert's style. He then released the checkpoint under an open license for anyone to use. The model uses her name as the identifier for prompts: "illustration of a princess in the forest, holliemengert artstyle," for example.

Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns. To address these limitations, recent research has explored the use of WiFi antennas (1D sensors) for body segmentation and key-point body detection.

Accuracy and diversity have long been considered to be two conflicting goals for recommendations. We point out, however, that as the diversity is typically measured by certain pre-selected item attributes, e.g., category as the most popularly employed one, improved diversity can be achieved without sacrificing recommendation accuracy, as long as the diversification respects the user's preference about the pre-selected attributes. This calls for a fine-grained understanding of a user's preferences over items, where one needs to recognize the user's choice is driven by the quality of the item itself, or the pre-selected attributes of the item. In this work, we focus on diversity defined on item categories. We propose a general diversification framework agnostic to the choice of recommendation algorithms. Our solution disentangles the learnt user representation in the recommendation module into category-independent and category-dependent components to differentiate a user's preference over items from two orthogonal perspectives. Experimental results on three benchmark datasets and online A/B test demonstrate the effectiveness of our solution in improving both recommendation accuracy and diversity. In-depth analysis suggests that the improvement is due to our improved modeling of users' categorical preferences and refined ranking within item categories.

Check-worthiness detection is the task of identifying claims, worthy to be investigated by fact-checkers. Resource scarcity for non-world languages and model learning costs remain major challenges for the creation of models supporting multilingual check-worthiness detection. This paper proposes cross-training adapters on a subset of world languages, combined by adapter fusion, to detect claims emerging globally in multiple languages. (1) With a vast number of annotators available for world languages and the storage-efficient adapter models, this approach is more cost efficient. Models can be updated more frequently and thus stay up-to-date. (2) Adapter fusion provides insights and allows for interpretation regarding the influence of each adapter model on a particular language. The proposed solution often outperformed the top multilingual approaches in our benchmark tasks.

We present a methodology to support the analysis of culture from text such as news events and demonstrate its usefulness on categorizing news events from different categories (society, business, health, recreation, science, shopping, sports, arts, computers, games and home) across different geographical locations (different places in 117 countries). We group countries based on the culture that they follow and then filter the news events based on their content category. The news events are automatically labelled with the help of Hofstedes cultural dimensions. We present combinations of events across different categories and check the performances of different classification methods. We also presents experimental comparison of different number of features in order to find a suitable set to represent the culture.

Automatically generating or captioning music playlist titles given a set of tracks is of significant interest in music streaming services as customized playlists are widely used in personalized music recommendation, and well-composed text titles attract users and help their music discovery. We present an encoder-decoder model that generates a playlist title from a sequence of music tracks. While previous work takes track IDs as tokenized input for playlist title generation, we use artist IDs corresponding to the tracks to mitigate the issue from the long-tail distribution of tracks included in the playlist dataset. Also, we introduce a chronological data split method to deal with newly-released tracks in real-world scenarios. Comparing the track IDs and artist IDs as input sequences, we show that the artist-based approach significantly enhances the performance in terms of word overlap, semantic relevance, and diversity.

Detection of news propagation barriers, being economical, cultural, political, time zonal, or geographical, is still an open research issue. We present an approach to barrier detection in news spreading by utilizing Wikipedia-concepts and metadata associated with each barrier. Solving this problem can not only convey the information about the coverage of an event but it can also show whether an event has been able to cross a specific barrier or not. Experimental results on IPoNews dataset (dataset for information spreading over the news) reveals that simple classification models are able to detect barriers with high accuracy. We believe that our approach can serve to provide useful insights which pave the way for the future development of a system for predicting information spreading barriers over the news.

We apply a large multilingual language model (BLOOM-176B) in open-ended generation of Chinese song lyrics, and evaluate the resulting lyrics for coherence and creativity using human reviewers. We find that current computational metrics for evaluating large language model outputs (MAUVE) have limitations in evaluation of creative writing. We note that the human concept of creativity requires lyrics to be both comprehensible and distinctive -- and that humans assess certain types of machine-generated lyrics to score more highly than real lyrics by popular artists. Inspired by the inherently multimodal nature of album releases, we leverage a Chinese-language stable diffusion model to produce high-quality lyric-guided album art, demonstrating a creative approach for an artist seeking inspiration for an album or single. Finally, we introduce the MojimLyrics dataset, a Chinese-language dataset of popular song lyrics for future research.

The 2013 video game "The Last of Us" was a hit with critics and players thanks to a powerful narrative. Ten years later, that story is headed to television on HBO in what the industry hopes is a harbinger for artfully adapting video games to TV and film. "The Last of Us," created by video game developer Naughty Dog and published by Sony Entertainment, follows hardened survivor Joel and his young protege Ellie as they navigate a post-pandemic world fighting people and mutated creatures. The zombie thriller, which premieres on Sunday, stars "Game of Thrones" veterans Pedro Pascal and Bella Ramsey. The PlayStation game won numerous awards, including "Game of the Year" at the 17th Annual Design Innovate Communicate Entertain summit (DICE), which honors video game industry professionals.

OpenAI researchers collaborated with Georgetown University's Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together 30 disinformation researchers, machine learning experts, and policy analysts, and culminated in a co-authored report building on more than a year of research. This report outlines the threats that language models pose to the information environment if used to augment disinformation campaigns and introduces a framework for analyzing potential mitigations. As generative language models improve, they open up new possibilities in fields as diverse as healthcare, law, education and science. But, as with any new technology, it is worth considering how they can be misused.