Comedian Anthony Rodia discusses the comedy industry and talks about the inspiration behind his jokes on'One Nation.' Though he may be covered in tattoos from head to toe -- quite literally -- the only thing more obvious than comedian Shayne Smith's body art lately might be his newfound Catholicism. And the former motorcycle gang member is certainly in good company. Jim Gaffigan, Kevin James, Stephen Colbert, Tom Leopold, Russell Brand, and Rob Schneider are just a few other comedians who share in the same faith -- the latter half of the boisterous bunch having converted to Catholicism in their adulthood. The former half has been just as busy keeping Catholicism alive: Gaffigan recently performed at The Sheen Center for Thought & Culture, at which Cardinal Timothy Dolan is a board member; Kevin James reportedly hosted a Catholic retreat before the pandemic; and Stephen Colbert is known for teaching Sunday school.

In online advertising, advertisers commonly utilize auto-bidding services to bid for impression opportunities. A typical objective of the auto-bidder is to optimize the advertiser's cumulative value of winning impressions within specified budget constraints. However, such a problem is challenging due to the complex bidding environment faced by diverse advertisers. To address this challenge, we introduce ABPlanner, a few-shot adaptable budget planner designed to improve budget-constrained auto-bidding. ABPlanner is based on a hierarchical bidding framework that decomposes the bidding process into shorter, manageable stages. Within this framework, ABPlanner allocates the budget across all stages, allowing a low-level auto-bidder to bids based on the budget allocation plan. The adaptability of ABPlanner is achieved through a sequential decision-making approach, inspired by in-context reinforcement learning. For each advertiser, ABPlanner adjusts the budget allocation plan episode by episode, using data from previous episodes as prompt for current decisions. This enables ABPlanner to quickly adapt to different advertisers with few-shot data, providing a sample-efficient solution. Extensive simulation experiments and real-world A/B testing validate the effectiveness of ABPlanner, demonstrating its capability to enhance the cumulative value achieved by auto-bidders.

In this paper, we study how well humans can detect text generated by commercial LLMs (GPT-4o, Claude, o1). We hire annotators to read 300 non-fiction English articles, label them as either human-written or AI-generated, and provide paragraph-length explanations for their decisions. Our experiments show that annotators who frequently use LLMs for writing tasks excel at detecting AI-generated text, even without any specialized training or feedback. In fact, the majority vote among five such "expert" annotators misclassifies only 1 of 300 articles, significantly outperforming most commercial and open-source detectors we evaluated even in the presence of evasion tactics like paraphrasing and humanization. Qualitative analysis of the experts' free-form explanations shows that while they rely heavily on specific lexical clues ('AI vocabulary'), they also pick up on more complex phenomena within the text (e.g., formality, originality, clarity) that are challenging to assess for automatic detectors. We release our annotated dataset and code to spur future research into both human and automated detection of AI-generated text.

Multimodal knowledge graph completion (MMKGC) aims to predict missing links in multimodal knowledge graphs (MMKGs) by leveraging information from various modalities alongside structural data. Existing MMKGC approaches primarily extend traditional knowledge graph embedding (KGE) models, which often require creating an embedding for every entity. This results in large model sizes and inefficiencies in integrating multimodal information, particularly for real-world graphs. Meanwhile, Transformer-based models have demonstrated competitive performance in knowledge graph completion (KGC). However, their focus on single-modal knowledge limits their capacity to utilize cross-modal information. Recently, Large vision-language models (VLMs) have shown potential in cross-modal tasks but are constrained by the high cost of training. In this work, we propose a novel approach that integrates Transformer-based KGE models with cross-modal context generated by pre-trained VLMs, thereby extending their applicability to MMKGC. Specifically, we employ a pre-trained VLM to transform relevant visual information from entities and their neighbors into textual sequences. We then frame KGC as a sequence-to-sequence task, fine-tuning the model with the generated cross-modal context. This simple yet effective method significantly reduces model size compared to traditional KGE approaches while achieving competitive performance across multiple large-scale datasets with minimal hyperparameter tuning.

Melody represents a move toward creating robots that closely resemble humans. In a world where loneliness is becoming increasingly prevalent, researchers have taken a bold step forward by introducing Melody, a life-sized artificial intelligence robot designed to combat this growing epidemic. However, Melody is not just another gadget; she represents a significant move toward creating robots that closely resemble humans in both appearance and interaction. Realbotix, the innovative tech firm responsible for Melody's creation, aims to produce robots that are not only visually indistinguishable from humans but also capable of meaningful interactions. According to CEO Andrew Kiguel, "Melody was created with the intention of having robots that are easy to travel with and modify for various forms of personal interaction."

The proposals could remove the incentive for writers and artists and result in a "loss of creativity", he told the BBC. McCartney, one of the two surviving members of the Beatles, said: "You get young guys, girls, coming up, and they write a beautiful song, and they don't own it, and they don't have anything to do with it. And anyone who wants can just rip it off." "The truth is, the money's going somewhere … Somebody's getting paid, so why shouldn't it be the guy who sat down and wrote Yesterday?" In contrast, some publishing organisations and media outlets have signed licensing deals with AI companies to allow them to use their material to train such models.

Generative AI programmes mine, or learn, from vast amounts of data like text, images, or music online to generate new content which feels like it has been made by a human. The proposals would give artists or creators a so called "rights reservation" – the ability to opt out. But critics of the plan believe it is not possible for an individual writer or artist to notify thousands of different AI service providers that they do not want their content used in that way, or to monitor what has happened to their work across the whole internet. An alternative proposal for artists to opt in to give their permission for their content to be used will be put forward in the House of Lords by cross bench peer Baroness Kidron this week. "It would be a wild punt against the creative sector that is already contributing over 120bn to the economy and be counterproductive to the government's own growth ambitions.

Due to the scarcity of data in low-resourced languages, the development of language models for these languages has been very slow. Currently, pre-trained language models have gained popularity in natural language processing, especially, in developing domain-specific models for low-resourced languages. In this study, we experiment with the impact of using occlusion-based techniques when training a language model for a text generation task. We curate 2 new datasets, the Sepedi monolingual (SepMono) dataset from several South African resources and the Sepedi radio news (SepNews) dataset from the radio news domain. We use the SepMono dataset to pre-train transformer-based models using the occlusion and non-occlusion pre-training techniques and compare performance. The SepNews dataset is specifically used for fine-tuning. Our results show that the non-occlusion models perform better compared to the occlusion-based models when measuring validation loss and perplexity. However, analysis of the generated text using the BLEU score metric, which measures the quality of the generated text, shows a slightly higher BLEU score for the occlusion-based models compared to the non-occlusion models.

This paper presents an approach that combines Human-In-The-Loop Reinforcement Learning (HITL RL) with principles derived from music theory to facilitate real-time generation of musical compositions. HITL RL, previously employed in diverse applications such as modelling humanoid robot mechanics and enhancing language models, harnesses human feedback to refine the training process. In this study, we develop a HILT RL framework that can leverage the constraints and principles in music theory. In particular, we propose an episodic tabular Q-learning algorithm with an epsilon-greedy exploration policy. The system generates musical tracks (compositions), continuously enhancing its quality through iterative human-in-the-loop feedback. The reward function for this process is the subjective musical taste of the user.

Artificial intelligence is reshaping creative domains, yet its co-creative processes, especially in group settings with novice users, remain under explored. To bridge this gap, we conducted a case study in a college-level course where nine undergraduate students were tasked with creating three original music tracks using AI tools over 10 weeks. The study spanned the entire creative journey from ideation to releasing these songs on Spotify. Participants leveraged AI for music and lyric production, cover art, and distribution. Our findings highlight how AI transforms creative workflows: accelerating ideation but compressing the traditional preparation stage, and requiring novices to navigate a challenging idea selection and validation phase. We also identified a new "collaging and refinement" stage, where participants creatively combined diverse AI-generated outputs into cohesive works. Furthermore, AI influenced group social dynamics and role division among human creators. Based on these insights, we propose the Human-AI Co-Creation Stage Model and the Human-AI Agency Model, offering new perspectives on collaborative co-creation with AI.