Activewear brand Sweaty Betty has become involved in a new dispute over advertising slogans, which a period underwear company claims were copied. Kelly Newton said Sweaty Betty's use of two taglines that were very similar to her firm Nixi Body's seemed a little off, and while she could not get them trademarked she felt Sweaty Betty was taking from other female founders. Sweaty Betty said the No ifs. Ms Newton said she was speaking out after seeing personal trainer Georgina Cox reveal Sweaty Betty had offered her a settlement over a disputed slogan . Ms Newton, who co-founded Nixi Body in 2019, said the company has advertised its leak-proof period underwear with the lines Keeping you moving through menstruation, motherhood and menopause and No leaks.

"I don't feel particularly unhappy about my work being used to train AI," says Japanese novelist Rie Qudan. "Even if it is copied, I feel confident there's a part of me that will remain, which nobody can copy." The 34-year old author is talking to me via Zoom from her home near Tokyo, ahead of the publication of the English-language translation of her fourth novel, Sympathy Tower Tokyo. The book attracted controversy in Japan when it won a prestigious prize, despite being partly written by ChatGPT. At the heart of Sympathy Tower Tokyo is a Japanese architect, Sara Machina, who has been commissioned to build a new tower to house convicted criminals. It will be a representation of what one character – not without irony – calls "the extraordinary broadmindedness of the Japanese people", in that the tower will house offenders in compassionate comfort.

How can AI move beyond imitation toward genuine creativity? This paper proposes the E.A.R.T.H. framework, a five-stage generative pipeline that transforms model-generated errors into creative assets through Error generation, Amplification, Refine selection, Transform, and Harness feedback. Drawing on cognitive science and generative modeling, we posit that "creative potential hides in failure" and operationalize this via structured prompts, semantic scoring, and human-in-the-loop evaluation. Implemented using LLaMA-2-7B-Chat, SBERT, BERTScore, CLIP, BLIP-2, and Stable Diffusion, the pipeline employs a composite reward function based on novelty, surprise, and relevance. At the Refine stage, creativity scores increase by 52.5% (1.179 to 1.898, t = -5.56, p < 0.001), with final outputs reaching 2.010 - a 70.4% improvement. Refined slogans are 48.4% shorter, 40.7% more novel, with only a 4.0% drop in relevance. Cross-modal tests show strong slogan-to-image alignment (CLIPScore: 0.249; BERTScore F1: 0.816). In human evaluations, the generated outputs were consistently rated highly, demonstrating strong creative quality and expressive clarity. Feedback highlights stylistic precision and emotional resonance. These results demonstrate that error-centered, feedback-driven generation enhances creativity, offering a scalable path toward self-evolving, human-aligned creative AI.

One thing most game developers can agree on in the modern industry is that it's hard to drum up any awareness for your latest project without a mammoth marketing budget. Last year, almost 20,000 new titles were released on the PC gaming platform Steam alone, the majority disappearing into the content blackhole that is the internet. So when a smaller studio is offered the chance to get on the stage at the Summer Games Fest, an event streamed live to a global audience of around 50 million people, it's a big deal. Not something that you want to spectacularly misjudge. Enter Ian Proulx, cofounder of 1047 Games.

The recent advances in large language models (LLMs) have revolutionized industries such as finance, marketing, and customer service by enabling sophisticated natural language processing tasks. However, the broad adoption of LLMs brings significant challenges, particularly in the form of social biases that can be embedded within their outputs. Biases related to gender, age, and other sensitive attributes can lead to unfair treatment, raising ethical concerns and risking both company reputation and customer trust. This study examined bias in finance-related marketing slogans generated by LLMs (i.e., ChatGPT) by prompting tailored ads targeting five demographic categories: gender, marital status, age, income level, and education level. A total of 1,700 slogans were generated for 17 unique demographic groups, and key terms were categorized into four thematic groups: empowerment, financial, benefits and features, and personalization. Bias was systematically assessed using relative bias calculations and statistically tested with the Kolmogorov-Smirnov (KS) test against general slogans generated for any individual. Results revealed that marketing slogans are not neutral; rather, they emphasize different themes based on demographic factors. Women, younger individuals, low-income earners, and those with lower education levels receive more distinct messaging compared to older, higher-income, and highly educated individuals. This underscores the need to consider demographic-based biases in AI-generated marketing strategies and their broader societal implications. The findings of this study provide a roadmap for developing more equitable AI systems, highlighting the need for ongoing bias detection and mitigation efforts in LLMs.

Large language models (LLMs) often struggle to objectively identify latent characteristics in large datasets due to their reliance on pre-trained knowledge rather than actual data patterns. To address this data grounding issue, we propose Data Scientist AI (DSAI), a framework that enables unbiased and interpretable feature extraction through a multi-stage pipeline with quantifiable prominence metrics for evaluating extracted features. On synthetic datasets with known ground-truth features, DSAI demonstrates high recall in identifying expert-defined features while faithfully reflecting the underlying data. Applications on real-world datasets illustrate the framework's practical utility in uncovering meaningful patterns with minimal expert oversight, supporting use cases such as interpretable classification. The title of our paper is chosen from multiple candidates based on DSAI-generated criteria.

From fake images designed to cause fears of an immigrant "invasion" to other demonisation campaigns targeted at leaders such as Emmanuel Macron, far-right parties and activists across western Europe are at the forefront of the political weaponisation of generative artificial intelligence technology. This year's European parliamentary elections were the launchpad for a rollout of AI-generated campaigning by the European far right, experts say, which has continued to proliferate since. This month, the issue reached the independent oversight board of Mark Zuckerberg's Meta when the body opened an investigation into anti-immigration content on Facebook. The inquiry by the oversight board will look at a post from a German account featuring an AI-generated image emblazoned with anti-immigrant rhetoric. It is part of a wave of AI-made rightwing content on social media networks.

When Sookie Orth sat down to write her college essay last fall, something quickly came to mind. Orth, then a senior at Sequoyah School in Pasadena, began her draft with a declaration: "I learned how to fold a pizza box at the age of nine." She told the story of her years-long connection with Pizza of Venice in Altadena, where she often dined with her family as a little kid. One day, the manager invited her to assemble a box. Impressed with Orth's speed, the woman told her she could work at the pizzeria when she was older.

Slogans play a crucial role in building the brand's identity of the firm. A slogan is expected to reflect firm's vision and brand's value propositions in memorable and likeable ways. Automating the generation of slogans with such characteristics is challenging. Previous studies developted and tested slogan generation with syntactic control and summarization models which are not capable of generating distinctive slogans. We introduce a a novel apporach that leverages pre-trained transformer T5 model with noise perturbation on newly proposed 1:N matching pair dataset. This approach serves as a contributing fator in generting distinctive and coherent slogans. Turthermore, the proposed approach incorporates descriptions about the firm and brand into the generation of slogans. We evaluate generated slogans based on ROUGE1, ROUGEL and Cosine Similarity metrics and also assess them with human subjects in terms of slogan's distinctiveness, coherence, and fluency. The results demonstrate that our approach yields better performance than baseline models and other transformer-based models.

Persuasion techniques detection in news in a multi-lingual setup is non-trivial and comes with challenges, including little training data. Our system successfully leverages (back-)translation as data augmentation strategies with multi-lingual transformer models for the task of detecting persuasion techniques. The automatic and human evaluation of our augmented data allows us to explore whether (back-)translation aid or hinder performance. Our in-depth analyses indicate that both data augmentation strategies boost performance; however, balancing human-produced and machine-generated data seems to be crucial.