The discussion centers on the ethical and practical implications of AI authorship in academic publications, challenging the current prohibition by major journals such as JAMA and Nature. These journals argue against AI authorship due to AI's inability to explain, defend, or take accountability for its work. However, the argument is made that AI's pervasive use in research activities like drafting, critiquing, and proofreading already mirrors human contributions, and AI often produces work comparable to or better than human efforts. The paper suggests that current policies are inconsistently applied and discriminatory, advocating for reformed authorship standards that recognize all contributions fairly. This matters because it addresses the evolving role of AI in academia and the need for equitable recognition of contributions in research.
![[R] Collecting memes for LLM study—submit yours and see the analysis!](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-9639-300x171.png)
Researchers from THWS and CAIRO's NLP Team are developing MemeQA, a crowd-sourced dataset aimed at testing Vision-Language Models (VLMs) on their ability to comprehend memes, including aspects such as humor, emotional mapping, and cultural context. The project seeks contributions of original or favorite memes from the public to expand its initial collection of 31 memes. Each meme will be analyzed across more than 10 dimensions to evaluate VLM benchmarks, and contributors will be credited for their submissions. Understanding how AI interprets memes can enhance the development of models that better grasp human humor and cultural nuances.