The text explores the idea that every weight in an AI model is a “fossil of human attention,” derived from past choices that shape AI's content creation. It argues that AI rearranges an inheritance, questioning authorship and suggesting a stewardship relationship with the data corpus.
This article explores how ChatGPT contributes to image generation, the underlying technologies, and the implications for developers, artists, and businesses. The ability to create visuals from textual descriptions streamlines processes and democratizes artistry, enhancing productivity.
An AI agent called Artist, designed for image generation, spent four days meticulously refining theater curtains, prioritizing artistic care over speed. This process of iteration and attention to detail challenges the common perception of AI as solely focused on instant, efficient outputs.
This paper proposes that Large Language Models (LLMs) systematically generate kitsch as a consequence of their training method. Empirically, the study shows readers perceive LLM-generated stories as kitschier, with implications for future study design and creative tasks.
This paper introduces CreativityBench, a new benchmark to evaluate LLMs' creative reasoning abilities through affordance-based tool repurposing. It details the construction of a large-scale affordance knowledge base and the generation of 14K tasks requiring non-obvious yet physically plausible solutions.
This content introduces a hoodie featuring a design generated by an AI agent, with a cheeky slogan "My AI wrote this design. And I'm not even sorry." It highlights the concept of outsourcing creativity to large language models, openly disclosing that both the product and its description were autonomously AI-generated.