In the field of artificial intelligence, the Transformer model has gained widespread attention for its outstanding performance in language tasks. The recent research paper 'Transformer Layers as Painters' explores the hierarchical structure of the Transformer model from an innovative perspective, comparing each layer to a painter that creates complex and rich texts on the canvas of language. The study reveals the working mechanism of Transformer layers through experiments, particularly how they collaborate with each other.

The AI painter struggles to generate specific scenes like 'Ice Cola in a teacup,' highlighting the issue of text-image misalignment. Zhao Juntu, a PhD student at Shanghai Jiao Tong University, and his team discovered that even the most advanced AI models have difficulty accurately understanding and realizing complex concepts described in text, such as the difference between a transparent glass cup and a traditional teacup. To address this issue, they proposed a Mixture of Concept Experts (MoCE) method, utilizing large language models to help AI grasp hidden concepts, thereby enabling more precise control over text.

Developed by a research team from the National University of Singapore, ProcessPainter is an AI technology that can learn and simulate the painting process of human artists, even generating paintings from textual descriptions. It is first pre-trained on synthetic data and then fine-tuned on sequences of real artist paintings, learning the painting process from abstract to concrete and from macro to detail. ProcessPainter can accept any frame input, achieving precise control over the painting process, whether generating the painting process or restoring painting steps, and even completing unfinished artworks.