Recent generative AI platforms are able to create texts or impressive images
from simple text prompts. This makes them powerful tools for summarizing
knowledge about architectural history or deriving new creative work in early
design tasks like ideation, sketching and modelling. But, how good is the
understanding of the generative AI models of the history of architecture? Has
it learned to properly distinguish styles, or is it hallucinating information?
In this chapter, we investigate this question for generative AI platforms for
text and image generation for different architectural styles, to understand the
capabilities and boundaries of knowledge of those tools. We also analyze how
they are already being used by analyzing a data set of 101 million Midjourney
queries to see if and how practitioners are already querying for specific
architectural concepts.

生成型 AI 平台在理解建筑历史和生成新创意作品方面具有强大的潜力，但我们需要了解它们对建筑风格的理解能力以及其已被实践者用于查询特定建筑概念的情况。

生成人工智能与建筑历史

Generative AI and the History of Architecture

Recently, ChatGPT, along with DALL-E-2 and Codex,has been gaining significant
attention from society. As a result, many individuals have become interested in
related resources and are seeking to uncover the background and secrets behind
its impressive performance. In fact, ChatGPT and other Generative AI (GAI)
techniques belong to the category of Artificial Intelligence Generated Content
(AIGC), which involves the creation of digital content, such as images, music,
and natural language, through AI models. The goal of AIGC is to make the
content creation process more efficient and accessible, allowing for the
production of high-quality content at a faster pace. AIGC is achieved by
extracting and understanding intent information from instructions provided by
human, and generating the content according to its knowledge and the intent
information. In recent years, large-scale models have become increasingly
important in AIGC as they provide better intent extraction and thus, improved
generation results. With the growth of data and the size of the models, the
distribution that the model can learn becomes more comprehensive and closer to
reality, leading to more realistic and high-quality content generation. This
survey provides a comprehensive review on the history of generative models, and
basic components, recent advances in AIGC from unimodal interaction and
multimodal interaction. From the perspective of unimodality, we introduce the
generation tasks and relative models of text and image. From the perspective of
multimodality, we introduce the cross-application between the modalities
mentioned above. Finally, we discuss the existing open problems and future
challenges in AIGC.

该研究综述了生成模型的历史和基本组成部分，并从单模态和多模态交互的角度介绍了文本和图像生成任务及相关模型，讨论了人工智能生成内容领域中的开放性问题和未来挑战。