Artificial intelligence (AI) technologies are widely deployed in smartphone
photography; and prompt-based image synthesis models have rapidly become
commonplace. In this paper, we describe a Research-through-Design (RtD) project
which explores this shift in the means and modes of image production via the
creation and use of the Entoptic Field Camera. Entoptic phenomena usually refer
to perceptions of floaters or bright blue dots stemming from the physiological
interplay of the eye and brain. We use the term entoptic as a metaphor to
investigate how the material interplay of data and models in AI technologies
shapes human experiences of reality. Through our case study using first-person
design and a field study, we offer implications for critical, reflective,
more-than-human and ludic design to engage AI technologies; the
conceptualisation of an RtD research space which contributes to AI literacy
discourses; and outline a research trajectory concerning materiality and design
affordances of AI technologies.

本研究通过 Entoptic Field 相机的创作和使用探索人工智能技术中数据和模型的物质相互作用如何影响人类对现实的体验，并根据第一人称设计和实地研究提出涉及人工智能素养，物质性以及设计合理性的研究轨迹和理论。

AI 技术的隐形视野相机作为以隐喻驱动的研究设计

The Entoptic Field Camera as Metaphor-Driven Research-through-Design with AI Technologies

Spatial commonsense, the knowledge about spatial position and relationship
between objects (like the relative size of a lion and a girl, and the position
of a boy relative to a bicycle when cycling), is an important part of
commonsense knowledge. Although pretrained language models (PLMs) succeed in
many NLP tasks, they are shown to be ineffective in spatial commonsense
reasoning. Starting from the observation that images are more likely to exhibit
spatial commonsense than texts, we explore whether models with visual signals
learn more spatial commonsense than text-based PLMs. We propose a spatial
commonsense benchmark that focuses on the relative scales of objects, and the
positional relationship between people and objects under different actions. We
probe PLMs and models with visual signals, including vision-language pretrained
models and image synthesis models, on this benchmark, and find that image
synthesis models are more capable of learning accurate and consistent spatial
knowledge than other models. The spatial knowledge from image synthesis models
also helps in natural language understanding tasks that require spatial
commonsense.

本研究利用图像合成的方法，构建了一个专注于对象相对尺度和位置关系的空间常识基准，并证明相较于文本预训练模型，具有视觉信号的模型更能准确和持续地学习空间常识，从中获得的知识也可用于具有空间常识的自然语言理解任务。