Recent advancements in AI foundation models have made it possible for them to
be utilized off-the-shelf for creative tasks, including ideating design
concepts or generating visual prototypes. However, integrating these models
into the creative process can be challenging as they often exist as standalone
applications tailored to specific tasks. To address this challenge, we
introduce Jigsaw, a prototype system that employs puzzle pieces as metaphors to
represent foundation models. Jigsaw allows designers to combine different
foundation model capabilities across various modalities by assembling
compatible puzzle pieces. To inform the design of Jigsaw, we interviewed ten
designers and distilled design goals. In a user study, we showed that Jigsaw
enhanced designers' understanding of available foundation model capabilities,
provided guidance on combining capabilities across different modalities and
tasks, and served as a canvas to support design exploration, prototyping, and
documentation.

最近人工智能基础模型的进步使得它们可以被即插即用地用于创意任务，包括创意设计概念和生成视觉原型。为了解决将这些模型整合到创意过程中的挑战，我们引入了 Jigsaw，这是一个使用拼图块作为隐喻来表示基础模型的原型系统。通过组装兼容的拼图块，Jigsaw 允许设计师将不同的基础模型能力跨越不同的模态组合起来。通过用户研究，我们展示了 Jigsaw 增强了设计师对可用基础模型能力的理解，提供了在不同模态和任务之间结合能力的指导，以及作为支持设计探索、原型设计和文档编写的画布。

Jigsaw：通过组装人工智能基础模型在原型设计多模态应用方面为设计师提供支持

Jigsaw: Supporting Designers in Prototyping Multimodal Applications by  Assembling AI Foundation Models

The rapid development of Artificial Intelligence (AI) requires developers and
designers of AI systems to focus on the collaboration between humans and
machines. AI explanations of system behavior and reasoning are vital for
effective collaboration by fostering appropriate trust, ensuring understanding,
and addressing issues of fairness and bias. However, various contextual and
subjective factors can influence an AI system explanation's effectiveness. This
work draws inspiration from findings in cognitive psychology to understand how
effective explanations can be designed. We identify four components to which
explanation designers can pay special attention: perception, semantics, intent,
and user & context. We illustrate the use of these four explanation components
with an example of estimating food calories by combining text with visuals,
probabilities with exemplars, and intent communication with both user and
context in mind. We propose that the significant challenge for effective AI
explanations is an additional step between explanation generation using
algorithms not producing interpretable explanations and explanation
communication. We believe this extra step will benefit from carefully
considering the four explanation components outlined in our work, which can
positively affect the explanation's effectiveness.

人工智能的迅猛发展需要开发者和设计师注重人和机器的合作。通过理解认知心理学的发现，确定了四个构成元素，即感知、语义、意图和用户与上下文，以设计有效的解释，如通过将文本和视觉、可能性和实例、意向交流融合，实现了计算食品卡路里的例子，并建议在解释生成和交流之间增加一个额外的步骤，以确定解释的效力。