AI alignment considers the overall problem of ensuring an AI produces desired
outcomes, without undesirable side effects. While often considered from the
perspectives of safety and human values, AI alignment can also be considered in
the context of designing and evaluating interfaces for interactive AI systems.
This paper maps concepts from AI alignment onto a basic, three step interaction
cycle, yielding a corresponding set of alignment objectives: 1) specification
alignment: ensuring the user can efficiently and reliably communicate
objectives to the AI, 2) process alignment: providing the ability to verify and
optionally control the AI's execution process, and 3) evaluation support:
ensuring the user can verify and understand the AI's output. We also introduce
the concepts of a surrogate process, defined as a simplified, separately
derived, but controllable representation of the AI's actual process; and the
notion of a Process Gulf, which highlights how differences between human and AI
processes can lead to challenges in AI control. To illustrate the value of this
framework, we describe commercial and research systems along each of the three
alignment dimensions, and show how interfaces that provide interactive
alignment mechanisms can lead to qualitatively different and improved user
experiences.

AI 对齐是确保 AI 产生期望结果而无副作用的总体问题，可以从安全性、人类价值以及设计和评价交互式 AI 系统的界面的角度进行考虑。本文将 AI 对齐的概念映射到一个基本的三步交互循环中，得到相应的对齐目标：1）规范对齐：确保用户能够高效、可靠地与 AI 沟通目标；2）过程对齐：提供验证和可选控制 AI 执行过程的能力；3）评估支持：确保用户能够验证和理解 AI 的输出。我们还介绍了替代过程的概念，即 AI 实际过程的简化、分离且可控的表示；以及过程差异（Process Gulf）的概念，强调人类和 AI 过程之间的差异可能导致 AI 控制方面的挑战。为了说明该框架的价值，我们描述了商业和研究系统在每个对齐维度上的情况，并展示了提供交互对齐机制的界面如何带来不同且改善的用户体验。

在交互式 AI 设计中的 AI 对齐：规范对齐、过程对齐和评估支持

AI Alignment in the Design of Interactive AI: Specification Alignment,  Process Alignment, and Evaluation Support

Despite the growing demand for interactive AI systems, there have been few
comprehensive studies on human-AI interaction in visual understanding e.g.
segmentation. Inspired by the development of prompt-based universal interfaces
for LLMs, this paper presents SEEM, a promptable, interactive model for
Segmenting Everything Everywhere all at once in an image. SEEM has four
desiderata: i) Versatility: by introducing a versatile prompting engine for
different types of prompts, including points, boxes, scribbles, masks, texts,
and referred regions of another image; ii) Compositionality: by learning a
joint visual-semantic space for visual and textual prompts to compose queries
on the fly for inference as shown in Fig 1; iii)Interactivity: by incorporating
learnable memory prompts to retain dialog history information via mask-guided
cross-attention; and iv) Semantic-awareness: by using a text encoder to encode
text queries and mask labels for open-vocabulary segmentation.

SEEM 是一个可提示、互动的模型，可以同时将图像中的所有内容分割成多个部分，它具有多样性、组合性、互动性和语义感知等特点。