Our ability to build autonomous agents that leverage Generative AI continues
to increase by the day. As builders and users of such agents it is unclear what
parameters we need to align on before the agents start performing tasks on our
behalf. To discover these parameters, we ran a qualitative empirical research
study about designing agents that can negotiate during a fictional yet
relatable task of selling a camera online. We found that for an agent to
perform the task successfully, humans/users and agents need to align over 6
dimensions: 1) Knowledge Schema Alignment 2) Autonomy and Agency Alignment 3)
Operational Alignment and Training 4) Reputational Heuristics Alignment 5)
Ethics Alignment and 6) Human Engagement Alignment. These empirical findings
expand previous work related to process and specification alignment and the
need for values and safety in Human-AI interactions. Subsequently we discuss
three design directions for designers who are imagining a world filled with
Human-Agent collaborations.

我们进行了一项关于设计能够在虚构但具有实际意义的在线相机销售任务中进行谈判的代理的质性经验研究，发现为了代理成功完成任务，人类 / 用户和代理需要在知识架构、自主性和代理性、操作性、培训、声誉启发式和伦理、人类参与等六个维度上达成一致。这些经验研究结果扩展了以往有关过程和规范对齐以及人工智能与人类交互中价值观和安全性需求的相关工作，并讨论了设计师在设想充满人类 - 代理协作的世界中的三个设计方向。

设计人机一致性：了解人类对其代理人的需求

Designing for Human-Agent Alignment: Understanding what humans want from  their agents

Digitising the 3D world into a clean, CAD model-based representation has
important applications for augmented reality and robotics. Current
state-of-the-art methods are computationally intensive as they individually
encode each detected object and optimise CAD alignments in a second stage. In
this work, we propose FastCAD, a real-time method that simultaneously retrieves
and aligns CAD models for all objects in a given scene. In contrast to previous
works, we directly predict alignment parameters and shape embeddings. We
achieve high-quality shape retrievals by learning CAD embeddings in a
contrastive learning framework and distilling those into FastCAD. Our
single-stage method accelerates the inference time by a factor of 50 compared
to other methods operating on RGB-D scans while outperforming them on the
challenging Scan2CAD alignment benchmark. Further, our approach collaborates
seamlessly with online 3D reconstruction techniques. This enables the real-time
generation of precise CAD model-based reconstructions from videos at 10 FPS.
Doing so, we significantly improve the Scan2CAD alignment accuracy in the video
setting from 43.0% to 48.2% and the reconstruction accuracy from 22.9% to
29.6%.

通过使用 FastCAD 方法，可以实时地检索和对齐给定场景中的所有对象的 CAD 模型，同时预测对齐参数和形状嵌入，加快了推理时间并提高了 3D 重建和对齐的准确性。