通过协同观察实现个人智能

中文摘要 AI 辅助

个人AI系统需要建立用户目标、约束及持续承诺的模型，才能代表用户进行规划与行动，而该模型的质量受限于系统可观测到的信息范围。更广泛的观测本身并不能提升辅助效果，因为受限于资源的系统必须针对当前任务选择并压缩信息。本文提出，该观测瓶颈具有协同结构：系统构建用户不断变化的生活的部分模型，用户对系统的行动进行评估，用户的同意与控制权决定了系统后续可观测的内容。有用且可检查的行为能让用户有理由维持或扩展观测渠道，而失败则会导致用户纠正、缩小、撤销或放弃该渠道。本文将这种在有用性、信任与未来访问权限之间的反馈循环称为协同观察，并将其作为实现个人智能的框架。我们报告了来自Organizm的初步单主体研究结果，Organizm是一款使用时长超过六个月的原型系统，同时概述了用于测量观测质量如何影响个人AI的评估方向。

英文摘要

A personal AI system needs a model of the user's goals, constraints, and ongoing commitments to plan and act on their behalf, and the quality of that model is bounded by what the system can observe. Broader observation does not by itself improve assistance because a bounded system must select and compress information for the task at hand. We argue that this observation bottleneck has a cooperative structure: the system builds a partial model of the user's changing life, the user evaluates its actions, and the user's consent and control shape what it can observe next. Useful and inspectable behavior can give users a reason to maintain or expand the observation channel, while failures can lead them to correct, narrow, revoke, or abandon it. We use the term cooperative observation for this feedback loop among usefulness, trust, and future access, and propose it as a framework for personal intelligence. We report a preliminary single-subject account from Organizm, a prototype used over six months, and outline evaluation directions for measuring how observation quality shapes personal AI.

发表机构

• Alberta Machine Intelligence Institute, University of Alberta（阿尔伯塔机器智能研究所，阿尔伯塔大学）
• MacEwan University（麦克尤恩大学）
• Network for Applied Technology（应用技术网络）

机构由 AI 辅助整理，请以论文原文为准。