Although some current AIs surpass human abilities in closed artificial worlds such as board games, their abilities in the real world are limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Mainstream approaches for creating AIs are the traditional manually-constructed symbolic AI approach and generative and deep learning AI approaches including large language models (LLMs). These systems are not well suited for creating robust and trustworthy AIs. Although it is outside of the mainstream, the developmental bootstrapping approach has more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. They interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. They acquire the competences they need through bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before their speech is fluent. They also do not bridge the Reading Barrier, to skillfully and skeptically draw on the socially developed information resources that power current LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to acquire further competences and create robust, resilient, and human-compatible AIs.

翻译：尽管当前某些人工智能在封闭的人工世界（如棋盘游戏）中超越了人类能力，但它们在现实世界中的能力仍然有限。它们会犯下奇怪的错误且不自知，难以被简单指导，缺乏常识与好奇心，也无法成为良好的协作者。当前主流的人工智能构建方法包括传统手工构建的符号人工智能方法，以及涵盖大语言模型（LLMs）的生成式与深度学习人工智能方法。这些系统并不适合构建稳健可信的人工智能。尽管并非主流，发展自举方法却更具潜力。在发展自举中，人工智能像人类儿童一样发展能力：它们以先天能力为起点，通过与环境的互动进行学习，在先天能力基础上逐步扩展出自发发展的能力。它们与人类互动并向其学习，建立感知、认知与常识根基，通过自举获得所需的能力。然而，发展机器人学尚未产出具备成人级稳健能力的人工智能。相关项目通常止步于“幼儿屏障”——对应人类婴儿约两岁前的发育阶段（此时语言尚未流利），也未能跨越“阅读屏障”——即无法巧妙地、审慎地利用当前支撑大语言模型的社会发展信息资源。人类认知发展中的下一阶段能力涉及内在动机、模仿学习、想象力、协调与沟通。本立场文件阐述了将发展自举实践拓展至获取更高阶能力、构建稳健、富有韧性且与人类兼容的人工智能的逻辑、前景、差距与挑战。