Although some AIs surpass human abilities in closed artificial worlds such as board games, in the real world they make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. Mainstream approaches for creating AIs include the traditional manually-constructed symbolic AI approach and the generative and deep learning AI approaches including large language models (LLMs). Although it is outside of the mainstream, the developmental bootstrapping approach may have 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 competence bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped before reaching the Toddler Barrier. This corresponds to human infant development at about two years of age, before infant speech becomes fluent. They also do not bridge the Reading Barrier, where they could skillfully and skeptically draw on the socially developed online information resources that power 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 create robust, trustworthy, and human-compatible AIs.

翻译：尽管某些人工智能在封闭的人工世界（如棋类游戏）中超越了人类能力，但在现实世界中它们会犯下奇怪的错误且无法察觉。它们难以被指令引导，缺乏常识运用能力，也缺少好奇心。当前主流的AI构建方法包括传统的手工构建符号AI方法，以及生成式和深度学习AI方法（涵盖大型语言模型）。尽管不属于主流，但发育性自主构建方法可能更具潜力。在这种方法中，AI像人类儿童一样发展能力：它们从先天能力出发，与环境互动并从中学习，逐步以自主发展的能力扩展其先天能力；它们与人类互动学习，建立感知、认知和共同基础，并通过能力自主构建获取所需技能。然而，发育机器人学尚未产生具备稳健成人级能力的AI，相关项目通常止步于“幼儿屏障”——这对应人类婴幼儿约两岁时、在语言流利前的发展阶段。它们也未能跨越“阅读屏障”，即无法熟练且批判性地利用支撑大型语言模型的社会化在线信息资源。人类认知发展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。本立场论文阐述了扩展发育性自主构建实践以创造稳健、可信且与人类兼容的AI的逻辑、前景、差距与挑战。