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棋盘游戏等封闭人造世界中超越了人类能力，但在现实世界中它们会犯下离奇错误且无法察觉。它们难以被轻松指导，缺乏常识，也缺少好奇心。主流的人工智能创建方法包括传统的手工构建符号AI方法，以及涵盖大型语言模型（LLM）的生成式与深度学习AI方法。尽管非主流，但发展型自举方法可能更具潜力。在发展型自举中，人工智能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，逐步用自我发展的能力扩展先天能力。它们与人类互动学习，建立感知、认知和共同基础，通过能力自举获取所需能力。然而，发展型机器人学尚未产出具备稳健成人级能力的人工智能。相关项目通常在突破"幼儿壁垒"前停滞——这对应人类婴儿约两岁时、语言变流畅前的发育阶段。它们也未能跨越"阅读壁垒"，即无法熟练且批判性地利用支撑LLM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。本立场论文系统阐述了扩展发展型自举实践以创建稳健、可信且人类兼容型人工智能的逻辑、前景、差距与挑战。