Although some current AIs surpass human abilities especially in closed worlds such as board games, their performance in the messy real world is 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. Neither systems built using the traditional manually-constructed symbolic AI approach nor systems built using generative and deep learning AI approaches including large language models (LLMs) can meet the challenges. They are not well suited for creating robust and trustworthy AIs. Although it is outside of mainstream AI approaches, developmental bootstrapping shows promise. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. Like humans, 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. Following a bootstrapping process, they acquire the competences that they need. 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 speech is fluent. They also do not bridge the Reading Barrier, where they can skillfully and skeptically tap into the vast socially developed recorded information resources that power LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to create robust and resilient AIs.

翻译：尽管当前某些人工智能在封闭环境（如棋类游戏）中已超越人类能力，但在混乱的现实世界中其表现仍有限。它们会犯奇怪错误且不自知，难以通过指令训练，缺乏常识运用能力与好奇心，无法成为良好协作者。无论是采用传统手工构建的符号主义AI方法，还是包括大型语言模型（LLMs）在内的生成式与深度学习AI方法构建的系统，均无法应对这些挑战，难以创建稳健可信的人工智能。虽非主流AI方法，发展自举展现了潜力。在发展自举中，AI像人类儿童一样逐步发展能力：从先天能力出发，像人类般与环境互动并从交互中学习，通过自生能力逐步扩展先天能力，与人类互动学习并建立感知、认知与共同基域。遵循自举过程，AI获得所需能力。然而，发展机器人学尚未产出具备稳健成人级能力的AI，相关项目通常在婴幼儿障碍（对应人类约两岁婴儿发展阶段，言语尚不流利）处停滞。它们同样未能跨越阅读障碍——即无法熟练且审慎地接入LLMs所依赖的庞大社会性记录信息资源。人类认知发展的后续能力涉及内在动机、模仿学习、想象、协调与通信。本文阐述了扩展发展自举实践以创建稳健坚韧AI的逻辑、前景、不足与挑战。