The mainstream AIs approaches are the generative and deep learning approaches with large language models (LLMs) and the manually constructed symbolic approach. Both approaches have led to valuable AI systems and impressive feats. However, manually constructed AIs are brittle even in circumscribed domains. Generative AIs make strange mistakes and do not notice them. In both approaches the AIs cannot be instructed easily, fail to use common sense, and lack curiosity. They have abstract knowledge but lack social alignment. Developmental AIs have more potential. They start with innate competences, interact with their environment, and learn from their interactions. They interact and learn from people and establish perceptual, cognitive, and common grounding. Developmental AIs have demonstrated capabilities including multimodal perception, object recognition, and manipulation. Powerful computational models for hierarchical planning, abstraction discovery, curiosity, and language acquisition exist but need to be adapted to a developmental learning based approach. The promise is that developmental AIs will acquire self-developed and socially developed competences. They would address the shortcomings of current mainstream AI approaches, and ultimately lead to sophisticated forms of learning involving critical reading, provenance evaluation, and hypothesis testing. However, developmental AI projects have not yet fully reached the Speaking Gap corresponding to toddler development at about two years of age, before their speech is fluent. The AIs do not bridge the Reading Gap, to skillfully and skeptically learn from written and online information resources. This position paper lays out the prospects, gaps, and challenges for extending the practice of developmental AIs to create resilient, intelligent, and human-compatible AIs that learn what they need to know.

翻译：主流人工智能方法包括基于大语言模型（LLM）的生成式与深度学习方法，以及人工构建的符号方法。这两种方法均产生了有价值的人工智能系统与令人瞩目的成就。然而，人工构建的人工智能即使在限定领域也显脆弱。生成式人工智能会犯奇怪错误且不自知。两种方法下的人工智能难以被轻松指导，缺乏常识运用能力，也缺少好奇心。它们拥有抽象知识，但缺乏社会对齐性。发展型人工智能更具潜力。它们从先天能力出发，与环境互动并从中学习。它们与人类互动学习，建立感知、认知与共同基础。发展型人工智能已展现出多模态感知、物体识别与操作等能力。虽已存在层次规划、抽象发现、好奇心与语言习得等强大的计算模型，但需适配基于发展性学习的方法。其前景在于，发展型人工智能将获得自我发展与社会发展的能力，从而弥补当前主流人工智能方法的缺陷，最终实现涉及批判性阅读、来源评估与假设检验等高级学习形式。然而，当前发展型人工智能项目尚未完全达到相当于两岁幼儿语言发展（即口语流利前的"语言鸿沟"阶段）。这些人工智能也未能跨越"阅读鸿沟"，即无法熟练且带批判性地从书面及在线信息资源中学习。本立场文件阐述了发展型人工智能扩展实践的前景、差距与挑战，旨在创建具有韧性、智能且与人类兼容的人工智能系统，使其能自主学习所需知识。