The mainstream approaches for creating AIs are the generative and deep learning AI approaches with large language models (LLMs) and the traditional manually constructed symbolic AI approach. Manually constructed AIs are generally 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 may have more potential. They develop competences like human children do. They start with innate competences, interact with the 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 visual and multimodal perception, and object recognition and manipulation. Computational models for abstraction discovery, curiosity, imitation learning, and early language acquisition have also been demonstrated. The promise is that developmental AIs will acquire self-developed and socially developed competences like people do. 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 toddler level competencies corresponding to human development at about two years of age, before their speech is fluent. They do not bridge the Reading Barrier, to skillfully and skeptically draw on online information resources. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental AIs to create intelligent, human-compatible AIs.

翻译：目前主流的AI构建方法包括基于大型语言模型的生成式深度学习AI，以及传统的手工构建符号AI。手工构建的AI即使在限定领域内也通常脆弱易错。生成式AI会犯奇怪的错误且无法察觉。这两种方法中的AI都难以被轻松指导，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AI可能更具潜力，它们像人类儿童一样逐步发展能力——从先天能力出发，通过与环境的互动进行学习，在与人类的互动中建立感知、认知和共同基础。发展型AI已展现出视觉与多模态感知、物体识别与操作等能力，并实现了抽象发现、好奇心驱动、模仿学习及早期语言习得的计算模型。其前景在于：发展型AI将像人类一样获得自我发展与社会发展的能力，弥补当前主流AI方法的缺陷，最终实现涉及批判性阅读、溯源评估和假设检验的复杂学习形式。然而，当前发展型AI项目尚未完全达到人类两岁幼儿（语言流利前）的能力水平，未能跨越"阅读屏障"——即无法熟练且批判性地利用在线信息资源。本文立场性论文阐述了通过拓展发展型AI实践来创造智能且人性化AI的逻辑、前景、差距与挑战。