引导式发展型人工智能：从简单能力到智能的人类兼容型 AI
Bootstrapping Developmental AIs: From Simple Competences to Intelligent Human-Compatible AIs
AI总结：
本文提出沿生物启发轨迹引导发展型具身 AI，从先天能力出发通过世界交互构建经验基础模型，以弥补现有符号与生成式 AI 在常识、社会一致性等方面的不足。
摘要：
发展型 AI 创造能够发展出类人能力的具身 AI。这些 AI 从先天能力出发，并通过与包括人在内的世界交互来学习更多能力。发展型 AI 已经得到展示，但迄今为止其能力尚未超过学步前儿童。相比之下，主流方法已经带来了令人瞩目的成就和具有商业价值的 AI 系统。这些方法包括深度学习和生成式 AI（例如大语言模型）以及人工构建的符号建模。然而，人工构建的 AI 即使在受限领域中也往往很脆弱。生成式 AI 平均而言很有帮助，但它们可能犯下奇怪的错误且意识不到这些错误。由于不从其在世界中的经验中学习，它们可能缺乏常识和社会一致性。这篇立场论文阐述了一种遵循生物启发轨迹的、用于发展型 AI 的引导式方法的前景、差距和挑战。该方法为人类兼容型 AI 创建经验基础模型。一个良性的多学科研究循环已经带来了具备多模态感知、物体识别和操作能力的发展型 AI。用于层级规划、抽象发现、好奇心和语言习得的计算模型已经存在，但需要适配到具身学习方法。剩余差距包括非语言交流、言语、阅读和写作。这些能力使人能够获得社会发展出来的能力。作为愿景，发展型 AI 将学习、分享其所学，并协作以达到高标准。它们将学习交流、建立共同基础、批判性阅读、考虑信息来源、检验假设并开展协作。该方法将使 AI 的训练更加民主化。
英文摘要：
Developmental AI creates embodied AIs that develop human-like abilities. The AIs start with innate competences and learn more by interacting with the world including people. Developmental AIs have been demonstrated, but their abilities so far do not surpass those of pre-toddler children. In contrast, mainstream approaches have led to impressive feats and commercially valuable AI systems. The approaches include deep learning and generative AI (e.g., large language models) and manually constructed symbolic modeling. However, manually constructed AIs tend to be brittle even in circumscribed domains. Generative AIs are helpful on average, but they can make strange mistakes and not notice them. Not learning from their experience in the world, they can lack common sense and social alignment. This position paper lays out prospects, gaps, and challenges for a bootstrapping approach to developmental AI that follows a bio-inspired trajectory. The approach creates experiential foundation models for human-compatible AIs. A virtuous multidisciplinary research cycle has led to developmental AIs with capabilities for multimodal perception, object recognition, and manipulation. Computational models for hierarchical planning, abstraction discovery, curiosity, and language acquisition exist but need to be adapted to an embodied learning approach. The remaining gaps include nonverbal communication, speech, reading, and writing. These competences enable people to acquire socially developed competences. Aspirationally, developmental AIs would learn, share what they learn, and collaborate to achieve high standards. They would learn to communicate, establish common ground, read critically, consider the provenance of information, test hypotheses, and collaborate. The approach would make the training of AIs more democratic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
