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Developmental AI is a bootstrapping approach where embodied AIs start withinnate competences and learn by interacting with the world. They developabilities in small steps along a bio-inspired trajectory. However,developmental AIs have not yet reached the abilities of young children. Incontrast, mainstream approaches for creating AIs have led to valuable AIsystems and impressive feats. These approaches include deep learning andgenerative approaches (e.g., large language models) and manually constructedsymbolic approaches. Manually constructed AIs are brittle even in circumscribeddomains. Generative AIs can make strange mistakes and not notice them. Takentogether, mainstream approaches lack common sense, curiosity, and socialalignment. This position paper lays out prospects, gaps, and challenges foraugmenting the AI mainstream approaches with developmental AI. The ambition isto create data-rich experientially based foundation models forhuman-compatible, resilient, and trustworthy AIs. This research aims to producedevelopmental AIs that learn to communicate, establish common ground, readcritically, consider the provenance of information, test hypotheses, andcollaborate. A virtuous multidisciplinary research cycle has led todevelopmental AIs with capabilities for multimodal perception, objectrecognition, and manipulation. Computational models for hierarchical planning,abstraction discovery, curiosity, and language acquisition exist but need to beadapted to an embodied learning approach. They need to bridge competence gapsinvolving nonverbal communication, speech, reading, and writing.Aspirationally, developmental AIs would learn, share what they learn, andcollaborate to achieve high standards. The approach would make the creation ofAIs more democratic, enabling more people to train, test, build on, andreplicate AIs.

Bootstrapping Developmental AIs: From Simple Competences to Intelligent Human-Compatible AIs