Developmental AI creates embodied AIs that develop human-like abilities. In a bootstrapping approach to developmental AI, 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.
The paper below lays out prospects, gaps, and challenges for developmental AI. The goal is to create data-rich experientially based 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 competence gaps involve nonverbal communication, speech, reading, and writing.
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.
Publications
Stefik, M., Price, R. (2023) Bootstrapping Developmental AIs (112 pages) arXiv http://arxiv.org/abs/2308.04586