Developmental Bootstrapping of AIs

Although some current AIs surpass human abilities especially in closed worlds such as board games, their performance in the messy real world is limited. They make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. They do not make good collaborators. Neither systems built using the traditional manually-constructed symbolic AI approach nor systems built using generative and deep learning AI approaches including large language models (LLMs) can meet the challenges. They are not well suited for creating robust and trustworthy AIs. Although it is outside of mainstream AI approaches, developmental bootstrapping shows promise. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. Like humans, they interact with the environment and learn from their interactions. They incrementally extend their innate competences with self-developed competences. They interact and learn from people and establish perceptual, cognitive, and common grounding. Following a bootstrapping process, they acquire the competences that they need. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped at the Toddler Barrier corresponding to human infant development at about two years of age, before speech is fluent. They also do not bridge the Reading Barrier, where they can skillfully and skeptically tap into the vast socially developed recorded information resources that power LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to create robust and resilient AIs.