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

Although some AIs surpass human abilities in closed artificial worlds such as board games, in the real world they make strange mistakes and do not notice them. They cannot be instructed easily, fail to use common sense, and lack curiosity. Mainstream approaches for creating AIs include the traditional manually-constructed symbolic AI approach and the generative and deep learning AI approaches including large language models (LLMs). Although it is outside of the mainstream, the developmental bootstrapping approach may have more potential. In developmental bootstrapping, AIs develop competences like human children do. They start with innate competences. 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. They acquire the competences they need through competence bootstrapping. However, developmental robotics has not yet produced AIs with robust adult-level competences. Projects have typically stopped before reaching the Toddler Barrier. This corresponds to human infant development at about two years of age, before infant speech becomes fluent. They also do not bridge the Reading Barrier, where they could skillfully and skeptically draw on the socially developed online information resources that power LLMs. The next competences in human cognitive development involve intrinsic motivation, imitation learning, imagination, coordination, and communication. This position paper lays out the logic, prospects, gaps, and challenges for extending the practice of developmental bootstrapping to create robust, trustworthy, and human-compatible AIs.