Although some current AIs surpass human abilities in closed artificial worlds
such as board games, their abilities in the real world are 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.
Mainstream approaches for creating AIs are the traditional manually-constructed
symbolic AI approach and generative and deep learning AI approaches including
large language models (LLMs). These systems are not well suited for creating
robust and trustworthy AIs. Although it is outside of the mainstream, the
developmental bootstrapping approach has 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 bootstrapping. 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 their speech is fluent. They also do not bridge
the Reading Barrier, to skillfully and skeptically draw on the socially
developed information resources that power current 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 acquire further competences and create
robust, resilient, and human-compatible AIs.Comment: 102 pages, 29 figure