This article examines the fundamental differences between how human children acquire values and competencies versus how current artificial intelligence systems are trained. While modern AI training relies on large datasets and software guardrails, humans learn through a combination of innate biological programming, personal experience, and essential social interaction within a community. The author argues that to achieve truly reliable, trustworthy, and human-compatible AI, developers should move beyond mere data containment and instead integrate deep learning with the collaborative, staged experiences found in developmental robotics and social species.
This paper details the reconstruction and execution of the Logic Theorist (LT), considered the first artificial intelligence program, originally created in 1955-1956. The authors built a new IPL-V interpreter in Common Lisp and faithfully reanimated LT from code transcribed from a 1963 RAND technical report. The reanimated LT successfully proved 16 of 23 theorems from Principia Mathematica, consistent with the original system's behavior. This work demonstrates "executable archaeology" as a method for understanding early AI systems, highlighting the challenges and insights gained from reconstructing and running historical code.
This Perspective outlines ways in which generative artificial intelligence aligns with and supports the core ideas of generative linguistics, and how generative linguistics can provide criteria to evaluate and improve neural language models.
