Autonomous Programming for General Purposes: Theory and Experiments
Juyang Weng, Zejia Zheng, Xiang Wu, Juan Castro-Garcia · 2020
Since the birth of AI, symbols have been well accepted as an abstract representation for intelligent systems, even when neural networks are employed. This theoretical work shows the following new methods: (1) Symbols are probably not used by biological brains because their behaviors are not limited by symbols. (2) Autonomous Programming For General Purposes (APFGP) is necessary for scaling-up AI to animal level intelligence. "Autonomous " means inside the skull (or network). (3) A Developmental Network (DN) performs APFGP by learning a super Turing machine, called Grounded, Emergent, Natural, Incremental, Skull-closed, Attentive, Motivated, and Abstractive (GENISAMA) Turing machine. (4) A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). (5) The GENISAMA DN does APFGP without using symbols. Experiments are reported for vision guided navigation, auditory recognition, and natural language learning. This is the first conference paper on APFGP.