On Machine Thinking
Xiang Wu, Zejia Zheng, Juyang Weng · 2021
Artificial Intelligence (AI) has made much progress, but the existing paradigm for AI is still basically pattern recognition based on a human-handcrafted representation. An AI paradigm shift seems to be necessary to address the machine thinking question raised by Alan Turing over 90 years ago. As a necessary subject of our new conscious learning paradigm introduced 2020, this work deals with general-purpose machine thinking, with planning as a special case, based on new concepts of emergent Super-Turing Machines realized by our proposed neural network models-Developmental Networks (DNs) that have been mathematically proven for its optimally in the sense of Maximum Likelihood (ML). Experimental demonstrations are presented for simulated new mazes in disjoint tests.