Article
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Preserved in Portico This version is not peer-reviewed
Top-down Design of Human-like Teachable Mind
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Version 1
: Received: 23 July 2023 / Approved: 25 July 2023 / Online: 26 July 2023 (10:36:18 CEST)
A peer-reviewed article of this Preprint also exists.
Xie, M. Top-Down Design of Human-Like Teachable Mind. International Journal of Humanoid Robotics 2023, doi:10.1142/s0219843623500263. Xie, M. Top-Down Design of Human-Like Teachable Mind. International Journal of Humanoid Robotics 2023, doi:10.1142/s0219843623500263.
Abstract
Teachability has been extensively studied under the context of making industrial robots to be programmable and reprogrammable. However, it is only recently that the artificial intelligence (AI) research community is accelerating the research works with the objective of making humanoid robots and many other robots to be teachable under the context of using natural languages. We human beings spend many years to learn knowledge and skills despite our extraordinary mental capabilities of being teachable with the use of natural languages. Therefore, if we would like to develop human-like robots such as humanoid robots, it is inevitable for us to face the issue of making future humanoid robots to be teachable with the use of natural languages as well. In this paper, we present the key details of a top-down design for achieving a teachable mind which consists of two major processes: the first one is the process which enables humanoid robots to gain innate mental capabilities of transforming incoming signals into meaningful crisp data, and the second one is the process which enables humanoid robots to gain innate mental capabilities of undertaking incremental and deep learning with the main focus of associating conceptual labels in a natural language to meaningful crisp data. These two processes consist of the two necessary and sufficient conditions for future humanoid robots to be teachable with the use of natural languages. In addition, this paper outlines a very likely new finding underlying human brain’s neural systems as well as the obvious mathematics underlying artificial deep neural networks. These outlines provide us the strong reason to separate the study of mind from the study of brain. Hopefully, the content discussed in this paper will help the AI research community to venture into the right direction which is to make future humanoid robots, non-humanoid robots, and many other systems to achieve human-like self-intelligence at cognitive level with the use of natural languages.
Keywords
teaching; learning; brain; mind; neural network; cognition; recognition
Subject
Computer Science and Mathematics, Robotics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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