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 learning 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 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 that 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 the human brain's neural systems as well as the obvious mathematics underlying artificial deep neural networks. These outlines provide us with a strong reason to separate the study of the mind from the study of the 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 the cognitive level with the use of natural languages.
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Univ British Columbia, Dept Psychol, Vancouver, BC V6T 1Z4, Canada
Univ British Columbia, Grad Program Neurosci, Vancouver, BC V6T 1Z4, CanadaUniv British Columbia, Dept Psychol, Vancouver, BC V6T 1Z4, Canada
Mickleborough, Marla J. S.
Truong, Grace
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Univ British Columbia, Dept Psychol, Vancouver, BC V6T 1Z4, CanadaUniv British Columbia, Dept Psychol, Vancouver, BC V6T 1Z4, Canada
Truong, Grace
Handy, Todd C.
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Univ British Columbia, Dept Psychol, Vancouver, BC V6T 1Z4, Canada
Univ British Columbia, Grad Program Neurosci, Vancouver, BC V6T 1Z4, CanadaUniv British Columbia, Dept Psychol, Vancouver, BC V6T 1Z4, Canada
机构:
Microsoft Res NYC, Reinforcement Learning Stn, 300 Lafayette, New York, NY 10012 USAMicrosoft Res NYC, Reinforcement Learning Stn, 300 Lafayette, New York, NY 10012 USA
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EGADE Business School, Tecnológico de Monterrey, Av. Carlos Lazo No. 100, Col. Santa Fe, Delegación Álvaro Obregón D.F.EGADE Business School, Tecnológico de Monterrey, Av. Carlos Lazo No. 100, Col. Santa Fe, Delegación Álvaro Obregón D.F.
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Univ Washington, Dept Psychol, Seattle, WA 98105 USAUniv Washington, Dept Psychol, Seattle, WA 98105 USA
Fan, Xiaoxu
Guo, Qiang
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Guangdong Sanjiu Brain Hosp, Epilepsy Ctr, Guangzhou 510510, Guangdong, Peoples R ChinaUniv Washington, Dept Psychol, Seattle, WA 98105 USA
Guo, Qiang
Zhang, Xinxin
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South China Normal Univ, Key Lab Brain Cognit & Educ Sci, Minist Educ, Guangzhou 510898, Guangdong, Peoples R ChinaUniv Washington, Dept Psychol, Seattle, WA 98105 USA
Zhang, Xinxin
Fei, Lingxia
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Guangdong Sanjiu Brain Hosp, Epilepsy Ctr, Guangzhou 510510, Guangdong, Peoples R ChinaUniv Washington, Dept Psychol, Seattle, WA 98105 USA
Fei, Lingxia
He, Sheng
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Chinese Acad Sci, Inst Biophys, State Key Lab Brain & Cognit Sci, Beijing 100101, Peoples R ChinaUniv Washington, Dept Psychol, Seattle, WA 98105 USA
He, Sheng
Weng, Xuchu
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South China Normal Univ, Key Lab Brain Cognit & Educ Sci, Minist Educ, Guangzhou 510898, Guangdong, Peoples R ChinaUniv Washington, Dept Psychol, Seattle, WA 98105 USA