Hyper Smart Developmentally Based Stacked Neural Networks and Evidence that Allows for True Androids that Pass the Turing Test.
This paper describes how a mathematically-based model of cognition and species evolution, the Model of Hierarchical Complexity (MHC) can be applied to create more effective and smarter artificial intelligence that is based upon how humans and animals solve problems. To more precisely emulate how a h...
| Publicado en: | Integral Review: A Transdisciplinary & Transcultural Journal for New Thought, Research, & Praxis Vol. 18; no. 1; pp. 171 - 198 |
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| Autores principales: | , |
| Formato: | Artículo |
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ARINA, Inc.
Sep2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=173004414&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 173004414 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 15533069 781Z jtl: Integral Review: A Transdisciplinary & Transcultural Journal for New Thought, Research, & Praxis issn: 15533069 maglogo: N pubinfo: dt: Sep2023 vid: 18 iid: 1 pid: 47364 pub: ARINA, Inc. artinfo: ui: 173004414 ppf: 171 ppct: 27 formats: fmt: @attributes: type: P size: 3.7MB tig: atl: Hyper Smart Developmentally Based Stacked Neural Networks and Evidence that Allows for True Androids that Pass the Turing Test. aug: au: Leite, Sofia R. S. Commons, Michael Lamport affil: PhD in Biomedical Engineering w/Fulbright grant at Harvard Medical School Undergraduate work at the University of California at Berkeley su: Turing test Artificial intelligence Learning in animals Learning sug: subj: Turing test Artificial intelligence Learning in animals Learning keyword: Androids Artificial Intelligence Human Learning Model of Hierarchical Complexity Stacked Architecture ab: This paper describes how a mathematically-based model of cognition and species evolution, the Model of Hierarchical Complexity (MHC) can be applied to create more effective and smarter artificial intelligence that is based upon how humans and animals solve problems. To more precisely emulate how a human acts upon the environment, a computer must learn from the environment in a way that is closer to the way that humans do. Moreover, the way humans learn from the environment is an evolutionary extension of how nonhuman animals learn. Hence, to more precisely emulate a human, nonhuman animal learning should also be taken into account. To do so, the MHC proposes an analytic, a priori measurement of the difficulty of task-actions called the Order of Hierarchical Complexity (Commons & Pekker, 2008). Task-actions mean actions directed toward problem-solving. According to the MHC, task-actions grow in complexity throughout development and evolution. The definitions for what makes an action more hierarchically complex will be presented. An application of the MHC to a general artificial intelligence architecture will be presented, and an application of it to a physics problem, called the balance beam problem, will be described. Following that, the possibility of creating truly intelligent androids based on the MHC architectural concept is discussed. An android is a computer based "organism" designed to act like an animal or human. It receives signals or input through sensors (simulating afferent nerves) and acts upon the environment through output agents (simulating muscles activated through motor nerves). The notions from the MHC that will be applied to the design of androids will also be discussed. Based on these developmental and evolutionary principles, such androids will be at least as smart as humans. They will not only pass the Turing test, as will be explained, but they will also be able to complete other tests specifically designed and appropriate for humans. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Integral Review: A Transdisciplinary & Transcultural Journal for New Thought, Research, & Praxis is the property of ARINA, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Integral Review: A Transdisciplinary & Transcultural Journal for New Thought, Research, & Praxis holder: ARINA, Inc. dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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