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The energy challenges of artificial superintelligence

We argue here that contemporary semiconductor computing technology poses a significant if not insurmountable barrier to the emergence of any artificial general intelligence system, let alone one anticipated by many to be “superintelligent”. This limit on artificial superintelligence (ASI) emerges fr...

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Detalles Bibliográficos
Autores principales: Stiefel, Klaus M., Coggan, Jay S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629395/
https://www.ncbi.nlm.nih.gov/pubmed/37941679
http://dx.doi.org/10.3389/frai.2023.1240653
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author Stiefel, Klaus M.
Coggan, Jay S.
author_facet Stiefel, Klaus M.
Coggan, Jay S.
author_sort Stiefel, Klaus M.
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description We argue here that contemporary semiconductor computing technology poses a significant if not insurmountable barrier to the emergence of any artificial general intelligence system, let alone one anticipated by many to be “superintelligent”. This limit on artificial superintelligence (ASI) emerges from the energy requirements of a system that would be more intelligent but orders of magnitude less efficient in energy use than human brains. An ASI would have to supersede not only a single brain but a large population given the effects of collective behavior on the advancement of societies, further multiplying the energy requirement. A hypothetical ASI would likely consume orders of magnitude more energy than what is available in highly-industrialized nations. We estimate the energy use of ASI with an equation we term the “Erasi equation”, for the Energy Requirement for Artificial SuperIntelligence. Additional efficiency consequences will emerge from the current unfocussed and scattered developmental trajectory of AI research. Taken together, these arguments suggest that the emergence of an ASI is highly unlikely in the foreseeable future based on current computer architectures, primarily due to energy constraints, with biomimicry or other new technologies being possible solutions.
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spelling pubmed-106293952023-11-08 The energy challenges of artificial superintelligence Stiefel, Klaus M. Coggan, Jay S. Front Artif Intell Artificial Intelligence We argue here that contemporary semiconductor computing technology poses a significant if not insurmountable barrier to the emergence of any artificial general intelligence system, let alone one anticipated by many to be “superintelligent”. This limit on artificial superintelligence (ASI) emerges from the energy requirements of a system that would be more intelligent but orders of magnitude less efficient in energy use than human brains. An ASI would have to supersede not only a single brain but a large population given the effects of collective behavior on the advancement of societies, further multiplying the energy requirement. A hypothetical ASI would likely consume orders of magnitude more energy than what is available in highly-industrialized nations. We estimate the energy use of ASI with an equation we term the “Erasi equation”, for the Energy Requirement for Artificial SuperIntelligence. Additional efficiency consequences will emerge from the current unfocussed and scattered developmental trajectory of AI research. Taken together, these arguments suggest that the emergence of an ASI is highly unlikely in the foreseeable future based on current computer architectures, primarily due to energy constraints, with biomimicry or other new technologies being possible solutions. Frontiers Media S.A. 2023-10-24 /pmc/articles/PMC10629395/ /pubmed/37941679 http://dx.doi.org/10.3389/frai.2023.1240653 Text en Copyright © 2023 Stiefel and Coggan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Stiefel, Klaus M.
Coggan, Jay S.
The energy challenges of artificial superintelligence
title The energy challenges of artificial superintelligence
title_full The energy challenges of artificial superintelligence
title_fullStr The energy challenges of artificial superintelligence
title_full_unstemmed The energy challenges of artificial superintelligence
title_short The energy challenges of artificial superintelligence
title_sort energy challenges of artificial superintelligence
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629395/
https://www.ncbi.nlm.nih.gov/pubmed/37941679
http://dx.doi.org/10.3389/frai.2023.1240653
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