AI for Technoscientific Discovery: A Human-Inspired Architecture

Tsao, J. Y.; Abbott, R. G.; Crowder, D. C.; Desai, S.; Dingreville, R. P. M.; Fowler, J. E.; Garland, A.; Iyer, P. P.; Murdock, J.; Steinmetz, S. T.; Yarritu, K. A.; Johnson, C. M.; Stracuzzi, D. J.

doi:10.1016/j.yjoc.2024.100077

Title: AI for Technoscientific Discovery: A Human-Inspired Architecture

Abstract

We present a high-level architecture for how artificial intelligences might advance and accumulate scientific and technological knowledge, inspired by emerging perspectives on how human intelligences advance and accumulate such knowledge. Agents advance knowledge by exercising a technoscientific method—an interacting combination of scientific and engineering methods. The technoscientific method maximizes a quantity we call “useful learning” via more-creative implausible utility (including the “aha!” moments of discovery), as well as via less-creative plausible utility. Society accumulates the knowledge advanced by agents so that other agents can incorporate and build on to make further advances. The proposed architecture is challenging but potentially complete: its execution might in principle enable artificial intelligences to advance and accumulate an equivalent of the full range of human scientific and technological knowledge.

Authors:

; Abbott, R. G.; Crowder, D. C.; Desai, S.;

;

; Garland, A.; Iyer, P. P.; Murdock, J.;

;

; Johnson, C. M.; Stracuzzi, D. J.

Publication Date:: Thu Aug 01 00:00:00 EDT 2024

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC)

OSTI Identifier:: 2311922

Alternate Identifier(s):: OSTI ID: 2311255

Report Number(s):: SAND-2024-01626J
Journal ID: ISSN 2713-3745; S2713374524000037; 100077; PII: S2713374524000037

Grant/Contract Number:: 230710; NA0003525

Resource Type:: Published Article

Journal Name:: Journal of Creativity (Online)

Additional Journal Information:: Journal Name: Journal of Creativity (Online) Journal Volume: 34 Journal Issue: 2; Journal ID: ISSN 2713-3745

Publisher:: Elsevier

Country of Publication:: United Kingdom

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; artificial intelligence; creativity; extended neurosymbolic knowledge; technoscientific method

Citation Formats


                    Tsao, J. Y., Abbott, R. G., Crowder, D. C., Desai, S., Dingreville, R. P. M., Fowler, J. E., Garland, A., Iyer, P. P., Murdock, J., Steinmetz, S. T., Yarritu, K. A., Johnson, C. M., and Stracuzzi, D. J. AI for Technoscientific Discovery: A Human-Inspired Architecture.  United Kingdom: N. p., 2024. 
Web.  doi:10.1016/j.yjoc.2024.100077.

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                    Tsao, J. Y., Abbott, R. G., Crowder, D. C., Desai, S., Dingreville, R. P. M., Fowler, J. E., Garland, A., Iyer, P. P., Murdock, J., Steinmetz, S. T., Yarritu, K. A., Johnson, C. M., & Stracuzzi, D. J. AI for Technoscientific Discovery: A Human-Inspired Architecture.  United Kingdom.  https://doi.org/10.1016/j.yjoc.2024.100077

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                    Tsao, J. Y., Abbott, R. G., Crowder, D. C., Desai, S., Dingreville, R. P. M., Fowler, J. E., Garland, A., Iyer, P. P., Murdock, J., Steinmetz, S. T., Yarritu, K. A., Johnson, C. M., and Stracuzzi, D. J. Thu .  
"AI for Technoscientific Discovery: A Human-Inspired Architecture".  United Kingdom.  https://doi.org/10.1016/j.yjoc.2024.100077.

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@article{osti_2311922,

  title        = {AI for Technoscientific Discovery: A Human-Inspired Architecture},

  author       = {Tsao, J. Y. and Abbott, R. G. and Crowder, D. C. and Desai, S. and Dingreville, R. P. M. and Fowler, J. E. and Garland, A. and Iyer, P. P. and Murdock, J. and Steinmetz, S. T. and Yarritu, K. A. and Johnson, C. M. and Stracuzzi, D. J.},

  abstractNote = {We present a high-level architecture for how artificial intelligences might advance and accumulate scientific and technological knowledge, inspired by emerging perspectives on how human intelligences advance and accumulate such knowledge. Agents advance knowledge by exercising a technoscientific method—an interacting combination of scientific and engineering methods. The technoscientific method maximizes a quantity we call “useful learning” via more-creative implausible utility (including the “aha!” moments of discovery), as well as via less-creative plausible utility. Society accumulates the knowledge advanced by agents so that other agents can incorporate and build on to make further advances. The proposed architecture is challenging but potentially complete: its execution might in principle enable artificial intelligences to advance and accumulate an equivalent of the full range of human scientific and technological knowledge.},

  doi          = {10.1016/j.yjoc.2024.100077},

  journal      = {Journal of Creativity (Online)},

  number       = 2,

  volume       = 34,

  place        = {United Kingdom},

  year         = {Thu Aug 01 00:00:00 EDT 2024},

  month        = {Thu Aug 01 00:00:00 EDT 2024}

}

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Title: AI for Technoscientific Discovery: A Human-Inspired Architecture

Abstract

Citation Formats

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Generalizing from a Few Examples
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A History of Microwave Heating Applications
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journal, September 2001

Polanyi's revenge and AI's new romance with tacit knowledge
journal, January 2021

Explainable Machine Learning for Scientific Insights and Discoveries
journal, January 2020

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications
journal, January 2017

Continual lifelong learning with neural networks: A review
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
journal, February 2019

Defining Creativity: Don't We Also Need to Define What Is Not Creative?
journal, January 2016

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Technological paradigms and technological trajectories
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More Is Different
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Inverse design in search of materials with target functionalities
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Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
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