Accelerating AI for science: open data science for science

Neil D. Lawrence; Jessica Montgomery

doi:10.1098/rsos.231130

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Accelerating AI for science: open data science for science

Neil D. Lawrence, Jessica Montgomery

Royal Society Open Science, 11(8), 2024.

Abstract

Aspirations for artificial intelligence (AI) as a catalyst for scientific discovery are growing. High-profile successes deploying AI in domains such as protein folding have highlighted AI’s potential to unlock new frontiers of scientific knowledge. However, the pathway from AI innovation to deployment in research is not linear. Those seeking to drive a new wave of scientific progress through the application of AI require a diffusion engine that can enhance AI adoption across disciplines. Lessons from previous waves of technology change, experiences of deploying AI in real-world contexts and an emerging research agenda from the AI for science community suggest a framework for accelerating AI adoption. This framework requires action to build supply chains of ideas between disciplines; rapidly transfer technological capabilities through open research; create AI tools that empower researchers; and embed effective data stewardship. Together, these interventions can cultivate an environment of open data science that deliver the benefits of AI across the sciences.

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BibTeX


@Article{accelerating-ai-for-science-open-data-science-for-science,
  title = 	 {Accelerating AI for science: open data science for science },
  author = 	 {Lawrence, Neil D. and Montgomery, Jessica},
  journal =      {Royal Society Open Science},
  year = 	 {2024},
  volume = 	 {11},
  number =       {8},
  doi = 	 {10.1098/rsos.231130},
  url = 	 {/publications/accelerating-ai-for-science-open-data-science-for-science.html},
  abstract = 	 {Aspirations for artificial intelligence (AI) as a catalyst for scientific discovery are growing. High-profile successes deploying AI in domains such as protein folding have highlighted AI’s potential to unlock new frontiers of scientific knowledge. However, the pathway from AI innovation to deployment in research is not linear. Those seeking to drive a new wave of scientific progress through the application of AI require a diffusion engine that can enhance AI adoption across disciplines. Lessons from previous waves of technology change, experiences of deploying AI in real-world contexts and an emerging research agenda from the AI for science community suggest a framework for accelerating AI adoption. This framework requires action to build supply chains of ideas between disciplines; rapidly transfer technological capabilities through open research; create AI tools that empower researchers; and embed effective data stewardship. Together, these interventions can cultivate an environment of open data science that deliver the benefits of AI across the sciences.
}
}

Endnote

%0 Journal Article
%T Accelerating AI for science: open data science for science 
%A Neil D. Lawrence
%A Jessica Montgomery
%J Royal Society Open Science
%D 2024	
%F accelerating-ai-for-science-open-data-science-for-science
%R 10.1098/rsos.231130
%U /publications/accelerating-ai-for-science-open-data-science-for-science.html
%V 11
%N 8
%X Aspirations for artificial intelligence (AI) as a catalyst for scientific discovery are growing. High-profile successes deploying AI in domains such as protein folding have highlighted AI’s potential to unlock new frontiers of scientific knowledge. However, the pathway from AI innovation to deployment in research is not linear. Those seeking to drive a new wave of scientific progress through the application of AI require a diffusion engine that can enhance AI adoption across disciplines. Lessons from previous waves of technology change, experiences of deploying AI in real-world contexts and an emerging research agenda from the AI for science community suggest a framework for accelerating AI adoption. This framework requires action to build supply chains of ideas between disciplines; rapidly transfer technological capabilities through open research; create AI tools that empower researchers; and embed effective data stewardship. Together, these interventions can cultivate an environment of open data science that deliver the benefits of AI across the sciences.

RIS


TY  - JOUR
TI  - Accelerating AI for science: open data science for science 
AU  - Neil D. Lawrence
AU  - Jessica Montgomery
DA  - 2024/08/21	
ID  - accelerating-ai-for-science-open-data-science-for-science
VL  - 11
IS  - 8
DO  - 10.1098/rsos.231130
UR  - /publications/accelerating-ai-for-science-open-data-science-for-science.html
AB  - Aspirations for artificial intelligence (AI) as a catalyst for scientific discovery are growing. High-profile successes deploying AI in domains such as protein folding have highlighted AI’s potential to unlock new frontiers of scientific knowledge. However, the pathway from AI innovation to deployment in research is not linear. Those seeking to drive a new wave of scientific progress through the application of AI require a diffusion engine that can enhance AI adoption across disciplines. Lessons from previous waves of technology change, experiences of deploying AI in real-world contexts and an emerging research agenda from the AI for science community suggest a framework for accelerating AI adoption. This framework requires action to build supply chains of ideas between disciplines; rapidly transfer technological capabilities through open research; create AI tools that empower researchers; and embed effective data stewardship. Together, these interventions can cultivate an environment of open data science that deliver the benefits of AI across the sciences.

ER  -

APA


Lawrence, N.D. & Montgomery, J.. (2024). Accelerating AI for science: open data science for science . Royal Society Open Science 11(8) doi:10.1098/rsos.231130 Available from /publications/accelerating-ai-for-science-open-data-science-for-science.html.