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Accelerating AI for science: open data science for science.

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Artificial intelligence (AI) can accelerate scientific discovery but requires a framework for widespread adoption. Enhancing AI diffusion across disciplines through open science and better data stewardship is key to unlocking its full potential.

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Area of Science:

  • Scientific discovery
  • Artificial intelligence applications
  • Interdisciplinary research

Background:

  • Growing aspirations for artificial intelligence (AI) to catalyze scientific discovery.
  • Successes in fields like protein folding demonstrate AI's potential for new knowledge.
  • The path from AI innovation to research deployment is complex and non-linear.

Purpose of the Study:

  • To propose a framework for accelerating AI adoption in scientific research.
  • To identify key interventions for enhancing AI diffusion across disciplines.
  • To foster an environment of open data science for broader AI benefits.

Main Methods:

  • Synthesizing lessons from past technology adoption waves.
  • Analyzing real-world AI deployment experiences.
  • Incorporating insights from the AI for Science research agenda.

Main Results:

  • A proposed framework for accelerating AI adoption in science.
  • Identified interventions: interdisciplinary idea exchange, open research for capability transfer, researcher-empowering AI tools, and robust data stewardship.
  • Emphasis on cultivating open data science.

Conclusions:

  • A structured approach is needed to translate AI potential into widespread scientific advancement.
  • Interdisciplinary collaboration and open science practices are crucial for AI diffusion.
  • Effective data stewardship and accessible AI tools will empower researchers and accelerate discovery.