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Updated: May 15, 2025

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Generative artificial intelligence: a historical perspective.

Ran He1,2, Jie Cao1, Tieniu Tan1,2

  • 1New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

National Science Review
|April 7, 2025
PubMed
Summary
This summary is machine-generated.

Generative artificial intelligence (GAI) has evolved over 70 years, with recent advances enabling content creation and novel insights. This review categorizes GAI developments and explores future research directions.

Keywords:
artificial intelligencefoundation modelgenerative method

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Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Generative artificial intelligence (GAI) has seen rapid advancements, enabling creation of diverse content like text, images, and code.
  • GAI offers insights beyond traditional analytical tools.

Purpose of the Study:

  • To provide a historical overview of Generative Artificial Intelligence (GAI) developments over the past 70 years.
  • To categorize GAI achievements and identify future research avenues.

Main Methods:

  • Review of historical and ongoing developments in GAI.
  • Categorization of GAI achievements into four types: rule-based, model-based, deep generative, and foundation models.

Main Results:

  • GAI advancements are classified into rule-based systems, model-based algorithms, deep generative methodologies, and foundation models.
  • Successful generative applications have been reviewed, highlighting current challenges.

Conclusions:

  • GAI technologies have a rich history and diverse applications.
  • Further research is needed to better understand, utilize, and harness GAI capabilities.