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Creative AI Through Evolutionary Computation: Principles and Examples.

Risto Miikkulainen1

  • 1The University of Texas at Austin and Cognizant Technology Solutions, San Francisco, USA.

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View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence excels at discovering novel solutions within complex search spaces. Evolutionary computation, a population-based technique, is key to unlocking creative AI, potentially surpassing deep learning.

Keywords:
Decision makingEvolutionary computationMachine creativityNeuroevolutionReal-world applicationsSurrogate modeling

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Area of Science:

  • Artificial Intelligence
  • Computational Intelligence
  • Evolutionary Computation

Background:

  • The primary strength of AI lies in generating novel solutions, not just modeling existing knowledge.
  • Complex, high-dimensional search spaces often contain these innovative solutions.

Purpose of the Study:

  • To highlight the suitability of population-based search techniques for discovering creative AI solutions.
  • To position evolutionary computation as the next frontier in AI development, following deep learning.

Main Methods:

  • Utilizing population-based search techniques, specifically variants of evolutionary computation.
  • Applying these methods to navigate large, high-dimensional search spaces.

Main Results:

  • Evolutionary computation effectively identifies creative solutions within complex problem domains.
  • These techniques enable the discovery of novel, practical applications for AI.

Conclusions:

  • Population-based search, particularly evolutionary computation, is a powerful approach for creative AI.
  • Creative AI driven by evolutionary computation is poised to be the next significant advancement in artificial intelligence.