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通过进化计算实现的创意人工智能：原理与示例。

Creative AI Through Evolutionary Computation: Principles and Examples.

作者信息

Miikkulainen Risto

机构信息

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

出版信息

SN Comput Sci. 2021;2(3):163. doi: 10.1007/s42979-021-00540-9. Epub 2021 Mar 23.

DOI:10.1007/s42979-021-00540-9
PMID:33778772
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7986182/
Abstract

The main power of artificial intelligence is not in modeling what we already know, but in creating solutions that are new. Such solutions exist in extremely large, high-dimensional, and complex search spaces. Population-based search techniques, i.e. variants of evolutionary computation, are well suited to finding them. These techniques make it possible to find creative solutions to practical problems in the real world, making creative AI through evolutionary computation the likely "next deep learning."

摘要

人工智能的主要力量不在于对我们已知的事物进行建模，而在于创造新的解决方案。此类解决方案存在于极其庞大、高维和复杂的搜索空间中。基于群体的搜索技术，即进化计算的变体，非常适合于找到这些解决方案。这些技术使我们能够找到现实世界中实际问题的创造性解决方案，通过进化计算实现创造性人工智能很可能成为 “下一个深度学习”。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab0/7986182/f308731386ca/42979_2021_540_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab0/7986182/85f592d2d814/42979_2021_540_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab0/7986182/6a25a29649b3/42979_2021_540_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab0/7986182/d687c8fb28ff/42979_2021_540_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab0/7986182/f308731386ca/42979_2021_540_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab0/7986182/85f592d2d814/42979_2021_540_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab0/7986182/6a25a29649b3/42979_2021_540_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab0/7986182/d687c8fb28ff/42979_2021_540_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6ab0/7986182/f308731386ca/42979_2021_540_Fig4_HTML.jpg

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