An evolutionary view on the emergence of Artificial Intelligence
Matheus E. Leusin, Bjoern Jindra, Daniel S. Hain

TL;DR
This paper uses evolutionary concepts and patent data to analyze the long-term global and national development patterns of AI, revealing increasing complexity and relatedness, with distinct national trajectories and core technology stability.
Contribution
It introduces an evolutionary framework to study AI emergence using patent indicators, highlighting patterns of relatedness and complexity at global and national levels.
Findings
Global AI relatedness and complexity are increasing.
Core AI technologies show decreasing related variety and rising complexity.
US and Japan increase innovation relatedness; China and South Korea show different trends.
Abstract
This paper draws upon the evolutionary concepts of technological relatedness and knowledge complexity to enhance our understanding of the long-term evolution of Artificial Intelligence (AI). We reveal corresponding patterns in the emergence of AI - globally and in the context of specific geographies of the US, Japan, South Korea, and China. We argue that AI emergence is associated with increasing related variety due to knowledge commonalities as well as increasing complexity. We use patent-based indicators for the period between 1974-2018 to analyse the evolution of AI's global technological space, to identify its technological core as well as changes to its overall relatedness and knowledge complexity. At the national level, we also measure countries' overall specialisations against AI-specific ones. At the global level, we find increasing overall relatedness and complexity of AI.…
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Taxonomy
TopicsEconomic and Technological Innovation · Economic Growth and Productivity · Innovation Diffusion and Forecasting
