The Role of Evolution in Machine Intelligence
This addresses the research allocation problem in AI for researchers and practitioners, but it is incremental as it builds on existing evolutionary methods.
The paper argues that evolutionary algorithms are underinvested in machine intelligence research compared to direct learning, and suggests diversifying research across evolutionary approaches and introducing meta-evolutionary algorithms for optimal trade-offs.
Machine intelligence can develop either directly from experience or by inheriting experience through evolution. The bulk of current research efforts focus on algorithms which learn directly from experience. I argue that the alternative, evolution, is important to the development of machine intelligence and underinvested in terms of research allocation. The primary aim of this work is to assess where along the spectrum of evolutionary algorithms to invest in research. My first-order suggestion is to diversify research across a broader spectrum of evolutionary approaches. I also define meta-evolutionary algorithms and argue that they may yield an optimal trade-off between the many factors influencing the development of machine intelligence.