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.
翻译:目前的研究工作主要侧重于直接从经验中学习的算法。我认为,替代的进化对于发展机器情报十分重要,在研究分配方面投资不足。这项工作的主要目的是评估各种进化算法中哪些方面可以投资于研究。我的第一级建议是将研究分散到更广泛的进化方法中。我还定义了元进化算法,并主张这些算法可以在影响机器情报发展的许多因素之间产生最佳的取舍。