Reed College.
ProtoLife, Inc.
Artif Life. 2019 Winter;25(1):33-49. doi: 10.1162/artl_a_00279.
We detect ongoing innovation in empirical data about human technological innovations. Ongoing technological innovation is a form of open-ended evolution, but it occurs in a nonbiological, cultural population that consists of actual technological innovations that exist in the real world. The change over time of this population of innovations seems to be quite open-ended. We take patented inventions as a proxy for technological innovations and mine public patent records for evidence of the ongoing emergence of technological innovations, and we compare two ways to detect it. One way detects the first instances of predefined patent pigeonholes, specifically the technology classes listed in the United States Patent Classification (USPC). The second way embeds patents in a high-dimensional semantic space and detects the emergence of new patent clusters. After analyzing hundreds of years of patent records, both methods detect the emergence of new kinds of technologies, but clusters are much better at detecting innovations that are unanticipated and undetected by USPC pigeonholes. Our clustering methods generalize to detect unanticipated innovations in other evolving populations that generate ongoing streams of digital data.
我们在有关人类技术创新的实证数据中发现了正在进行的创新。正在进行的技术创新是一种开放式进化的形式，但它发生在一个非生物的、文化的群体中，这个群体由实际存在于现实世界中的技术创新组成。这个创新群体随时间的变化似乎是相当开放的。我们以专利发明作为技术创新的代理，从公开专利记录中挖掘技术创新不断涌现的证据，并比较了两种检测方法。一种方法检测预先定义的专利分类（USPC）中列出的技术类别，检测到专利的第一个实例。另一种方法将专利嵌入高维语义空间，并检测新的专利集群的出现。在分析了数百年的专利记录后，这两种方法都检测到了新技术的出现，但集群更能检测到 USPC 分类未预见到的、未被发现的创新。我们的聚类方法可以推广到检测其他正在进化的、产生持续数据流的数字数据的群体中的意外创新。
