James Bessen, Stephen Michael Impink, Lydia Reichensperger, Robert C. Seamans
{"title":"数据在人工智能初创企业成长中的作用","authors":"James Bessen, Stephen Michael Impink, Lydia Reichensperger, Robert C. Seamans","doi":"10.2139/ssrn.3896463","DOIUrl":null,"url":null,"abstract":"Artificial intelligence (“AI”)-enabled products are expected to drive economic growth. Training data are important for firms developing AI-enabled products; without training data, firms cannot develop or refine their algorithms. This is particularly the case for AI startups developing new algorithms and products. However, there is no consensus in the literature on which aspects of training data are most important. Using unique survey data of AI startups, we find that startups with access to proprietary training data are more likely to acquire venture capital funding.","PeriodicalId":14586,"journal":{"name":"IO: Productivity","volume":"6 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"The Role of Data for AI Startup Growth\",\"authors\":\"James Bessen, Stephen Michael Impink, Lydia Reichensperger, Robert C. Seamans\",\"doi\":\"10.2139/ssrn.3896463\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Artificial intelligence (“AI”)-enabled products are expected to drive economic growth. Training data are important for firms developing AI-enabled products; without training data, firms cannot develop or refine their algorithms. This is particularly the case for AI startups developing new algorithms and products. However, there is no consensus in the literature on which aspects of training data are most important. Using unique survey data of AI startups, we find that startups with access to proprietary training data are more likely to acquire venture capital funding.\",\"PeriodicalId\":14586,\"journal\":{\"name\":\"IO: Productivity\",\"volume\":\"6 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IO: Productivity\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3896463\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IO: Productivity","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3896463","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Artificial intelligence (“AI”)-enabled products are expected to drive economic growth. Training data are important for firms developing AI-enabled products; without training data, firms cannot develop or refine their algorithms. This is particularly the case for AI startups developing new algorithms and products. However, there is no consensus in the literature on which aspects of training data are most important. Using unique survey data of AI startups, we find that startups with access to proprietary training data are more likely to acquire venture capital funding.