风险投资
人工神经网络
训练集
计算机科学
人工智能
产业组织
机器学习
数据科学
营销
业务
财务
作者
James Bessen,Stephen Michael Impink,Lydia Reichensperger,Robert Seamans
出处
期刊:Research Policy
[Elsevier]
日期:2022-03-07
卷期号:51 (5): 104513-104513
被引量:44
标识
DOI:10.1016/j.respol.2022.104513
摘要
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 a positive correlation between having proprietary training data and obtaining future venture capital funding. Moreover, this correlation is greater for startups in markets where data is a major advantage and for startups using more sophisticated algorithms, such as neural networks and ensemble learning.
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