Quantifying the progress of artificial intelligence subdomains using the patent citation network

Abstract

Even though Artificial Intelligence (AI) has been having a transformative effect on human life, there is currently no precise quantitative method for measuring and comparing the performance of different AI methods. Technology Improvement Rate (TIR) is a measure that describes a technology’s rate of performance improvement, and is represented in a generalization of Moore’s Law. Estimating TIR is important for R&D purposes to forecast which competing technologies have a higher chance of success in the future. The present contribution estimates the TIR for different subdomains of applied and industrial AI by quantifying each subdomain’s centrality in the global flow of technology, as modeled by the Patent Citation Network and shown in previous work. The estimated TIR enables us to quantify and compare the performance improvement of different AI methods. We also discuss the influencing factors behind slower or faster improvement rates. Our results highlight the importance of Rule-based Machine Learning (not to be confused with Rule-based Systems), Multi-task Learning, Meta-Learning, and Knowledge Representation in the future advancement of AI and particularly in Deep Learning.