Abstract

The year 2022 marks the first year that artificial intelligence surpasses statistics as keywords on Google web search, compared to the 2000s when statistics had ten times more than artificial intelligence (https://trends.google.com). As part of artificial intelligence, machine learning has the same growing trend as artificial intelligence. However, their difference gets smaller and smaller. In the 2000s, artificial intelligence is 15 times as many as machine learning. The difference is reduced to four times. This summary is a typical example of descriptive statistics, which joins inferential statistics as the two major statistics divisions. As a branch of inferential statistics, statistical learning is the central hub that links to machine learning. The web search activity remains relatively constant for statistical learning. However, machine learning has gained 40-fold over statistics learning in the last five years compared to two-fold in the 2000s.
Their difference and similarity have been debated since the creation of these terms and will continue to emerge. Among the 212 Invited Paper Sessions (IPS) at the 63rd ISI WCS in 2021, there were three IPS on statistical learning: Advances in Statistical Learning Applications (IPS 40) Nonparametric Statistical Learning of Complex Data (IPS 80), and Statistical Learning Models in Practice (IPS 173). There were also three IPS on machine learning: Labeling Issues in Machine Learning Methodology (IPS 21), Stein’s method for Machine Learning (IPS 34), and Developing Machine Learning Methods in Experimental Official and Analytical Statistics (IPS 211). There was no session on both statistical learning and machine learning to address a critical question: how do we inspire each other to build better paths from data to conclusions?
This invited paper session is organized to answer this question by Dr. Zhiwu Zhang, an Associate Professor at Washington State University. Dr. Zhang has developed multiple widely used statistical methods and computing tools for genome-wide association studies and genomic prediction (https://zzlab.net). Currently, Dr. Zhang is leading two USDA projects to apply artificial intelligence for improving wheat quality using hyperspectral images and plant breeding efficiency using both satellite and unmanned aerial vehicles. The IPS invited five diversified speakers, from theory to application, from medicine to economy, from statistical learning to machine learning and artificial intelligence. These speakers include Dr. Qiwei Li (The University of Texas at Dallas), Dr. Arvind Rao (University of Michigan), Dr. Honglang Wang (Purdue University), Dr. Shan Yu (University of Virginia), and Dr. Elena Zarova (Plekhanov Russian Academy of Economics). Their common interdisciplinary approach is to integrate human knowledge and computer capability to build explainable and predictable paths from data to conclusions.