From Macro Polo, October 21:
Who benefits from the research breakthroughs made in the China-based research labs of American artificial intelligence (AI) companies?
Just five years ago, that question hardly ever came up, and if it was asked, the answer often centered on the shared benefits of global research. Today, much has changed. The field of machine learning has made major strides, China’s technology markets and its surveillance apparatus have boomed, and technological competition has moved to the center of the US-China relationship.....MUCH MORE
What do all these changes mean for the overseas research labs of leading American technology companies? To answer that question, it’s useful to zoom in on a specific research breakthrough to examine the ideas, institutions, and people involved in it. By tracing those factors over time, a better assessment can be made on where the benefits from this research flow to, and how government policies and corporate practices can best shape those flows.
The case I examine here is the single most-cited paper in AI research over the past five years: Deep Residual Learning for Image Recognition. Often abbreviated as “ResNet,” the 2015 paper is not just the most-cited AI paper based on Google Scholar metrics. With 25,256 citations between 2014 and July 2019, it’s the most-cited paper in any academic field during that time.
The Breakthrough: What’s So Important About ResNet?
ResNet’s central contribution was a technique making it possible to stack many more layers on a neural network—the engines behind much of machine learning today. By stacking more layers (making them “deeper”), the neural network’s performance can be dramatically improved for different tasks: facial recognition, natural language processing, speech recognition, and many other domains.
The technique proved so successful that in 2015, the research team behind the paper took home first place in two of the most important global image recognition contests. By 2017, the technique was one of the core advances behind AlphaGo Zero, the landmark DeepMind system that turned itself into the world’s Go champion by playing against itself.
The Institution: What Lab Produced ResNet?
ResNet was the product of a small research team at Microsoft Research Asia (MSRA), the Beijing lab of the American tech juggernaut.
Founded in 1998 and situated next to Tsinghua University, the lab quickly turned into a research powerhouse, making major contributions to both academic research and Microsoft’s global products. In 2004, MIT Technology Review dubbed it “the world’s hottest computer lab” for its research advances in machine learning, particularly in the areas of natural language processing, speech synthesis, and image recognition.
Research out of the MSRA lab fed directly into software innovations for Windows and advances in graphics for Microsoft’s Xbox, as well as a major improvement in typing input methods for character-based languages like Chinese.
Yet at the same time, MSRA has been perhaps the single most important institution in the birth and growth of the Chinese AI ecosystem over the past two decades. The lab served as a training ground for many future leaders of China’s then-embryonic AI ecosystem, with alumni that include Alibaba’s CTO, Baidu’s President, the head of technology strategy at Bytedance, and the founders of several unicorn AI startups. The Chinese media has even compared MSRA to the “Whampoa Academy of the Chinese internet“—a reference to the legendary military academy that churned out prominent army commanders for both the Kuomintang and the Chinese Communist Party....
