Nov 18, 2020

Last week, I wrote about the limitation of using human-level performance (HLP) as a metric to beat in machine learning applications for manufacturing and other fields. In this letter, I would like to show why beating HLP isn’t always the best way to improve performance.

Social biases are well documented in decisions made by supervised models trained on ImageNet’s labels. But they also crept into the output of unsupervised models pretrained on the same dataset.

Covid-19 wreaked havoc with models that predict retail sales — but China’s biggest annual e-commerce event showed that they’re back in business.

Large transformer networks work wonders with natural language, but they require enormous amounts of computation. New research slashes processor cycles without compromising performance.

A new study used AI to track the explosive growth of AI innovation. Researchers from the U.S. Patent and Trademark Office deployed natural language processing to track AI’s increasing presence in four decades of patent data.

Last week, I wrote about the limitation of using human-level performance (HLP) as a metric to beat in machine learning applications for manufacturing and other fields. In this letter, I would like to show why beating HLP isn’t always the best way to improve performance.