I think this highlights the disconnect between AI researchers and industry. This research focuses on image data and then makes the claim that they want to use it to enable millions of businesses to solve problems with machine learning.
However, image data isn't what most businesses are using for machine learning. They use relational datasets. If you look at the recent "State of Data Science" by Kaggle, relational data is ~3x more common than image data in every industry besides academia and the military [0]. While Google wants to 1000x the number of organizations using AI, they aren't focusing on the problems companies actually have.
Basically, academics love building AI for images, but what companies really need are better ways to build AI systems on tabular and relational data. Images will be a piece, but shouldn't be the focus.
Disclaimer: my company develops an open source library called Featuretools [1] to automate feature engineering for data with relational structure.
However, image data isn't what most businesses are using for machine learning. They use relational datasets. If you look at the recent "State of Data Science" by Kaggle, relational data is ~3x more common than image data in every industry besides academia and the military [0]. While Google wants to 1000x the number of organizations using AI, they aren't focusing on the problems companies actually have.
Basically, academics love building AI for images, but what companies really need are better ways to build AI systems on tabular and relational data. Images will be a piece, but shouldn't be the focus.
Disclaimer: my company develops an open source library called Featuretools [1] to automate feature engineering for data with relational structure.
[0] https://www.kaggle.com/surveys/2017
[1] https://github.com/featuretools/featuretools