Mark Johnson wants to beat the United States Department of Agriculture at its own game: predicting yields of America's crops. The USDA puts boots on the ground, deploying hundreds of workers to survey thousands of farms a month ahead of the October corn harvest, America's biggest crop. Johnson's startup, Descartes Labs, has just 20 employees, and they never leave the office in Los Alamos, New Mexico. Instead, Descartes relies on 4 petabytes of satellite imaging data and a machine learning algorithm to figure out how healthy the corn crop is from space. Corn yield prediction is big business in the US. Billions of dollars are at stake along the ag supply chain each year as corn starts to come out of the ground in August.

Translating design into code can be tedious and not particularly thought-provoking--which also happens to be the criteria that makes a task ripe for automation. The Copenhagen-based startup UIzard Technologies is already on it: The company has trained a neural network to take a screenshot of a graphic interface and translate it into lines of code, effectively eliminating that part of the web design process for developers. Impressively, the same model works across iOS, Android, and web-based interfaces, and at this early point in the research the algorithm works with 77% accuracy. Last week, Tony Beltramelli, the founder and CEO of UIzard Technologies published a research paper on how the model, called Pix2Code works. The gist is this: Like all machine learning, the researchers had to train the model on examples of the task at hand.