Google's AI Rewrite

Building Machine Learning Into Everything

- Rob Marvin

Makoto Koike is a cucumber farmer in Japan. He’s a former embedded systems designer who spent years working in the Japanese automobile industry, but in 2015 he returned home to help out on his parents’ farm. He soon realized that the manual task of sorting cucumbers by color, shape, size, and attributes such as “thorniness” was often trickier and more arduous than growing them. Inspired by the deep learning innovation of Google’s artificial intelligence (AI) software AlphaGo, he set out to automate the task.

Businesses are beginning to implement practical AI in all sorts of ways, but it’s safe to say that no one saw Koike’s cucumber-sorting solution coming. Koike had never worked with AI techniques before, but using the open-source Tensor Flow machine learning (ML) library, he started inputting images of cucumbers. Thanks to computer vision algorithms for recognizing objects and deep learning to train Tensor Flow on the nuances of cucumbers, Koike realized it could identify and sort the vegetables with a high level of accuracy. Then, by using nothing but Tensor Flow and a cheap Raspberry Pi 3 computer, Koike built his automated sorting machine, which the farm still uses today.

Tensor Flow is one of the many open-source algorithms and tools revolutionizing what businesses and developers can solve using AI. The company expanded on its mission to “bring the benefits of AI to everyone” with the release of Google.ai at its Google I/O conference, bundling all its AI resources together into a unified platform. Google is also incorporating these techniques and application programming interfaces (APIs) into everything it does, baking ML into its products and fundamentally redefining how its software works in the process.