Today marks the culmination of two years of development for some software that could make machine learning a lot easier to program, thereby helping to democratize AI. That's the hope of Jeremy Howard, a co-founder of San Francisco-based Fast.ai, a startup outfit that is today releasing version 1.0 of "fastai," a set of code libraries designed to radically simplify writing machine learning tasks. Built on top of Facebook's Pytorch library, which also has its own 1.0 version release today, fastai allows one to do tasks such as run a convolutional neural network for image recognition on the ImageNet benchmark tests with just a handful of lines of code. The tools come out of a series of popular courses on machine learning run by Fast.ai, which Howard founded three years ago with his wife, Rachel Thomas. Both individuals had an over-arching goal of making AI much more accessible.