It's something of a pattern for Google. Existing products (say, Theano and Torch) were revolutionary, but had a number of pain points that were difficult to rem

It's something of a pattern for Google. Existing products (say, Theano and Torch) were revolutionary, but had a number of pain points that were difficult to remove[0]. TensorFlow is essentially Theano without those problems, and with the guarantee of continued relevance thanks to Google's backing[1]. And of course, they reap the benefits of making it "open-source" while keeping a closed version for private use, similar to Chrome and Google Ultron. However, I still feel kinda nostalgic for those warm summer days, writing my first autoencoder in Theano and using it to make blurry versions of photos. ----- 0. Theano had some truly gnarly exception tracebacks, which made it difficult to identify if you'd made a truly incompatible model or if one of the layers/operations just needed a datatype specified. 1. I wish more companies did something like this. It's not necessarily innovation, so much as having a corps of people experienced with some wondrous but unwieldy technology and then giving those people the opportunity to reimplement it "right". Maybe there are many companies that attempt it, but instead produce a clone with problems of its own? Or maybe you need someone like Jeff Dean supervising the project to make sure everything comes together?