Google Open Sources its Sophisticated Machine Learning Toolset

Artificial intelligence and machine learning are going through a mini-renaissance right now. In fact, just this week, we reported on how H2O.ai, formerly known as Oxdata, has announced a new funding round that it is getting to the tune of $20 million. The money will go toward advancing its machine learning toolset.

 Now, Google is making a possibly hugely influential contribution to the field of machine learning. It is open sourcing a program called TensorFlow that will be freely available. It’s based on the same internal toolset that Google has spent years developing to support its AI software and other predictive and analytics programs.

You can find out more about TensorFlow at its site, and you might be surprised to learn that it is the engine behind several Google tools you may already use, including Google Photos and the speech recognition found in the Google app.

According to Google, TensorFlow could help speed up processes ranging from drug discovery to processing astronomy-related data sets. Vincent Vanhoucke is an engineer who has worked on TensorFlow, and he writes:

"Very proud to be open-sourcing TensorFlow, Google's newest Deep Learning framework! TensorFlow is both a production-grade C++ backend, which runs on Intel CPUs, NVidia GPUs, Android, iOS and OSX, and a very simple and research-friendly Python front-end that interfaces with Numpy, iPython Notebooks, and all the familiar Python-based scientific tooling that we love. TensorFlow is what we use every day in the Google Brain team, and while it's still very early days and there are a ton of rough edges to be ironed out, I'm excited about the opportunity to build a community of researchers, developers and infrastructure providers around it. Try it out!"

 The TensorFlow team adds:

"TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well."

 The basic goal with most machine learning tools is to take a vast quantity of data and reduce it to manageable, actionable insights. TensorFlow, in all likelihood, will branch out as an open source tool into forks that can be applied for these types of tasks and more. It has already found applications in visual recognition scenarios and in speech recognition. This toolset will be interesting to follow.