The Artificial Intelligence community was abuzz recently with the news that Google has open-sourced it’s machine learning framework, called TensorFlow. This system was created by the Google Brain Team, working in it’s Machine Intelligence Research group.
This is not the first open source machine learning framework. Within the Python environment in particular, there are frameworks such as scikit-learn, PyBrain and others that have been around for a good while. What’s different about this new framework is that it has the backing of one of the most advanced commercial machine learning organisations, Google. In committing the project to open-source, it is inviting researchers, commercial practitioners and hobbyists to contribute to the framework. With Google’s backing, it seems destined for a long life.
But back to today. The framework has both Python and C++ APIs, with the expectation that C++ will be slightly faster on certain tasks. The instructions for installing TensorFlow are straightforward, but immediately I ran into a problem. My (slightly ageing) MacBook was running Python 2.7.5 and running TensorFlow caused a segmentation fault. Updating to Python 2.7.10 fixed the problem and I was able to successfully run though some of the tutorials.
There seems to be a wide range of neural network capabilities already available within the framework which provides much opportunity for exploration and experimentation. The tutorials cover areas such as handwriting recognition, image classification (using convolutional neural networks) and language modelling (using recurrent neural networks).
What’s also interesting is that since it’s an open source framework, the underlying code behind all these machine learning techniques is available for anyone to download, examine, modify and improve.
What will be the long-term impact of this is hard to tell. However, it is clear that Google has already put in quite a bit of effort already effort into this framework, and now that it’s out in the open, there will be lots more improvement to come.
If you want to know more and perhaps even try it out yourself, you can download TensorFlow here.