The company uses TensorFlow to improve its products by training its neural networks faster but it’s looking for a bit of help and that’s why Google has decided to make TensorFlow open source.
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For Google, that means it’s able to improve its products more quickly, the company explains. Google stresses that TensorFlow is ideal for research and product development because the two can be one in the same.
Google would benefit as a company if the tool it uses internally for machine learning becomes a standard in artificial intelligence research, as it would increase the pool of people improving the software it uses (and, as Bloomberg notes, act as a way to identify potential hires). That will help accelerate research on machine learning and ultimately improve technology for everybody. It also uses AI to help fight Gmail spam, while Google Research is using deep learning techniques to aid drug discovery. Right now, TensorFlow powers speech recognition in the Google app, visual search in Photos, and Smart Reply in Inbox.
Google has built and launched a new machine-learning system called TensorFlow, making it available for any developer through an online open source library. It may be useful wherever researchers are trying to make sense of very complex data-everything from protein folding to crunching astronomy data.
Machine learning is cropping up with increasing frequency in a variety of areas, including enterprise software.
TensorFlow is interesting for the way it enables researchers and developers to collaborate on machine learning tech. The open sourcing of TensorFlow was announced by none other than Google CEO, Sundar Pichai himself. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them. Smart Reply reads the content of the email, then suggests short phrases at the bottom of the screen which you can use to reply.
For more information on TensorFlow, kindly check out the official website.
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Google says it used its earlier system, DistBelief, developed in 2011, to demonstrate that concepts like “cat” can be learned from unlabeled YouTube images, to improve speech recognition in the Google app by 25%, and to build image search in Google Photos. TensorFlow was built from the ground up to be fast, portable, and ready for production service.
