Why did Google Open-Source TensorFlow?

On November 9, 2015, Google announced to a pleased and perplexed AI community that it has open-sourced TensorFlow, its proprietary machine learning system. Sundar Pichai said he hoped “this will let the machine learning community—everyone from academic researchers, to engineers, to hobbyists—exchange ideas much more quickly, through working code rather than just research papers”.

Although this brewed a lot of debate, very few people must have realized the impact of this decision at that point of time, and those who did very well knew it was a pivotal moment in Google’s strides towards an AI-first world.

The Inception of TensorFlow

TensorFlow began as part of Google’s former DeepDream product called DistBelief. The DeepDream program was built for scientists and engineers to visualize how image processing is done by deep neural networks. However, the algorithm went viral as people started visualizing psychedelic art in it, unaware that those images were powered by two of the most advanced technologies: deep learning and neural networks.

This led to the creation of TensorFlow, a comprehensive machine learning platform that enabled a plethora of deep learning and neural network-based projects. A highly flexible and scalable platform, TensorFlow has accelerated the production of several machine learning and AI-based projects across applications, including face recognition, music, art, and online content. And ever since Google has open-sourced it, one can only imagine the boom TensorFlow is set to create in the world of AI. What with it being equally accessible to scientists and talented enthusiasts alike, it is imperative that their inspired contributions will harness the yet unfathomable power of Artificial Intelligence.

Okay, agreed that the move is all set to redefine IT as we know it, but what was in it for Google when it decided to make TensorFlow open source? Let’s take a look.

Competitive Edge in the Market

A platform with such incredible potential had to go open source, as staying proprietary would have been pointless and extremely unfortunate. A majority of the deep learning core users prefer working on open source environments as they are much more convenient and certainly conducive to the process. Major competitors like Keras and Theano had already gone open source, and Google didn’t want to lose out in its vision to lead the AI boom.

Better Support for Google Brain

You all must have heard Sundar Pichai talking about Google’s transformation from Search to AI; Google Brain is the project that will drive this transformation. Google Brain is being developed by the best minds in the industry, including Jeff Dean, Geoffery Hilton, and Andrew NG among others, who are also the minds behind TensorFlow. Open-sourcing TensorFlow will only accelerate the platform’s development while also making significant inroads into relevant research areas, which will further strengthen Google’s hold on AI and Cloud.

Leveraging Academic Intelligentsia

Some of the major innovations in the past decade have come as research prototypes from universities before they went mainstream. AI is still making that transition and, consequently, requires huge investments in research. TensorFlow going open source will help a lot in this respect, as some of the best minds will collaborate towards several AI problems on Google’s platform for free, and this will add to the existing body of knowledge. They will have all the access to bleeding-edge algorithms that are not yet available in the market. Their engineers could simply pick and choose what they like and start developing commercially ready services.

TensorFlow as Platform-as-a-Service for AI

What Amazon did for storage with AWS, Google can do it for AI with TensorFlow. As mentioned earlier, open-sourcing TensorFlow will give access to a lot of brilliant minds out there. This will accelerate the time to build and test apps through collaborative development; that is, most of the basic infrastructure will be available for developers to further build on, thereby leaving a lot of scope for customization and abstraction.

Expand its Talent Pool

Hiring for AI development is competitive in the Silicon Valley as all major companies vie for attention from the same niche talent pool. With TensorFlow made freely available, Google can quickly reach out to a talent pool specifically well-versed with the technology and also save on training costs.Just look at the interest TensorFlow has generated on a forum like StackOverflow:

                                                                                  

                                                                                       

This indicates that growing number of users are asking and inquiring about TensorFlow. Some of these users will migrate into power users who the Google can tap into. A developer pool at this scale would never have been possible with a proprietary tool.

The road to AI world domination for Google is on the back of an open sourced TensorFlow platform. It appears not just exciting but also promises to be one full of exponential growth, crowdsourced innovation and learnings drawn from other highly successful Google products and services.

The storm that started three years ago is surely morphing into a hurricane. As Professor Michael Guerzhoy of University of Toronto quotes in Business Insider, “Ten years ago, it took me months to do something that for my students takes a few days with TensorFlow.”

This article is written by Packt Publishing, the leading UK provider of technology eBooks, coding eBooks, videos and blogs; helping it professionals to put the software to work.

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