7 min read

On November 9, 2015, a storm loomed over the SF Bay area creating major outages. At Mountain View, California, Google engineers were busy creating a storm of their own. That day, Sundar Pichai announced to the world that TensorFlow, their machine learning system, was going Open Source.

He said:

“…today we’re also open-sourcing TensorFlow. We hope 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.”

That day the tech world may not have fully grasped the gravity of the announcement but those in the know knew it was a pivotal moment in Google’s transformational journey into an AI first world.

How did TensorFlow begin?

TensorFlow was part of a former Google product called DistBelief. DistBelief was responsible for a program called DeepDream. The program was built for scientists and engineers to visualise how deep neural networks process images. As fate would have it, the algorithm went viral and everyone started visualising abstract and psychedelic art in it. Although people were having fun playing with image forms, they were unaware of the technology that powered those images – neural networks and deep learning – the exact reason why TensorFlow was built for. TensorFlow is a machine learning platform that allows one to run a wide range of algorithms like the aforementioned neural networks and deep learning based projects.

TensorFlow with its flexibility, high performance, portability, and production-readiness is changing the landscape of artificial intelligence and machine learning. Be it face recognition, music, and art creation or detecting clickbait headline for blogs, the use cases are immense. With Google open sourcing TensorFlow, the platform that powers Google search and other smart Google products is now accessible to everyone – researchers, scientists, machine learning experts, students, and others.

So why did Google open source TensorFlow?

Yes, Google made a world of difference to the machine learning community at large by open sourcing TensorFlow. But what was in it for Google? As it turns out, a whole lot. Let’s look at a few.

Google is feeling the heat from rival deep learning frameworks

Major deep learning frameworks like Theano, Keras, etc., were already open source. Keeping a framework proprietary was becoming a strategic disadvantage as most DL core users i.e. scientists, engineers, and academicians prefer using open source software for their work. “Pure” researchers and aspiring “Phds” are key groups that file major patents in the world of AI. By open sourcing TensorFlow, Google gave this community access to a platform it backs to power their research. This makes migrating the world’s algorithms from other deep learning tools onto TensorFlow theoretically possible. AI as a trend is clearly here to stay and Google wants a platform that leads this trend.

An open source TensorFlow can better support the Google Brain project

Behind all the PR, Google does not speak much about its pet project Google Brain. When Sundar Pichai talks of Google’s transformation from Search to AI, this project is doing all the work behind the scenes. Google Brain is headed by some of the best minds in the industry like Jeff Dean, Geoffery Hilton, Andrew NG, among many others. They developed TensorFlow and they might still have some state-of-the-art features up their sleeves privy only to them. After all, they have done a plethora of stunning research in areas like parallel computing, machine intelligence, natural language processing and many more. With TensorFlow now open sourced, this team can accelerate the development of the platform and also make significant inroads into areas they are currently researching on. This research can then potentially develop into future products for Google which will allow them to expand their AI and Cloud clout, especially in the enterprise market.

Tapping into the collective wisdom of the academic intelligentsia

Most innovations and breakthroughs come from universities before they go mainstream and become major products in enterprises. AI, still making this transition, will need a lot of investment in research. To work on difficult algorithms, researchers will need access to sophisticated ML frameworks. Selling TensorFlow to universities is an old school way to solve the problem – that’s why we no longer hear about products like LabView. Instead, by open-sourcing TensorFlow, the team at Google now has the world’s best minds working on difficult AI problems on their platform for free. As these researchers start writing papers on AI using TensorFlow, it will keep adding 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.

Google wants to develop TensorFlow as a platform-as-a-service for AI application development

An advantage of open-sourcing a tool is that it accelerates time to build and test through collaborative app development. This means most of the basic infrastructure and modules to build a variety of TensorFlow based applications will already exist on the platform. TensorFlow developers can develop and ship interesting modular products by mixing and matching code and providing a further layer of customization or abstraction.

What Amazon did for storage with AWS, Google can do for AI with TensorFlow. It won’t come as a surprise if Google came up with their own integrated AI ecosystem with TensorFlow on the Google Cloud promising you the AI resources your company would need. Suppose you want a voice based search function on your ecommerce mobile application. Instead, of completely reinventing the wheel, you could buy TensorFlow powered services provided by Google. With easy APIs, you can get voice based search and save substantial developer cost and time.

Open sourcing TensorFlow will help Google to extend their talent pipeline in a competitive Silicon Valley jobs market

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’s HR team can quickly reach out to a talent pool specifically well versed with the technology and also save on training cost. 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 HR team can tap into. A developer pool at this scale would never have been possible with a proprietary tool.

Replicating the success and learning from Android

Agreed, a direct comparison with Android is not possible. However, the size of the mobile market and Google’s strategic goal of mobile-first when they introduced Android bear striking similarity with the nascent AI ecosystem we have today and Google’s current AI-first rhetoric.

In just a decade since its launch, Android now owns more than 85% of the smartphone mobile OS market. Piggybacking on Android’s success, Google now has control of mobile search (96.19%), services (Google Play), a strong connection with the mobile developer community and even a viable entry into the mobile hardware market.

Open sourcing Android did not stop Google from making money. Google was able to monetize through other ways like mobile search, mobile advertisements, Google Play, devices like Nexus, mobile payments, etc. Google did not have all this infrastructure planned and ready before Android was open sourced – It innovated, improvised, and created along the way.

In the future, we can expect Google to adopt key learnings from its Android growth story and apply to TensorFlow’s market expansion strategy. We can also see supporting infrastructures and models for commercialising TensorFlow emerge for enterprise developers.

[dropcap]T[/dropcap]he 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 two 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.”


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