Over the past few years, deep-learning artificial-intelligence algorithms have made tremendous leaps. They can now translate words and sentences in real time. They can recognize faces (and whom they belong to). And they can even order Chinese delivery for you -- as is the case with Facebook's (NASDAQ:FB) experimental virtual assistant, M.
You'd think that computer scientists would want to keep their software under wraps, but in fact just the opposite is happening. The foremost AI researchers are pushing to make all of their developments open source, available for anyone to work with. Most recently, Facebook open-sourced the design of its latest computer server built to run its deep-learning algorithms, with plans to add the design to its Open Compute Project. This development comes just a month after Google, an Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL) company, released the source code for Tensorflow, its AI used for things such as photo search, voice recognition, translation, and more.
So why are Google and Facebook both giving away access to their AI designs?
The Android of AI
Google's decision to license Android via open source led to it becoming the most popular operating system in the world. Google has benefited greatly from the rapid growth in Internet-connected devices running Android and searching on Google. It also has seen an extraordinary benefit from controlling the platform, in particular the Google Play Store and the other default apps included with Google's stock version of Android.
The Google Play app store is bringing in around $1 billion gross revenue per month for Google thanks to the proliferation of Android. Likewise, Google now sees more searches on mobile devices than on desktops and laptops. Google is benefiting substantially from controlling the ecosystem around Android.
The strategy appears similar with AI. By open-sourcing hardware designs and algorithms, both Facebook and Google stand to see substantial benefits. They'll be able to easily identify new talent that's interested in working on its algorithms and hardware by seeing who's contributing to the code base. Additionally, they'll increase the prevalence of deep-learning algorithms, providing more data to train the algorithms and make them better while decreasing the cost of hardware designs. Finally, and most importantly, they'll be able to control the platform future algorithms are built on.
Scaling for free
Google didn't have to build the hardware to scale Android and its Google Play store because it gave other people the tools to do that. Likewise, Google and Facebook don't have to think of every possible instance for their deep-learning algorithms, because they've given the tools to other engineers. Additionally, Facebook doesn't have to order a ton of "Big Sur" servers -- its latest design -- to lower the price. Other people will order the hardware as well, allowing manufacturers to scale up.
Scaling is particularly important for deep-learning AI algorithms. They feed on data, and while Facebook and Google have tons of data, their stores are only a fraction of the potential data available to train these AI algorithms. By open-sourcing their algorithms, Facebook and Google get all the benefits of training algorithms using other people's data and use cases without having to pay for it.
The future of AI
Hardware is just as important to the future of deep-learning algorithms as software. The better the hardware, the more effective the algorithms become. Facebook's Big Sur server is twice as fast as its predecessor, which means it can train AI algorithms twice as fast, or train neural networks twice as large.
Facebook's decision to open-source Big Sur is just as notable as open-sourcing its software through Torch or Google's decision to open-source Tensorflow. It also increases Facebook's chances of controlling at least part of the advancements in AI as it licenses the hardware design on which the neural networks run.
While Facebook and Google are working hard to create the next great development in AI, their decisions to open-source their work increase the likelihood they'll at least have a part in it if they don't create it in-house. They can then feed back those breakthroughs into their own products, such as Search and News Feed.