Toward the end of 2016, Google DeepMind made their machine learning platform, DeepMind Lab, publicly available. Despite warnings from experts like Professor Stephen Hawking, Google’s decision to expose its software to other developers is part of a movement to further develop the capabilities of machine learning. They aren’t the only ones though. Facebook made its deep learning software public last year, and Elon Musk’s non-profit organization OpenAI released Universe, an open software platform that can be used to train AI systems. So, why have Google, OpenAI, and others made their platforms public, and how will this affect the adoption of machine learning?
The examples mentioned gives us a better picture. If you look closely, machine learning has always been open-source, and open R&D is the fundamental reason why machine learning is where it is today.
By making its machine learning platform available to the public, Google has validated an increased consciousness about its AI research. There are various advantages to making the software accessible such as finding new talent and capable startups to add to the Alphabet Inc. family. At the same time, developers can access DeepMind Lab, which will help address one of the key issues with ML research – the dearth of training environments. OpenAI has introduced a new virtual school for AI, Universe, which uses games and websites to train AI systems.
Making machine learning platforms publicly available is a much-needed move now.
Reproducing scientific results and fair comparison of algorithms: In machine learning, numerical simulations are frequently used to provide experimental validation and comparison of methods. Preferably, such a comparison between methods is based on a rigorous theoretical analysis. Open source tools and technology offer an opportunity to thoroughly conduct research using publicly available source code without depending on the vendor.
Quick bug finding and fixing: When you carry out machine learning projects using open source software, it becomes easy to detect and resolve bugs in the software.
Accelerate scientific development with low-cost, reusing methods: It is a known fact that scientific progress is always made based on existing methods and discovery, and the machine learning field is not an exception. The availability of open source technologies in machine learning can leverage existing resources for research and projects greatly.
Long-term availability and support: Whether it is an individual researcher, developer, or data scientist, open source might serve as a medium to ensure that everyone can use his/her research or discovery even after changing jobs. Thus, the chances of having long-term support are increased by releasing code under an open source license.
Faster adoption of Machine Learning by various industries: There are notable paradigms of the open source software that has supported the creation of multi-billion dollar machine learning companies and industries. The main reason for the adoption of machine learning by researchers and developers is the easy availability of high-quality open source implementations for free.
The advancement of open-source machine learning will enable a steeper adoption curve of Artificial Intelligence thus encouraging developers and startups to work towards making AI smarter. The availability of software platforms is changing the way in which businesses develop AI, encouraging them to follow in the footsteps of Google, Facebook, and OpenAI’s by being more transparent about their research.
The shift toward open machine learning platforms is an important phase in ensuring that AI works for everyone, instead of just a handful of tech giants.
From my perspective, there are three reasons for tech giants to release open-source machine learning projects:
When a startup releases an open-source project, it triggers awareness, some of which gets converted into paid customers and recruitment. Startups, by their very definition, are trying to get a foothold in a specific market instead of growing an existing market. Open-source is frictionless. It costs nothing to serve another organic user and enable organizations to solve real problems, thus allowing the code to have a greater impact.
Instead of disrupting the startups that build proprietary technologies, open-source has given the world a taller pair of shoulders to stand on. One of the knock-on effects may be a shift in focus on where the value lies. With the commoditization of the entire AI technology stack, the focus shifts from core machine learning technologies to building the best models–and this requires a vast amount of data and domain experts to create and train the models. Large incumbent businesses with an existing network effect have a natural advantage.
There is a wide range of open source machine learning frameworks available in the market, which enable machine learning engineers to:
Some of the important frameworks include:
Shogun is designed for unified large-scale learning for a broad range of feature types and learning settings, like classification, regression, dimensionality reduction, clustering, etc. It contains several exclusive state-of-the-art algorithms, such as a wealth of efficient SVM implementations, multiple kernel learning, kernel hypothesis testing, Krylov methods, etc.
Machine learning can indeed solve real scientific and technological problems with the help of open source tools. If machine learning is to solve real scientific and technological problems, the community needs to build on each other’s open source software tools. We believe that there is an urgent need for machine learning open source software, which will fulfill several concurrent roles, which include:
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