Over the past decade, open source software has become an integral part of the landscape in the rapidly evolving field of artificial intelligence (AI). Major technology companies like Google, Facebook, Microsoft as well as startups and academic institutions have embraced the open source model for developing and distributing AI software. This has led to the creation of a thriving ecosystem of open source AI projects and communities that are helping drive innovation in the field.
Some of the most widely used open source AI software projects today include TensorFlow, PyTorch, Keras, OpenCV, Scikit-Learn, Jupyter, Apache MXNet, Theano, and Caffe. These projects cover a diverse range of capabilities for developing and deploying machine learning models, including neural networks, computer vision, natural language processing and more.
TensorFlow, developed by the Google Brain team, has emerged as one of the most popular open source AI frameworks. It allows developers and researchers to carry out end-to-end machine learning workflow from data preprocessing to model training, evaluation and deployment. The fact that Google uses TensorFlow internally across a wide range of applications has helped establish it as a robust, production-ready platform.
Pytorch
PyTorch, originally developed by Facebook’s AI research group, is another leading open source library based on the Torch framework. It is designed for flexible and intuitive model building with Python while scaling efficiently across GPUs and distributed environments. The Python-first approach of PyTorch has made it popular among researchers and students.
Keras is a high-level API focused on fast prototyping and experimentation via user friendliness, modularity and extensibility. It provides a simplified interface running on top of TensorFlow, PyTorch or other backend engines. Keras has helped make state-of-the-art deep learning more accessible to a wide audience.
Besides these frameworks tailored for deep neural networks, projects like OpenCV, Scikit-Learn and Apache Spark MLlib provide open source capabilities for computer vision, conventional machine learning algorithms and distributed big data pipelines respectively. Jupyter Notebook is a hugely popular open source web application for interactive data analysis and scientific computing.
The vibrant open source ecosystem has helped lower the barriers for students, researchers, hobbyists and professionals to acquire AI skills and experiment with cutting edge techniques. The open and collaborative nature of development has also enabled steady improvements in the tools and capabilities available. For instance, TensorFlow 2.0 was a major evolution adding user-friendly high-level APIs like Keras on top of the core engine.
Faster Adoption of AI
For large tech companies, open sourcing internal tools allows them to tap into the expertise of a global developer community. It also helps extend the use of their platforms and create de facto standards. For example, TensorFlow and PyTorch have become ubiquitous in research publications and practical applications. Even cloud providers like Amazon AWS and Microsoft Azure now offer managed services for these open source frameworks.
The open source model offers several benefits compared to closed source proprietary development:
Greater transparency: The ability to inspect open source code promotes trust and accountability, as users can verify its integrity. This is especially important as AI is increasingly being applied in sensitive domains like healthcare, finance and transportation.
Faster innovation: The collaborative development process harnesses the skills and perspectives of a diverse global community. This creates a positive feedback cycle where more adoption leads to more contributions and improvements to the software.
Lower costs: Open source provides a shared resource that many can build upon rather than reinventing the wheel. It also lowers barriers for accessing cutting-edge technology.
Flexibility: Users have more control over open source tools to customize, extend or integrate as per their needs. This is in contrast with proprietary “black boxes” where users are locked-in to specific vendors or platforms.
The Open Source Community
Active user community: Open source projects foster communities of users who can share knowledge and provide user support. This creates a valuable resource especially for beginners.
As artificial intelligence is adopted across industries and impacts society more profoundly, there are growing calls for AI systems to be trustworthy, ethical and responsible. Initiatives like the AI for People and Society (AIPS) program by Intel and the Ethics and Governance of AI Fund by Google are supporting open source projects that advance socially beneficial AI. Academic institutions like MIT and UC Berkeley also host open source AI labs focusing on themes like explainable AI and AI for science.
Government agencies are also recognizing the value of open source AI. For instance, the US Defense Advanced Research Projects Agency (DARPA) funded the development of Seldon Core, an open source platform for deploying and monitoring machine learning models. Such initiatives help raise the standards for safety, reliability and transparency in AI systems built using open source components.
The open source culture also promotes inclusion and diversity by removing barriers to access. Programs like Google’s Women Techmakers actively try to get more women involved in open source. The decentralized nature of open source communities allows contributors from across the world to collaborate according to their merit and interest rather than factors like geography.
As companies strategize around AI adoption, open source is proving to be a smart approach compared to investing in proprietary systems. With ever-evolving tools and technologies, being locked into one vendor can make it challenging to take advantage of latest innovations. Building on flexible, interoperable open source components helps position organizations for agility and success as AI capabilities continue to rapidly advance.
The incredible pace of evolution in artificial intelligence over the past decade likely could not have happened without the collaborative open source model. Looking ahead, open source will continue to power key innovations and applications of AI across industries. The communities co-creating these AI building blocks will play a crucial role in turning advanced intelligent algorithms into practical solutions that benefit people and society worldwide.
