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Open source AI is defined as artificial intelligence technology that is available for commercial and non-commercial use under an open source license. Open source AI includes datasets, pre-built algorithms, and ready-to-use interfaces. Up from 48% in 2021, 65% of enterprises are expected to start using open source AI technology in the next two years. This article lists 10 great open source software to consider for your next AI project.
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Open source AI is an artificial intelligence technology that is available for commercial and non-commercial use under various open source licenses. Open source includes a variety of technologies that benefit AI product groups, independent app developers, and enterprises.
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Open source AI differs from free AI applications – the underlying code is exposed to users and open to modification and implementation in situations other than those originally intended. Open source also provides a large active community where developers can both contribute and ask for help.
Today, open source artificial intelligence is a booming segment that benefits developers and businesses alike. Research Opens a New Window. While 48 percent of enterprises are currently using open source technologies for AI/ML, that number will rise to 65 percent by 2023.
As mentioned, AI software can come in all shapes and sizes, combining data, algorithms, and UI or all three. In this roundup (arranged alphabetically), we focus on the top ten open source AI algorithm libraries for building software applications that address AI use cases such as computer vision, object recognition, character recognition, and text-to-speech. and more.
Disclaimer: This is a compiled list based on publicly available information and includes websites that target medium to large businesses. Readers are advised to conduct their own final research to ensure the best fit for their unique organizational needs.
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Overview: Founded in 2019, Acumos is a relative newcomer to the open source AI software segment, but is backed by industry leaders AT&T and TechMahindra. The two companies have broken the mold of tech giants such as Microsoft, Google and Apple by creating open-source innovations that bring AI to commercial deployments. They introduced Acumos AI, a Linux-based design studio to help integrate other frameworks and develop cloud-based AI applications.
USP: Acumos AI’s main USP is its GUI design office. It simplifies the development process through visual programming and makes AI development smoother and more accessible. You can also use onboard tools to integrate with other frameworks like TensorFlow, H2O, etc.
Editorial Comment: Acumos is a fantastic open source option for those interested in making AI more accessible. It standardizes the infrastructure layer so you can develop and deploy AI applications faster, and it’s compatible with most major languages, including Java, Python, and R. The Acumos AI Platform powers the end-to-end lifecycle of AI/ML applications. Creating prototypes for market execution, enrichment and publication.
Overview: ClearML is the result of a recent redesign of Allegro AI, a provider of open source tools for data scientists and machine learning labs. Along with the redesign, ClearML announced free hosting plans, giving data scientists the freedom to manage their AI/ML experiences and orchestrate their workloads without investing in additional resources. ClearML can be used as an MLOps solution, ready to implement with just two lines of code.
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USP: ClearML is one of the open source AI software with premium support, defined SLAs and additional business add-ons like managed services. If you want the benefits of open source (vendor dependency, ecosystem compatibility, etc.), while also having a commercial vendor partner, ClearML is a great choice.
Editor’s note: ClearML has some amazing features that are rare in the open source segment. For example, you get 100GB of free storage, 3 partner workspaces, low integration development, and support for pre-deployment even if you choose ClearML Free. At the other end of the spectrum, there is also a feature store for more advanced development. In other words, ClearML perfectly balances the simplicity of open source with the functionality of traditional trading platforms.
Overview: Founded in 2012, H2O has been at the forefront of open source AI innovation for nearly a decade. The company works with tech giants such as NVIDIA, IBM, Intel and Google to drive large-scale AI and ML products. The company was recently accredited by Singapore’s Infocomm Media Development Authority (IMDA), further cementing its global presence and allowing Singapore’s public sector access to H2O.
USP: H2O’s biggest USP is its AI hybrid cloud capabilities. That is, it is an end-to-end platform for preparing, modeling, operating, developing, and consuming artificial intelligence (in collaboration with others) in a centralized environment. Because it’s deployed with Kubernetes, you can run it on any cloud, even on-premise infrastructure.
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Editor’s note: H2O Enterprise is perfect for businesses just getting started with AI because you can start with an open source platform that trains your enterprise data. As more applications evolve, H2O supports your enterprise journey with training, reinforcement requests, automated ML, and more.
Overview: Mycroft is an open source voice assistant that you can run on any ecosystem. The company has won several awards over the years and is backed by strategic investments from major corporations such as Jaguar Land Rover. Essentially, Mycroft uses open source AI technology to power the various elements of the audio suite. There are many users, developers, and translators to continuously improve AI algorithms.
USP: Mycroft’s biggest USP is that it’s relatively easy to get started. It offers an AI-based voice option instead of commercial deployments like Alexa or Siri, which will inevitably mine some level of data. It is therefore involved in several public sector and philanthropic initiatives where data privacy is critical.
Editor’s Note: Unlike most open source AI software, Mycroft is strictly focused on use cases. However, if you have voice assistant requirements and want to choose open source, Mycroft is one of the most powerful options available.
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Overview: The Open Source Computer Vision Library, or OpenCV, is a rich library of artificial intelligence algorithms aimed at solving real-world computer vision problems. It was launched in 1999 as part of an Intel research project in the early days of artificial intelligence development. In 2012, it was taken over by a non-profit foundation that now manages the community, user support, and developer support. In 2020, the OpenCV AI Series was launched to raise funds for new hardware modules.
USP: OpenCV is one of the longest running and most tested open source AI software libraries in the industry. Since its inception, Intel has evolved from a C++ native computer vision library to a more widely accessible and ready-to-implement platform.
Editorial Comment: Companies looking to develop facial recognition systems, augmented reality apps, and the like using AI-based computer vision should consider OpenCV. Its rich algorithm library, combined with learning support and additional hardware, creates a 360-degree solution.
Overview: OpenNN is an open source AI software library for implementing neural networks and ML. Its primary use cases include customer intelligence and industry-specific analytics. The company that develops and maintains OpenNN is called Artelnics, which is known for its groundbreaking AI and big data research. Importantly, OpenNN, unlike the other open source software on this list, does not deal with computer vision or natural language processing.
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USP: OpenNN’s most important USP is its predictive ability. You can use this open source AI software to build applications for customer segmentation, early healthcare diagnosis, device prediction, and much more.
Editor’s note: Companies, teams, and independent developers looking for a pure-play open source software library (without commercial bells and whistles) should consider OpenNN, especially for use cases in predictive analytics. You can draw from his extensive collection of materials, which can also serve as a useful tutorial to get you started.
Overview: PyTorch ML improves on the underlying Torch framework using the Lua programming language. Facebook’s AI research lab launched PyTorch in 2016 under an open source license as a Python-based interface for AI/ML app development. There is a C++ interface to PyTorch. Today, PyTorch has grown into a rich ecosystem that provides all the tools needed to accelerate AI development from research to production.
USP: Python’s biggest USP is probably cloud-ready availability on Alibaba Cloud, Amazon Web Services, Google Cloud Platform and Microsoft Azure. This allows you to quickly download the software library from the relevant app market and get started without leaving your current cloud-based development environment.
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Editorial Comment: PyTorch is the most widely used open source AI software. Not only can you use it for computer vision, but you can also use PyTorch for voice processing, NLP, language translation, and more.
Overview: Rasa is one of the most popular open source AIs
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