Artificial Intelligence (AI), is a popular technology because people want to make their lives easier and more valuable. Smartphones are shifting their attention to Artificial Intelligence. Google, Amazon, Facebook, and other large companies are already actively working towards it. To accelerate AI research, Facebook created Torchnet, an open-source AI project. DeepMind Lab is a Google open-source AI program. Standford University recently concluded that AI ( report ) will have a huge impact on the future. Today we will be discussing a variety of open-source AI software that can help you build your AI projects.

Related post – The 5 Best Programming Languages for AI

What is Open Source Artificial Intelligence?

Open-source AI refers to artificial intelligence technology that is freely available for commercial and noncommercial use under various open licenses.

These include:

• Open-source datasets: AI software can be trained using data. In open-source AI, both the test and training data are free. These datasets are available even if you don’t use open-source AI software. They will help you make your models more reliable, and more accurate.
• Open-source algorithms: The algorithm and its core statistical model have been made freely available. They are typically available as open-source algorithm libraries. You can use them as is or train with enterprise information. Or, you can configure the code to make custom AI applications.
• Open Source UI: This open-source interface allows you to leverage open-source AI efficiently. These interfaces can be anything from simple command-line interfaces to complex GUIs. A UI overlay could be created that uses a different algorithm library and does not contain its own.

Open-source AI differs from freeware AI apps in that the underlying code can be exposed to the user, and is open for modification and implementation in other scenarios than those originally planned. There is a large community of developers who can contribute to the open-source project and receive help.

Open-source Artificial Intelligence is a growing segment that benefits both developers and businesses. Research indicates that 48% of businesses currently use open-source technology for AI/ML. This number will increase to 65% by 2023.

Top 10 Open Source AI Software

AI software comes in many shapes and sizes. It can span algorithms, datasets, and UI. This roundup, which is arranged alphabetically, focuses on the top ten AI algorithm libraries you can use to create software applications. It addresses common AI use cases such as computer vision, object recognition and character recognition, speech-to-text, speech recognition, and others.

1. Acumos AI

Overview Founded in 2019, Acumos was a new player in the open-source AI software market. However, it is supported by TechMahindra and AT&T. They wanted to break the trend of tech giants such as Apple, Google, and Microsoft leading open-source innovations and make AI accessible for commercial deployments. Acumos AI was created by the two companies to integrate other frameworks and create cloud-based AI apps.

The key features of Acumos AI are:

• Acumos Marketplace to find and deploy different AI libraries
• To enable interoperability, we offer onboarding support
• A graphic tool for managing AI models in preparation of a runtime environment
• A community that develops market solutions
• Dockerization supports AI in a container
• Microservices tools and API connectivity

Advantage A major USP for Acumos AI lies in its GUI design studio feature. This feature simplifies the development process by using visual programming. It also streamlines AI development making it easier. The onboarding tools can be used to allow interoperability between other frameworks such as TensorFlow and H2O.

Comments Acumos is an appealing open-source option for anyone interested in AI accessibility. It standardizes infrastructure so that you can quickly develop and deploy AI apps. Additionally, Acumos is compatible with all major languages including Java, Python and R.

2. ClearML

Overview ClearML is the result the recent rebranding by Allegro AI, which provides open-source tools for data scientists and machine learning labs. ClearML also announced a free hosted plan that gives data scientists the ability to run AI/ML experiments and to organize workloads without having to invest in additional resources. ClearML is available as an MLOps solution and can be implemented in just two lines.

The key features ClearML are:

• A system for experiment management that is easy to integrate
• In containers, experiment with orchestration (both development and production).
• Scheduling jobs through priority queues and resource allocation
• Remote allocating computing resources via a single command line
• Bayesian hyperparameter optimization can be run with zero integration
• Collaboration workspace with optional permission management

Advantage ClearML is one of the few open-source AI programs that offers optional commercial add-ons like priority support, well-defined SLAs and managed services. Open-source software is a great way to reap the benefits (no vendor dependence, cross-ecosystem compatibility etc.). ClearML, which is open source and has a commercial partner, is a great choice.

Comments ClearML offers a number of unique capabilities that are not common in the open-source segment. ClearML Free offers 100GB of free storage, 3collaborator workspace, low integration, development support, and on-site deployment. There’s also an option to store feature data for advanced development. ClearML is a perfect balance between the simplicity of open-source and the features of commercial platforms.

3. H2O.ai

Overview H2O was founded in 2012 and has been at forefront of open-source AI innovation for nearly a decade. H2O works with tech giants such as NVIDIA and Intel to develop large-scale AI/ML products. Recently, the Infocomm Development Authority (IMDA), Singapore granted the company accreditation. This enables Singapore’s public sector organizations to benefit from H2O and further cements its global presence.

The key features of H2O.ai include:

• Integration with Hadoop or Spark for big-data-based AI modeling
• A library of ML algorithms, including unsupervised and supervised learning
• Built-in intelligence for anticipating schemas in incoming datasets
• Support for data ingestion across multiple sources in diverse formats
• Driverless AI will help non-technical users prepare data and set parameters.
• Navigate through Flow for a simple web UI navigation

Advantage: H2O is known for its AI hybrid cloud capabilities. It is an end-to-end platform that allows you to prepare, model and operate AI in collaboration with other people. It can be deployed with Kubernetes and run on any cloud or on-premise infrastructure.

Comments H2O can be a great option for companies just starting with AI. It is an open-source platform that allows you to train on your company data. H2O will continue to develop new applications that can be used to support enterprises’ journeys in AI.

4. Mycroft.ai

Overview Mycroft is an open-source voice assistant that can be used in any environment. The company has received numerous awards and is supported by strategic investments from large corporations such as Jaguar Landrover. Mycroft is the open-source AI technology that powers different elements of the voice stack. To improve the AI algorithms, there is a large user community, developers and translators.

Key features Among the most important features of Mycroft.ai are:

• You can purchase a hardware shell with the voice assistant in three versions: Mark 1, Mark 2 and Mark 3.
• Releases for Android, Linux and Docker as well as macOS, Windows via a VirtualBox VM
• Modular architecture with interchangeable internal components
• Conversion of speech to text in partnership with Mozilla’s Common Voice Project (and DeepSpeech software)
• Intent parsing is the conversion of natural language into the machine-readable data structure
• Festival Lite speech synthesis software allows text to speech conversion

Advantage The greatest USP of Mycroft lies in its simplicity. It is a private AI-based voice replacement to commercial deployments such as Siri and Alexa, which will invariably mine data at some level. It has been involved with philanthropic and public sector initiatives that require data privacy.

Comments Mycroft is staunchly case-oriented, unlike other open-source AI software. Mycroft is an excellent choice if you need a voice assistant and prefer open source.

5. OpenCV

Overview The Open Source Computer Vision Library (or OpenCV) is an open-source library of AI algorithms that aims to solve real-time computer vision problems. It was created as part of an Intel research project in 1999. It was taken over in 2012 by a non-profit foundation. This foundation now manages the community, user support and developer assistance. OpenCV AI Kit was launched in 2020 to raise funds for new hardware modules.

The key features of OpenCV are:

• There are many proven applications, including facial recognition, human-computer interactions, object detection, movement tracking and many other use cases.
• ML library containing algorithms to learn decision tree, k-nearest algorithm, artificial neural network, random forest and deep neural network (DNN) among other things
• Compatible with all desktop ecosystems, including Android, iOS and Maemo as well as BlackBerry 10, BlackBerry 10, and BlackBerry 10.
• Courses on deep learning, computer vision and use cases that can be paid
• C++ is the primary language used, but wrappers can also be written in Java, Python, and other languages.
• Hardware store for spatial imaging cameras

Advantage OpenCV is one of the most trusted open-source AI software libraries in the industry. It has evolved from a C++-native computer vision library to an easily accessible platform that is ready for implementation since its inception by Intel.

Comments – OpenCV is a platform that allows companies to use AI-based computer vision to create facial recognition systems, augmented realities apps, and other similar applications. OpenCV’s rich library of algorithms and learning support, combined with complementary hardware, make it a 360-degree solution.

6. OpenNN

Overview OpenNN is an open-source AI software library that allows you to implement neural networks and ML. It’s main uses include customer intelligence, industry-specific analytics and their predictive applications. Artelnics is the company that developed and maintains OpenNN. It is well-known for its groundbreaking AI and big data research. OpenNN is not specialized in computer vision and natural language processing like some other open-source software.

The key features of OpenNN are:

• Software library based on C++
• Regression analysis to model the ML outputs
• Data classification is used to assign patterns
• Forecasting using historical data
• Two correlated variables can be mapped to form an association
• The neural designer tool simplifies the process of creating neural networks

Advantage: OpenNN has the most important USP, which is its ability to provide predictive insights. This open-source AI software can be used to create apps for customer segmentation and early healthcare diagnosis. It also allows for predictive maintenance of equipment.

Comments Companies, teams and individuals looking for an open-source AI software library that is free and without any commercial features should consider OpenNN, especially for predictive analytics. OpenNN’s rich documentation can be a great resource. It also serves as a tutorial.

7. PyTorch

Overview PyTorch builds upon the foundational torch framework to ML that uses Lua programming language. In 2016, the Facebook AI Research Lab launched PyTorch, a Python-based interface that allows AI/ML app developers to develop under an open-source license. PyTorch also has a C++ interface. PyTorch is a rich ecosystem today that allows you to accelerate AI development, from research to production.

Key features – Some of the most important features of PyTorch are:

• TorchServe provides a production-ready environment for rapid deployment of models
• A distributed backend architecture that allows for distributed training and performance optimization
• Algorithms to aid in computer vision and natural language processing
• All major public cloud for flexible development
• End-to-end workflow for app development from Python to iOS/Android
• Open Neural Network Exchange (ONNX), allows native exports

Advantage – Python’s biggest USP is its cloud availability on Amazon Web Services and Google Cloud Platform. You can quickly download the appropriate app marketplace software library and get started in your cloud-based development environment.

Comment PyTorch is the best open-source AI software, with the largest number of possible uses. PyTorch can be used for computer vision. However, it can also be used for audio processing, NLP and language translation.

8. Rasa Open Source

Overview Rasa is an open-source AI program that allows you to create conversational interfaces. Although the company’s main focus is on enterprise products, Rasa also offers an open-source edition and separate toolsets for improving AI assistance. Rasa can be used to create custom ML models, or you can leverage its TensorFlow-based library of models. Rasa Enterprise adds security and service level agreements to the open-source platform.

The key features of Rasa are:

• Natural language understanding allows you to convert messages into structured data and analyze intent
• Dialog management using ML to guide the assistant’s conversations based on context
• Integration for over 10+ messaging channels
• You have complete visibility into the AI training pipeline and model design.
• Strong community support by 10,000+ forum members
• Optional Rasa X toolset to test, enhance, and receive new updates

Advantage: Rasa is known for its ability to accelerate the development of chatbots and conversational assistants. Rasa X allows developers to fine-tune and provide updates easily without affecting the underlying AI/ML algorithm and code.

Comments. Large and mid-sized enterprises who want to create chatbots, or ISVs that are looking to add conversational capabilities to their software products should consider Rasa. Rasa allows for collaborative AI development on a large scale. You can also integrate it with Slack and Facebook.

9. TensorFlow

Overview – TensorFlow is a well-known open-source AI program. It was originally developed by the Google Brain Team and based on deep learning neural network technology. The company started using TensorFlow in many ways and decided to open-source TensorFlow starting in 2015. TensorFlow is the foundation of many popular open-source AI frameworks on the market today.

The key features of TensorFlow are:

• JavaScript is one of the few languages that are supported by open-source AI.
• High-level intuitive APIs such as Keras allows you to quickly build and train ML models.
• Production of ML is platform-agnostic – locally, on-premises, in the cloud, in the browser or in the cloud
• TensorFlow Lite is for mobile apps and embedded devices
• Cross-compatibility of AI/ML models you have trained using different TensorFlow versions
• There are many applications available, including predictive analysis and object classification.

Advantage: TensorFlow is a learning community that makes it unique. For those who are just starting with open-source AI/ML development, TensorFlow offers free tutorials, extensive learning courses and certifications. TensorFlow’s flexibility is another advantage. You can use it in any language and production environment.

Comments – While TensorFlow can only be used for mature applications that are expert-backed, it can also be used in a variety of business situations. TensorFlow is a good choice for companies that are looking to make long-term AI/ML investments, or whose core business model relies on analytics.

10. Tesseract OCR

Overview – Tesseract, an optical character recognition engine (OCR), was originally created by Hewlett Packard in the 1980s as a proprietary technology. It is widely known for being one of the best OCR engines and was released as an open-source AI software in 2006 with sponsorship by Google. It is primarily intended for text extraction and unstructured data processing.

The key features: Tesseract OCR includes the following:

• Written in C++ with a common line interface (no GUI).
• Capable of character classification, line finding, word-finding
• Precompiled binaries make it easy to install
• GUI overlays are available for application development, including OCRFeeder
• Text detection and localization within an image
• Non-C++ installations can use the Python wrapper

Advantage – The primary purpose of Tesseract OCR, is its effectiveness. Its primary purpose is to recognize text in unstructured visual environments and convert it into a human-readable format. Tesseract is capable of recognizing 100+ languages right out of the box. It is so powerful, that Google uses Tesseract to detect image spam in Gmail.

Comments – If you’re looking for an OCR engine that is capable of working in difficult conditions and can recognize languages like Arabic or Hebrew which follow right to left, Tesseract is the best choice. It is the most popular solution for language analysis and text detection. If the command-line interface does not meet your needs, you can choose to overlay it with a GUI.

Takeaway

Your choice of open-source AI technology is ultimately a matter of your specific software development needs. What use cases do you want to solve with AI? Is a GUI required or can a command-line interface suffice? What language is your code written in?

These top ten technologies promise support from a large developer community, frequent enhancements, and iterations. They have been tested in real-world situations. These technologies are ideal for strengthening the foundations of your next AI-related project. They also bring together collective intelligence from the global developer community and the best research labs around the world.