The significance of technology in the modern era has changed how we live and interact with the outside world, going beyond simple convenience. Technological advancements have enabled significant progress in various industries, resulting in improved communication, streamlined business processes, and game-changing solutions in healthcare and education. The rise of Open Source AI is a key element of this technological environment; it not only increases the influence of technology but also encourages cooperation and creativity. 

Open Source AI enables researchers and developers from all over the world to contribute to the advancement and democratisation of AI, speeding up development and guaranteeing accessibility. This synergy is driving a paradigm shift by fusing the power of AI with the capabilities of open-source principles, promising a future marked by transparency, inclusivity, and constant technological advancement.

What is open-source AI?

Artificial intelligence (AI) that is based on open source principles and permits researchers and developers to share and alter its source code is known as open source AI. This cooperative strategy encourages creativity, openness, and accessibility, allowing a varied community to contribute to the advancement and development of artificial intelligence technologies. Open-source AI frameworks and tools facilitate the democratisation of AI and accelerate its applications across a wide range of industries and domains by enabling users to modify and customise AI solutions to meet specific needs. 

How is open-source AI different from Proprietary Software?

Software that is available for anyone to view, alter, and share the source code is known as open-source AI. This implies that anyone can help the AI model develop and enhance its functionality. Because open-source AI models are frequently offered for free or at a reduced cost, a larger range of users can use them. 

Proprietary AI systems are created for private entity’s use, and their source code is not made available to the general public. This implies that the system’s operation and usage are solely within the control of the company that developed it. Although proprietary AI systems are frequently more costly than open-source AI models, they might provide better functionality and performance.

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5 features of open-source AI

Transparency: Open-source AI allows anyone to view and inspect the source code, which means that anyone can understand how the AI model works and how it is trained. This transparency can help to build trust in AI systems and reduce the risk of bias and discrimination.

Cooperation: A global community of contributors frequently develops open-source AI projects. AI model development can be sped up and improved with this kind of cooperation.

Affordability: A greater range of users, including individuals, startups, and small businesses, can access open-source AI models because they are frequently offered for free or at a reduced cost.

Customization: Users can modify open-source AI models to suit their unique requirements. Either the source code can be changed, or pre-existing tools and libraries can be used to accomplish this customization.

Reproducibility: All the data and code required to replicate the outcomes are usually included in open-source AI projects. Building trust in AI systems and conducting scientific research both benefit from this reproducibility.

Here is the list of 8 Open-Source AI Technologies

OpenAI

OpenAI is an artificial intelligence (AI) organisation based in the United States. With the stated goal of creating “safe and beneficial” artificial general intelligence—defined as “highly autonomous systems that outperform humans at most economically valuable work”—OpenAI conducts research on artificial intelligence. Comprising its for-profit subsidiary corporation OpenAI Global, LLC and its non-profit OpenAI, Inc. registered in Delaware. The original board members of OpenAI were Sam Altman and Elon Musk. Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, Jessica Livingston, John Schulman, Pamela Vagata, and Wojciech Zaremba founded the company in 2015. Microsoft invested $1 billion in OpenAI Global LLC in 2019 and another $10 billion in 2023.

OpenCV

It has a programming function for real-time computer vision that is primarily found in the OpenCV (Open Source Computer Vision Library) library. It was initially created by Intel and later supported by Itseez (which Intel later acquired) and Willow Garage. The cross-platform library is available under the Apache License 2 as free and open-source software. OpenCV has had GPU acceleration for real-time operations since 2011. Both OpenCV’s main interface and its programming language are written in C++, but it also maintains an extensive but less feature-rich older C interface. The C++ interface contains all of the most recent advancements and algorithms. MATLAB/Octave, Java, and Python have language bindings.

Tensorflow

A free and open-source software library for artificial intelligence and machine learning is called TensorFlow. While it can be applied to many different tasks, its main focus is on deep neural network training and inference. The Google Brain team created TensorFlow for use in internal research and production at Google. 2015 saw the initial release of the product under the Apache License 2.0. In September 2019, Google launched TensorFlow 2.0, an upgraded version of the program. Numerous programming languages, such as Python, JavaScript, C++, and Java, can be used with TensorFlow. This adaptability makes it suitable for a wide range of uses across numerous industries.

PyTorch

Based on the Torch library, PyTorch is a machine learning framework used for tasks like computer vision and natural language processing. Originally created by Meta AI, PyTorch is currently under the Linux Foundation’s purview. The software is available for free and is open-source, with a modified BSD licence. PyTorch has a C++ interface as well, but the Python interface is more sophisticated and the main focus of development. PyTorch is the foundation for many deep-learning applications, such as PyTorch Lightning, Tesla Autopilot, Pyro from Uber, Hugging Face’s Transformers, and Catalyst. PyTorch offers two features at a high level:

Tensor processing via graphics processing units (GPU) with significant acceleration, similar to NumPy

Deep neural networks constructed on an automatic differentiation system based on tape

Keras

An open-source package called Keras offers an artificial neural network Python interface. Keras serves as the TensorFlow library’s interface.  In order to make programming in the deep neural network field easier, Keras includes a multitude of implementations of widely used neural network building blocks, including layers, objectives, activation functions, optimizers, and a host of tools for working with image and text data. The code is stored on GitHub, and there are Slack channels and a GitHub issues page for community support.

Keras supports convolutional and recurrent neural networks in addition to standard neural networks. Other widely used utility layers such as batch normalisation, pooling, and dropout are supported.

Accord. NET;

A.NET framework for scientific computing is called Accord. NET. The Gnu Lesser Public License, version 2.1, governs the use of the project’s source code. The framework consists of a number of libraries that can be obtained via NuGet packages, executable installers, and source code. Numerical linear algebra, statistics, machine learning, artificial neural networks, machine learning, signal and image processing, and support libraries (such as graph plotting and visualisation) are the primary topics covered.

Detectorn2

The main applications of Detectron2, an open-source deep learning library, are instance segmentation and object detection. The Facebook AI Research (FAIR) team is behind its development, and it is based on the PyTorch framework. Because of its adaptable and modular architecture, Detectron2 makes it simple for researchers and developers to build and modify intricate object detection models. It is appropriate for a range of computer vision applications, such as object detection, instance segmentation, keypoint detection, and panoptic segmentation, since it offers a collection of cutting-edge algorithms and models. When it comes to advanced computer vision tasks and research projects, Detectron2 has gained popularity due to its user-friendly design, high-performance capabilities, and extensive documentation.

Theano

A Python library called Theano is used for numerical evaluation and symbolic computation. It works especially well for defining and working with expressions in mathematics, especially tensor-based ones. Code for the effective execution of these expressions on a range of hardware platforms, such as CPUs, GPUs, and TPUs, can also be generated with Theano. It was first created as a machine learning research tool, but a variety of users, including scientists, engineers, and academics, have since embraced it. Financial modelling, scientific computing, deep learning, and machine learning are just a few of the many uses for Theano.

Bottom Line

Conclusively, open-source AI is a  priceless resource that demonstrates how dynamic and revolutionary technology can be. Their contributions have emphasised the value of accessibility, creativity, and cooperative development in the artificial intelligence space. Through the promotion of a culture that values knowledge exchange and community involvement, they are essential in advancing technology. Open-source AI keeps advancing, sparking creativity, and reshaping the future of AI by promoting an inclusive, innovative, and community-engaged culture. It promises an AI-powered future where everyone can use AI to improve society.