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Open-source artificial intelligence

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Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share.<a href="#cite_note-:1-1">[1]</a> These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development.<a href="#cite_note-:1-1">[1]</a> <a href="Free_and_open-source_software" title="Free and open-source software">Free and open-source software</a> (FOSS) licenses, such as the <a href="Apache_License" title="Apache License">Apache License</a>, <a href="MIT_License" title="MIT License">MIT License</a>, and <a href="GNU_General_Public_License" title="GNU General Public License">GNU General Public License</a>, outline the terms under which open-source artificial intelligence can be accessed, modified, and redistributed.<a href="#cite_note-2">[2]</a>

The open-source model provides widespread access to new AI technologies, allowing individuals and organizations of all sizes to participate in AI research and development.<a href="#cite_note-:7-3">[3]</a><a href="#cite_note-:8-4">[4]</a> This approach supports collaboration and allows for shared advancements within the field of artificial intelligence.<a href="#cite_note-:7-3">[3]</a><a href="#cite_note-:8-4">[4]</a> In contrast, closed-source artificial intelligence is proprietary, restricting access to the source code and internal components.<a href="#cite_note-:7-3">[3]</a> Only the owning company or organization can modify or distribute a closed-source artificial intelligence system, prioritizing control and protection of intellectual property over external contributions and transparency.<a href="#cite_note-:7-3">[3]</a><a href="#cite_note-5">[5]</a><a href="#cite_note-Solaiman_2023-6">[6]</a> Companies often develop closed products in an attempt to keep a competitive advantage in the marketplace.<a href="#cite_note-Solaiman_2023-6">[6]</a> However, some experts suggest that open-source AI tools may have a development advantage over closed-source products and have the potential to overtake them in the marketplace.<a href="#cite_note-Solaiman_2023-6">[6]</a><a href="#cite_note-:8-4">[4]</a>

Popular open-source artificial intelligence project categories include <a href="Large_language_models" class="mw-redirect" title="Large language models">large language models</a>, <a href="Machine_translation" title="Machine translation">machine translation</a> tools, and <a href="Chatbots" class="mw-redirect" title="Chatbots">chatbots</a>.<a href="#cite_note-Castelvecchi_2023-7">[7]</a> For <a href="Software_developers" class="mw-redirect" title="Software developers">software developers</a> to produce open-source artificial intelligence (AI) resources, they must trust the various other open-source software components they use in its development.<a href="#cite_note-Thummadi_2021-8">[8]</a><a href="#cite_note-9">[9]</a> Open-source AI software has been speculated to have potentially increased risk compared to closed-source AI as bad actors may remove safety protocols of public models as they wish.<a href="#cite_note-:8-4">[4]</a> Similarly, closed-source AI has also been speculated to have an increased risk compared to open-source AI due to issues of dependence, privacy, opaque algorithms, corporate control and limited availability while potentially slowing beneficial innovation.<a href="#cite_note-10.1038/d41586-023-03803-y-10">[10]</a><a href="#cite_note-10.1038/s41586-024-08141-1-11">[11]</a><a href="#cite_note-12">[12]</a>

There also is a debate about the openness of AI systems as openness is differentiated<a href="#cite_note-10.1145/3571884.3604316-13">[13]</a> – an article in <a href="Nature_(journal)" title="Nature (journal)">Nature</a> suggests that some systems presented as open, such as Meta's <a href="Llama_3" class="mw-redirect" title="Llama 3">Llama 3</a>, "offer little more than an API or the ability to download a model subject to distinctly non-open use restrictions". Such software has been criticized as "<a href="Openwashing" title="Openwashing">openwashing</a>"<a href="#cite_note-14">[14]</a> systems that are better understood as closed.<a href="#cite_note-10.1038/s41586-024-08141-1-11">[11]</a> There are some works and frameworks that assess the openness of AI systems<a href="#cite_note-:3-15">[15]</a><a href="#cite_note-10.1145/3571884.3604316-13">[13]</a> as well as a new definition by the <a href="Open_Source_Initiative" title="Open Source Initiative">Open Source Initiative</a> about what constitutes open source AI.<a href="#cite_note-16">[16]</a><a href="#cite_note-17">[17]</a><a href="#cite_note-18">[18]</a>

<div class="mw-heading mw-heading2"><h2 id="History">History</h2></div>

The history of open-source artificial intelligence (AI) is intertwined with both the development of AI technologies and the growth of the open-source software movement.<a href="#cite_note-19">[19]</a> Open-source AI has evolved significantly over the past few decades, with contributions from various academic institutions, research labs, tech companies, and independent developers.<a href="#cite_note-:0-20">[20]</a> This section explores the major milestones in the development of open-source AI, from its early days to its current state.

<div class="mw-heading mw-heading3"><h3 id="Early_development_of_AI_and_open-source_software">Early development of AI and open-source software</h3></div>

The concept of AI dates back to the mid-20th century, when computer scientists like <a href="Alan_Turing" title="Alan Turing">Alan Turing</a> and <a href="John_McCarthy_(computer_scientist)" title="John McCarthy (computer scientist)">John McCarthy</a> laid the groundwork for modern AI theories and algorithms.<a href="#cite_note-21">[21]</a> An early form of AI, the <a href="Natural_language_processing" title="Natural language processing">natural language processing</a> "doctor" <a href="ELIZA" title="ELIZA">ELIZA</a>, was re-implemented and shared in 1977 by Jeff Shrager as a BASIC program, and soon translated to many other languages. Early AI research focused on developing <a href="Symbolic_artificial_intelligence" title="Symbolic artificial intelligence">symbolic reasoning systems</a> and <a href="Rule-based_system" title="Rule-based system">rule-based expert systems</a>.<a href="#cite_note-22">[22]</a>

During this period, the idea of open-source software was beginning to take shape, with pioneers like <a href="Richard_Stallman" title="Richard Stallman">Richard Stallman</a> advocating for free software as a means to promote collaboration and innovation in programming.<a href="#cite_note-23">[23]</a> The <a href="Free_Software_Foundation" title="Free Software Foundation">Free Software Foundation</a>, founded in 1985 by Stallman, was one of the first major organizations to promote the idea of software that could be freely used, modified, and distributed. The ideas from this movement eventually influenced the development of open-source AI, as more developers began to see the potential benefits of open collaboration in software creation, including AI models and algorithms.<a href="#cite_note-:33-24">[24]</a><a href="#cite_note-:02-25">[25]</a>

<div class="mw-heading mw-heading3"><h3 id="Emergence_of_open-source_AI_(1990s-2000s)">Emergence of open-source AI (1990s-2000s)</h3></div>

In the 1990s, open-source software began to gain more traction as the internet facilitated collaboration across geographical boundaries.<a href="#cite_note-26">[26]</a> The rise of machine learning and statistical methods also led to the development of more practical AI tools. In 1993, the CMU Artificial Intelligence Repository was initiated, with a variety of openly shared software.

In the early 2000s open-source AI began to take off, with the release of more user-friendly foundational libraries and frameworks that were available for anyone to use and contribute to.<a href="#cite_note-27">[27]</a> One of the early open-source AI frameworks was <a href="OpenCV" title="OpenCV">OpenCV</a>, released in 2000 with a variety of traditional AI algorithms like <a href="Decision_tree_learning" title="Decision tree learning">decision trees</a>, <a href="K-nearest_neighbors_algorithm" title="K-nearest neighbors algorithm">k-Nearest Neighbors</a> (kNN), <a href="Naive_Bayes_classifier" title="Naive Bayes classifier">Naive Bayes</a> and <a href="Support_vector_machine" title="Support vector machine">Support Vector Machines</a> (SVM). In 2007, <a href="Scikit-learn" title="Scikit-learn">Scikit-learn</a> was released.<a href="#cite_note-28">[28]</a> It became one of the most widely used libraries for general-purpose machine learning due to its ease of use and robust functionality, providing implementations of common algorithms like regression, classification, and clustering.<a href="#cite_note-29">[29]</a><a href="#cite_note-30">[30]</a> Other open-source machine learning libraries such as <a href="Theano_(software)" title="Theano (software)">Theano</a> (2007) were released by tech companies and research labs, further cementing the growth of open-source AI.

<div class="mw-heading mw-heading3"><h3 id="Rise_of_open-source_AI_frameworks_(2010s)">Rise of open-source AI frameworks (2010s)</h3></div>

The 2010s marked a significant shift in the development of AI, driven by the advent of <a href="Deep_learning" title="Deep learning">deep learning</a> and neural networks.<a href="#cite_note-:43-31">[31]</a> Open-source deep learning frameworks such as <a href="Torch_(machine_learning)" title="Torch (machine learning)">Torch</a> (dating from 2002 but with the first open-source release coming with Torch7 in 2011), soon augmented by <a href="PyTorch" title="PyTorch">PyTorch</a> (developed by <a href="Meta_AI" title="Meta AI">Facebook's AI Research Lab</a>), and <a href="TensorFlow" title="TensorFlow">TensorFlow</a> (developed by <a href="Google_Brain" title="Google Brain">Google Brain</a>) revolutionized the AI landscape by making complex deep learning models more accessible.<a href="#cite_note-32">[32]</a><a href="#cite_note-33">[33]</a> These frameworks allowed researchers and developers to build and train sophisticated neural networks for tasks like image recognition, natural language processing (NLP), and autonomous driving.<a href="#cite_note-34">[34]</a><a href="#cite_note-35">[35]</a>

<div class="mw-heading mw-heading3"><h3 id="Open_models">Open models</h3></div>

Building on those frameworks, various groups began releasing highly capable <a href="Foundation_model" title="Foundation model">foundation models</a> that could be used for and adapted to multiple tasks. Some prominent examples were the image recognition model <a href="AlexNet" title="AlexNet">AlexNet</a> in 2012, and <a href="Word2vec" title="Word2vec">Word2vec</a> for <a href="Natural_language_processing" title="Natural language processing">natural language processing</a> by Google in 2013. These often involved a release of model weights which allowed others to use the model, but not all the code that was used to train the model, nor the data that the model was trained on. While it was possible to <a href="Fine-tuning_(deep_learning)" title="Fine-tuning (deep learning)">fine-tune</a> these models to adapt them to more specialized tasks, they were not fully open. As described in the <a href="The_Open_Source_Definition" title="The Open Source Definition">The Open Source Definition, a</a> fundamental principle of the open-source approach is to share everything needed to modify and build on what has been shared, including the source code in the preferred form in which a programmer would modify it. Furthermore there was rarely an explicit license for the use of the weights, and there were often restrictions on commercial use which meant even the model weights were not open in the sense used with open-source software.

But some new models did come out in fully open-source form, notably the 2014 release of <a href="GloVe" title="GloVe">GloVe</a>, a competitor to Word2vec, which released source code under an Apache 2.0 license, documented the datasets they trained on, and released the model weights under a <a href="Public_domain" title="Public domain">Public Domain</a> Dedication and License.<a href="#cite_note-36">[36]</a>

The practice of releasing model weights, but not the other necessary ingredients was also common when <a href="Large_language_model" title="Large language model">large language models</a> started coming out, like Google's <a href="BERT_(language_model)" title="BERT (language model)">BERT</a> (2018) for natural language processing and OpenAI's <a href="Generative_pre-trained_transformer" title="Generative pre-trained transformer">GPT</a> series (2018–present) for text generation. Nevertheless, these models demonstrated the potential for AI to revolutionize industries by improving understanding and generation of human language, sparking further interest in AI.

<div class="mw-heading mw-heading3"><h3 id="Standards_for_AI_system_openness">Standards for AI system openness</h3></div>

During early negotiations in 2021 and 2022 around AI legislation in Europe, proposals were made to avoid over-regulating open-source AI.<a href="#cite_note-37">[37]</a> Noting that some organizations were mis-applying the "open-source" label to their work, in 2022, the <a href="Open_Source_Initiative" title="Open Source Initiative">Open Source Initiative</a>, which originally came up with the widely accepted standard for open-source software in 1998, started working with experts on a definition of "open-source" that would fit the needs of AI software and models. The most controversial aspect relates to data access, since some models are trained on sensitive data which can't be released. In 2024, they finalized the Open Source AI Definition 1.0 (OSAID 1.0), with endorsements from over 20 organizations.<a href="#cite_note-38">[38]</a><a href="#cite_note-:20-39">[39]</a> It requires full release of the software for processing the data, training the model and making inferences from the model. For the data, it only requires "sufficiently detailed information about the data used to train the system so that a skilled person can build a substantially equivalent system".<a href="#cite_note-:20-39">[39]</a>

While the OSAID was in development, the <a href="Linux_Foundation" title="Linux Foundation">Linux Foundation</a> was also developing a rubric of components of an AI system, and published a draft Model Openness Framework (MOF).<a href="#cite_note-40">[40]</a> The MOF is a system for evaluating and classifying the completeness and openness of machine learning models. It included three classes of openness, from more open to less open: Class I: Open Science Model; Class II: Open Tooling Model, and Class III: Open Model.<a href="#cite_note-41">[41]</a><a href="#cite_note-42">[42]</a> The Linux Foundation participated in the OSAID development, and OSAID adopted the same rubric of components.<a href="#cite_note-43">[43]</a>

<div class="mw-heading mw-heading3"><h3 id="Open-source_generative_AI_(2020s–Present)">Open-source generative AI (2020s–Present)</h3></div>

The last release from OpenAI in the GPT series that was open-source was GPT-2. They originally planned to keep the source code of the model private citing concerns about malicious applications.<a href="#cite_note-:2-44">[44]</a> After they faced public backlash, however, they released the source code for GPT-2 to GitHub three months after its release.<a href="#cite_note-:2-44">[44]</a> Subsequent models from OpenAI including GPT-3 and GPT-4 were neither open-source nor open model. Prompts must be sent to the company via a web site or API to get responses from the proprietary models.<a href="#cite_note-45">[45]</a>

In 2022, <a href="EleutherAI" title="EleutherAI">EleutherAI</a> released GPT-NeoX-20B, a leading fully open-source AI model, having released the dataset they trained on, "<a href="The_Pile_(dataset)" title="The Pile (dataset)">The Pile</a>", in 2021. In 2023 they released Pythia, with a wide range of well-documented training options.

In 2024, the <a href="Allen_Institute_for_AI" title="Allen Institute for AI">Allen Institute for AI</a> released OLMo, an open-source 32B parameter LLM.

<div class="mw-heading mw-heading3"><h3 id="Open_Models_2">Open Models</h3></div>

The rise of <a href="Large_language_model" title="Large language model">large language models</a> (LLMs) and <a href="Generative_artificial_intelligence" title="Generative artificial intelligence">generative AI</a>, such as OpenAI's GPT-3 (2020), further propelled the demand for open AI systems.<a href="#cite_note-:03-46">[46]</a> These systems have been used in a variety of applications, including chatbots, content creation, and code generation, demonstrating the broad capabilities of AI systems.<a href="#cite_note-47">[47]</a> Many of the deployed systems are closer to the MOF Open Model category than open-source. <a href="Hugging_Face" title="Hugging Face">Hugging Face</a>, a company focused on NLP, became a hub for the development and distribution of state-of-the-art AI models, including RoBERTa, an analog to BERT that is close to open-source.<a href="#cite_note-48">[48]</a>

In 2024, Meta released a collection of large AI models, including <a href="Llama_(language_model)" title="Llama (language model)">Llama</a> 3.1 405B, comparable to the most advanced closed-source models.<a href="#cite_note-:6-49">[49]</a> The company claimed its approach to AI would be open-source, differing from other major tech companies.<a href="#cite_note-:6-49">[49]</a> The <a href="Open_Source_Initiative" title="Open Source Initiative">Open Source Initiative</a> and others nevertheless stated that Llama is not open-source despite Meta's claims, due to Llama's software license prohibiting it from being used for some purposes, and the lack of training code or clarity on the training data.<a href="#cite_note-50">[50]</a><a href="#cite_note-51">[51]</a><a href="#cite_note-CIO_Nov_2024-52">[52]</a> Starting December 2024 <a href="Lightricks" title="Lightricks">Lightricks</a> released its LTX Video models with a similar license, providing the model and weights while restricting commercial use.<a href="#cite_note-53">[53]</a><a href="#cite_note-54">[54]</a>

<a href="DeepSeek" title="DeepSeek">DeepSeek</a> released their highly capable V3 LLM in December of 2024, and their R1 <a href="Model-based_reasoning" title="Model-based reasoning">reasoning model</a> on January 20, 2025, both as open models under the MIT license.<a href="#cite_note-55">[55]</a>

<div class="mw-heading mw-heading2"><h2 id="Ethics">Ethics</h2></div>

In parallel with the development of AI models, there has been growing interest in ensuring ethical standards in AI development.<a href="#cite_note-:18-56">[56]</a><a href="#cite_note-:22-57">[57]</a> This includes addressing concerns such as bias, privacy, and the potential for misuse of AI systems.<a href="#cite_note-:18-56">[56]</a><a href="#cite_note-:22-57">[57]</a> As a result, frameworks for responsible AI development and the creation of guidelines for documenting ethical considerations, such as the Model Card concept introduced by Google, have gained popularity, though studies show the continued need for their adoption to avoid unintended negative outcomes.<a href="#cite_note-:52-58">[58]</a><a href="#cite_note-:63-59">[59]</a><a href="#cite_note-:72-60">[60]</a>

<div class="mw-heading mw-heading2"><h2 id="Frameworks">Frameworks</h2></div>

The LF AI & Data Foundation, a project under the <a href="Linux_Foundation" title="Linux Foundation">Linux Foundation</a>, has significantly influenced the open-source AI landscape by fostering collaboration and innovation, and supporting open-source projects.<a href="#cite_note-61">[61]</a> By providing a neutral platform, LF AI & Data unites developers, researchers, and organizations to build cutting-edge AI and data solutions, addressing critical technical challenges and promoting ethical AI development.<a href="#cite_note-62">[62]</a>

As of October 2024, the foundation comprised 77 member companies from North America, Europe, and Asia, and hosted 67 open-source software (OSS) projects contributed by a diverse array of organizations, including silicon valley giants such as <a href="Nvidia" title="Nvidia">Nvidia</a>, <a href="Amazon_(company)" title="Amazon (company)">Amazon</a>, <a href="Intel" title="Intel">Intel</a>, and <a href="Microsoft" title="Microsoft">Microsoft</a>.<a href="#cite_note-:4-63">[63]</a> Other large conglomerates like <a href="Alibaba_Group" title="Alibaba Group">Alibaba</a>, <a href="TikTok" title="TikTok">TikTok</a>, <a href="AT%26T" title="AT&T">AT&T</a>, and <a href="IBM" title="IBM">IBM</a> have also contributed.<a href="#cite_note-:4-63">[63]</a> Research organizations such as NYU, University of Michigan AI labs, Columbia University, Penn State are also associate members of the LF AI & Data Foundation.<a href="#cite_note-:4-63">[63]</a>

In September 2022, the PyTorch Foundation was established to oversee the widely used <a href="PyTorch" title="PyTorch">PyTorch</a> deep learning framework, which was donated by Meta.<a href="#cite_note-64">[64]</a> The foundation's mission is to drive the adoption of AI tools by fostering and sustaining an ecosystem of open-source, vendor-neutral projects integrated with PyTorch, and to democratize access to state-of-the-art tools, libraries, and other components, making these innovations accessible to everyone.<a href="#cite_note-:5-65">[65]</a>

The PyTorch Foundation also separates business and technical governance, with the PyTorch project maintaining its technical governance structure, while the foundation handles funding, hosting expenses, events, and management of assets such as the project's website, GitHub repository, and social media accounts, ensuring open community governance.<a href="#cite_note-:5-65">[65]</a> Upon its inception, the foundation formed a governing board comprising representatives from its initial members: <a href="AMD" title="AMD">AMD</a>, <a href="Amazon_Web_Services" title="Amazon Web Services">Amazon Web Services</a>, <a href="Google_Cloud_Platform" title="Google Cloud Platform">Google Cloud</a>, <a href="Hugging_Face" title="Hugging Face">Hugging Face</a>, IBM, Intel, Meta, Microsoft, and NVIDIA.<a href="#cite_note-:5-65">[65]</a>

<div class="mw-heading mw-heading2"><h2 id="Applications">Applications</h2></div>

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</style><div role="note" class="hatnote navigation-not-searchable">See also: <a href="Generative_AI" class="mw-redirect" title="Generative AI">Generative AI</a></div>

<div class="mw-heading mw-heading3"><h3 id="Machine_learning">Machine learning</h3></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Scikit-learn" title="Scikit-learn">Scikit-learn</a>, <a href="TensorFlow" title="TensorFlow">TensorFlow</a>, and <a href="PyTorch" title="PyTorch">PyTorch</a></div>

Open-source artificial intelligence has brought widespread accessibility to machine learning (ML) tools, enabling developers to implement and experiment with ML models across various industries. Sci-kit Learn, Tensorflow, and PyTorch are three of the most widely used open-source ML libraries, each contributing unique capabilities to the field.<a href="#cite_note-66">[66]</a> Sci-kit Learn is known for its robust toolkit, offering accessible functions for classification, regression, clustering, and dimensionality reduction.<a href="#cite_note-:9-67">[67]</a> This library simplifies the ML pipeline from data preprocessing to model evaluation, making it ideal for users with varying levels of expertise.<a href="#cite_note-:9-67">[67]</a> Tensorflow, initially developed by Google, supports large-scale ML models, especially in production environments requiring scalability, such as healthcare, finance, and retail.<a href="#cite_note-68">[68]</a> PyTorch, favored for its flexibility and ease of use, has been particularly popular in research and academia, supporting everything from basic ML models to advanced deep learning applications, and it is now widely used by the industry, too.<a href="#cite_note-69">[69]</a>

<div class="mw-heading mw-heading3"><h3 id="Natural_Language_Processing">Natural Language Processing</h3></div>

<div class="mw-heading mw-heading4"><h4 id="Large_language_models">Large language models</h4></div>

<div role="note" class="hatnote navigation-not-searchable">Further information: <a href="Large_language_model" title="Large language model">Large language model</a></div>Open-source AI has played a crucial role in developing and adopting of Large Language Models (LLMs), transforming text generation and comprehension capabilities. While proprietary models like OpenAI's GPT series have redefined what is possible in applications such as interactive dialogue systems and automated content creation, fully open-source models have also made significant strides. Google's BERT, for instance, is an open-source model widely used for tasks like entity recognition and language translation, establishing itself as a versatile tool in NLP.<a href="#cite_note-70">[70]</a> These open-source LLMs have democratized access to advanced language technologies, enabling developers to create applications such as personalized assistants, legal document analysis, and educational tools without relying on proprietary systems.<a href="#cite_note-71">[71]</a>

<div class="mw-heading mw-heading4"><h4 id="Machine_Translation">Machine Translation</h4></div>

<div role="note" class="hatnote navigation-not-searchable">Further information: <a href="Neural_machine_translation" title="Neural machine translation">Neural machine translation</a></div>

Open-source machine translation models have paved the way for multilingual support in applications across industries. Hugging Face's MarianMT is a prominent example, providing support for a wide range of language pairs, becoming a valuable tool for translation and global communication.<a href="#cite_note-72">[72]</a> Another notable model, OpenNMT, offers a comprehensive toolkit for building high-quality, customized translation models, which are used in both academic research and industries.<a href="#cite_note-73">[73]</a> Alongside these open-source models, open-source datasets such as the WMT (Workshop on Machine Translation) datasets, <a href="Europarl_Corpus" title="Europarl Corpus">Europarl Corpus</a>, and OPUS have played a critical role in advancing machine translation technology.<a href="#cite_note-:10-74">[74]</a><a href="#cite_note-75">[75]</a> These datasets provide diverse, high-quality parallel text corpora that enable developers to train and fine-tune models for specific languages and domains.<a href="#cite_note-:10-74">[74]</a>

<div class="mw-heading mw-heading3"><h3 id="Text-to-image_models">Text-to-image models</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Further information: <a href="Text-to-image_model" title="Text-to-image model">Text-to-image model</a></div>

<div class="mw-heading mw-heading3"><h3 id="Computer_vision_models">Computer vision models</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Further information: <a href="Computer_vision" title="Computer vision">Computer vision</a></div>Open-source AI has led to considerable advances in the field of computer vision, with libraries such as <a href="OpenCV" title="OpenCV">OpenCV</a> (Open Computer Vision Library) playing a pivotal role in the democratization of powerful image processing and recognition capabilities.<a href="#cite_note-:11-76">[76]</a> OpenCV provides a comprehensive set of functions that can support real-time computer vision applications, such as image recognition, motion tracking, and facial detection.<a href="#cite_note-:12-77">[77]</a> Originally developed by <a href="Intel" title="Intel">Intel</a>, OpenCV has become one of the most popular libraries for computer vision due to its versatility and extensive community support.<a href="#cite_note-:11-76">[76]</a><a href="#cite_note-:12-77">[77]</a> The library includes a range of pre-trained models and utilities for handling common tasks, making OpenCV into a valuable resource for both beginners and experts of the field. Beyond OpenCV, other open-source computer vision models like <a href="You_Only_Look_Once" title="You Only Look Once">YOLO</a> (You Only Look Once) and Detectron2 offer specialized frameworks for object detection, classification, and segmentation, contributing to advancements in applications like security, autonomous vehicles, and medical imaging.<a href="#cite_note-78">[78]</a><a href="#cite_note-79">[79]</a>

Unlike the previous generations of Computer Vision models, which process image data through convolutional layers, newer generations of computer vision models, referred to as <a href="Vision_transformer" title="Vision transformer">Vision Transformer</a> (ViT), rely on attention mechanisms similar to those found in the area of <a href="Natural_language_processing" title="Natural language processing">natural language processing</a>.<a href="#cite_note-:13-80">[80]</a> ViT models break down an image into smaller patches and apply self-attention to identify which areas of the image are most relevant, effectively capturing long-range dependencies within the data.<a href="#cite_note-:13-80">[80]</a> This shift from convolutional operations to attention mechanisms enables ViT models to achieve state-of-the-art accuracy in image classification and other tasks, pushing the boundaries of computer vision applications.<a href="#cite_note-81">[81]</a>

<div class="mw-heading mw-heading3"><h3 id="Robotics">Robotics</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Open-source_robotics" title="Open-source robotics">Open-source robotics</a></div>

Open-source artificial intelligence has made a notable impact in robotics by providing a flexible, scalable development environment for both academia and industry.<a href="#cite_note-:14-82">[82]</a> The <a href="Robot_Operating_System" title="Robot Operating System">Robot Operating System</a> (ROS) stands out as a leading open-source framework, offering tools, libraries, and standards essential for building robotics applications.<a href="#cite_note-83">[83]</a> ROS simplifies the development process, allowing developers to work across different hardware platforms and robotic architectures.<a href="#cite_note-:14-82">[82]</a> Furthermore, <a href="Gazebo_(simulator)" title="Gazebo (simulator)">Gazebo</a>, an open-source <a href="Robotics_simulator" title="Robotics simulator">robotic simulation software</a> often paired with ROS, enables developers to test and refine their robotic systems in a virtual environment before real-world deployment.<a href="#cite_note-84">[84]</a>

<div class="mw-heading mw-heading3"><h3 id="Healthcare">Healthcare</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Further information: <a href="Artificial_intelligence_in_healthcare" title="Artificial intelligence in healthcare">Artificial intelligence in healthcare</a></div>

In the <a href="Healthcare_industry" title="Healthcare industry">healthcare industry</a>, open-source AI has revolutionized <a href="Medical_diagnosis" title="Medical diagnosis">diagnostics</a>, <a href="Patient_care" class="mw-redirect" title="Patient care">patient care</a>, and <a href="Personalized_medicine" title="Personalized medicine">personalized treatment</a> options.<a href="#cite_note-:15-85">[85]</a> Open-source libraries like <a href="TensorFlow" title="TensorFlow">Tensorflow</a> and <a href="PyTorch" title="PyTorch">PyTorch</a> have been applied extensively in medical imaging for tasks such as <a href="Cancer_screening" title="Cancer screening">tumor detection</a>, improving the speed and accuracy of diagnostic processes.<a href="#cite_note-86">[86]</a><a href="#cite_note-:15-85">[85]</a> Additionally, OpenChem, an open-source library specifically geared toward chemistry and biology applications, enables the development of predictive models for <a href="Drug_discovery" title="Drug discovery">drug discovery</a>, helping researchers identify potential compounds for treatment.<a href="#cite_note-87">[87]</a> NLP models, adapted for analyzing <a href="Electronic_health_record" title="Electronic health record">electronic health records</a> (EHRs), have also become instrumental in healthcare.<a href="#cite_note-:16-88">[88]</a> By summarizing patient data, detecting patterns, and flagging potential issues, open-source AI has enhanced clinical decision-making and improved patient outcomes, demonstrating the transformative power of AI in medicine.<a href="#cite_note-:16-88">[88]</a>

<div class="mw-heading mw-heading3"><h3 id="Military">Military</h3></div>

<div role="note" class="hatnote navigation-not-searchable">See also: <a href="Artificial_intelligence_arms_race" title="Artificial intelligence arms race">Artificial intelligence arms race</a></div>

Open-source AI has become a critical component in military applications, highlighting both its potential and its risks. Meta's Llama models, which have been described as open-source by Meta, were adopted by U.S. defense contractors like <a href="Lockheed_Martin" title="Lockheed Martin">Lockheed Martin</a> and <a href="Oracle_Corporation" title="Oracle Corporation">Oracle</a> after unauthorized adaptations by Chinese researchers affiliated with the <a href="People's_Liberation_Army" title="People's Liberation Army">People's Liberation Army</a> (PLA) came to light.<a href="#cite_note-89">[89]</a><a href="#cite_note-:17-90">[90]</a> The <a href="Open_Source_Initiative" title="Open Source Initiative">Open Source Initiative</a> and others have contested Meta's use of the term open-source to describe Llama, due to Llama's license containing an <a href="Acceptable_use_policy" title="Acceptable use policy">acceptable use policy</a> that prohibits use cases including non-U.S. military use.<a href="#cite_note-CIO_Nov_2024-52">[52]</a> Chinese researchers used an earlier version of Llama to develop tools like ChatBIT, optimized for military intelligence and decision-making, prompting Meta to expand its partnerships with U.S. contractors to ensure the technology could be used strategically for national security.<a href="#cite_note-:17-90">[90]</a> These applications now include logistics, maintenance, and cybersecurity enhancements.<a href="#cite_note-:17-90">[90]</a>

<div class="mw-heading mw-heading2"><h2 id="Benefits">Benefits</h2></div>

The open-source movement has influenced the development of artificial intelligence, enabling the widespread adoption and collaboration that are key to its rapid evolution. By making AI tools freely available, open-source platforms empower individuals, research institutions, and companies to contribute, adapt, and innovate on top of existing technologies.

<div class="mw-heading mw-heading3"><h3 id="Democratizing_access">Democratizing access</h3></div>

Open-source AI democratizes access to cutting-edge tools, lowering entry barriers for individuals and smaller organizations that may lack resources.<a href="#cite_note-:82-91">[91]</a> By making these technologies freely available, open-source AI allows developers to innovate and create AI solutions that might have been otherwise inaccessible due to financial constraints, enabling independent developers and researchers, smaller organizations, and startups to utilize advanced AI models without the financial burden of proprietary software licenses.<a href="#cite_note-:82-91">[91]</a> This affordability encourages innovation in niche or specialized applications, as developers can modify existing models to meet unique needs.<a href="#cite_note-:82-91">[91]</a><a href="#cite_note-:92-92">[92]</a>

<div class="mw-heading mw-heading3"><h3 id="Collaboration_and_faster_advancements">Collaboration and faster advancements</h3></div>

By sharing code, data, and research findings, open-source AI enables collective problem-solving and innovation.<a href="#cite_note-:92-92">[92]</a> Large-scale collaborations, such as those seen in the development of frameworks like TensorFlow and PyTorch, have accelerated advancements in machine learning (ML) and deep learning.<a href="#cite_note-:43-31">[31]</a>

The open-source nature of these platforms also facilitates rapid iteration and improvement, as contributors from across the globe can propose modifications and enhancements to existing tools.<a href="#cite_note-:43-31">[31]</a><a href="#cite_note-:33-24">[24]</a> Beyond enhancements directly within ML and deep learning, this collaboration can lead to faster advancements in the products of AI, as shared knowledge and expertise are pooled together.<a href="#cite_note-:33-24">[24]</a><a href="#cite_note-:92-92">[92]</a>

<div class="mw-heading mw-heading3"><h3 id="Equitable_development">Equitable development</h3></div>

The openness of the development process encourages diverse contributions, making it possible for underrepresented groups to shape the future of AI. This inclusivity not only fosters a more equitable development environment but also helps to address biases that might otherwise be overlooked by larger, profit-driven corporations.<a href="#cite_note-arxiv.org-93">[93]</a> With contributions from a broad spectrum of perspectives, open-source AI has the potential to create more fair, accountable, and impactful technologies that better serve global communities.<a href="#cite_note-arxiv.org-93">[93]</a>

<div class="mw-heading mw-heading3"><h3 id="Transparency_and_obscurity">Transparency and obscurity</h3></div>

One key benefit of open-source AI is the increased transparency it offers compared to closed-source alternatives.<a href="#cite_note-94">[94]</a> With open-source models, the underlying algorithms and code are accessible for inspection, which promotes accountability and helps developers understand how a model reaches its conclusions.<a href="#cite_note-:3-15">[15]</a> Additionally, open-weight models, such as Llama and <a href="Stable_Diffusion" title="Stable Diffusion">Stable Diffusion</a>, allow developers to directly access model parameters, potentially facilitating the reduced bias and increased fairness in their applications.<a href="#cite_note-:3-15">[15]</a> This transparency can help create systems with human-readable outputs, or "explainable AI", which is a growingly key concern, especially in high-stakes applications such as healthcare, criminal justice, and finance, where the consequences of decisions made by AI systems can be significant (though may also pose certain risks, as mentioned in the Concerns section).<a href="#cite_note-:19-95">[95]</a>

<div class="mw-heading mw-heading3"><h3 id="Privacy_and_independence">Privacy and independence</h3></div>

A <a href="Nature_(journal)" title="Nature (journal)">Nature</a> editorial suggests medical care could become dependent on AI models that could be taken down at any time, are difficult to evaluate, and may threaten patient privacy.<a href="#cite_note-10.1038/d41586-023-03803-y-10">[10]</a> Its authors propose that health-care institutions, academic researchers, clinicians, patients and technology companies worldwide should collaborate to build open-source models for health care of which the underlying code and base models are easily accessible and can be fine-tuned freely with own data sets.<a href="#cite_note-10.1038/d41586-023-03803-y-10">[10]</a>

<div class="mw-heading mw-heading2"><h2 id="Concerns">Concerns</h2></div>

In parallel with its benefits, open-source AI brings with it important ethical and social implications, as well as quality and security concerns.

<div class="mw-heading mw-heading3"><h3 id="Quality_and_security">Quality and security</h3></div>

Open-sourced development of AI has been criticized by researchers for additional quality and security concerns beyond general concerns regarding <a href="AI_safety" title="AI safety">AI safety</a>.

Current open-source models underperform closed-source models on most tasks, but open-source models are improving faster to close the gap.<a href="#cite_note-96">[96]</a>

Open-source development of models has been deemed to have theoretical risks. Once a model is public, it cannot be rolled back or updated if serious security issues are detected.<a href="#cite_note-:8-4">[4]</a> For example, Open-source AI may allow <a href="Bioterrorism" title="Bioterrorism">bioterrorism</a> groups like <a href="Aum_Shinrikyo" title="Aum Shinrikyo">Aum Shinrikyo</a> to remove <a href="Fine-tuning_(deep_learning)" title="Fine-tuning (deep learning)">fine-tuning</a> and other safeguards of AI models to get AI to help develop more devastating terrorist schemes.<a href="#cite_note-97">[97]</a> The main barrier to developing real-world terrorist schemes lies in stringent restrictions on necessary materials and equipment.<a href="#cite_note-:8-4">[4]</a> Furthermore, the rapid pace of AI advancement makes it less appealing to use older models, which are more vulnerable to attacks but also less capable.<a href="#cite_note-:8-4">[4]</a>

In July 2024, the <a href="United_States" title="United States">United States</a> released a presidential report saying it did not find sufficient evidence to restrict revealing model weights.<a href="#cite_note-98">[98]</a>

<div class="mw-heading mw-heading3"><h3 id="Equity,_social,_and_ethical_implications">Equity, social, and ethical implications</h3></div>

There have been numerous cases of artificial intelligence leading to unintentionally biased products. Some notable examples include AI software predicting higher risk of future crime and recidivism for African-Americans when compared to white individuals, voice recognition models performing worse for non-native speakers, and facial-recognition models performing worse for women and darker-skinned individuals.<a href="#cite_note-:04-99">[99]</a><a href="#cite_note-arxiv.org-93">[93]</a><a href="#cite_note-:102-100">[100]</a>

Researchers have also criticized open-source artificial intelligence for existing security and ethical concerns. An analysis of over 100,000 open-source models on <a href="Hugging_Face" title="Hugging Face">Hugging Face</a> and <a href="GitHub" title="GitHub">GitHub</a> using <a href="Vulnerability_scanner" title="Vulnerability scanner">code vulnerability scanners</a> like Bandit, FlawFinder, and <a href="Semgrep" title="Semgrep">Semgrep</a> found that over 30% of models have high-severity vulnerabilities.<a href="#cite_note-101">[101]</a> Furthermore, closed models typically have fewer safety risks than open-sourced models.<a href="#cite_note-:8-4">[4]</a> The freedom to augment open-source models has led to developers releasing models without ethical guidelines, such as <a href="GPT4-Chan" title="GPT4-Chan">GPT4-Chan</a>.<a href="#cite_note-:8-4">[4]</a>

With AI systems increasingly employed into critical frameworks of society such as law enforcement and healthcare, there is a growing focus on preventing biased and unethical outcomes through guidelines, development frameworks, and regulations. Open-source AI has the potential to both exacerbate and mitigate bias, fairness, and equity, depending on its use.

<div class="mw-heading mw-heading2"><h2 id="Improving_AI_models">Improving AI models</h2></div>

While AI suffers from a lack of centralized guidelines for ethical development, frameworks for addressing the concerns regarding AI systems are emerging. These frameworks, often products of independent studies and interdisciplinary collaborations, are frequently adapted and shared across platforms like GitHub and Hugging Face to encourage community-driven enhancements.

<div class="mw-heading mw-heading3"><h3 id="Common_development_malpractices">Common development malpractices</h3></div>

<div class="mw-heading mw-heading4"><h4 id="Data_quality">Data quality</h4></div>

There are numerous systemic problems that may contribute to inequitable and biased AI outcomes, stemming from causes such as biased data, flaws in model creation, and failing to recognize or plan for the possibility of these outcomes.<a href="#cite_note-:73-102">[102]</a> As highlighted in research, poor data quality—such as the underrepresentation of specific demographic groups in datasets—and biases introduced during data curation lead to skewed model outputs.<a href="#cite_note-:102-100">[100]</a>

A study of open-source AI projects revealed a failure to scrutinize for data quality, with less than 28% of projects including data quality concerns in their documentation.<a href="#cite_note-:73-102">[102]</a> This study also showed a broader concern that developers do not place enough emphasis on the ethical implications of their models, and even when developers do take ethical implications into consideration, these considerations overemphasize certain metrics (behavior of models) and overlook others (data quality and risk-mitigation steps).<a href="#cite_note-:73-102">[102]</a> These issues are compounded by AI documentation practices, which often lack actionable guidance and only briefly outline ethical risks without providing concrete solutions.

<div class="mw-heading mw-heading4"><h4 id="Transparency_and_"black_boxes"">Transparency and "black boxes"</h4></div>

Another key flaw notable in many of the systems shown to have biased outcomes is their lack of transparency.<a href="#cite_note-:102-100">[100]</a> Many open-source AI models operate as "black boxes", where their decision-making process is not easily understood, even by their creators.<a href="#cite_note-:102-100">[100]</a> This lack of interpretability can hinder accountability, making it difficult to identify why a model made a particular decision or to ensure it operates fairly across diverse groups.<a href="#cite_note-:102-100">[100]</a>

Furthermore, when AI models are closed-source (proprietary), this can facilitate biased systems slipping through the cracks, as was the case for numerous widely adopted facial recognition systems.<a href="#cite_note-:102-100">[100]</a> These hidden biases can persist when those proprietary systems fail to publicize anything about the decision process which could help reveal those biases, such as confidence intervals for decisions made by AI.<a href="#cite_note-:102-100">[100]</a> Especially for systems like those used in healthcare, being able to see and understand systems' reasoning or getting "an [accurate] explanation" of how an answer was obtained is "crucial for ensuring trust and transparency".<a href="#cite_note-xAI-103">[103]</a>

<div class="mw-heading mw-heading3"><h3 id="Frameworks_for_improvement">Frameworks for improvement</h3></div>

Efforts to counteract these challenges have resulted in the creation of structured documentation frameworks that guide the ethical development and deployment of AI:

<ul><li>Model Cards: Introduced in a Google research paper, these documents provide transparency about an AI model's intended use, limitations, and performance metrics across different demographics.<a href="#cite_note-:53-104">[104]</a><a href="#cite_note-:63-59">[59]</a> They serve as a standardized tool to highlight ethical considerations and facilitate informed usage.<a href="#cite_note-:53-104">[104]</a><a href="#cite_note-:63-59">[59]</a> Though still relatively new, Google believes this framework will play a crucial role in helping increase AI transparency.<a href="#cite_note-:63-59">[59]</a></li>

<li>Measurement Modeling: This method combines qualitative and quantitative methods through a social sciences lens, providing a framework that helps developers check if an AI system is accurately measuring what it claims to measure. The framework focuses on two key concepts, examining test-retest reliability ("construct reliability") and whether a model measures what it aims to model ("construct validity"). Through these concepts, this model can help developers break down abstract ideas which can't be directly measured (like socioeconomic status) into specific, measurable components while checking for errors or mismatches that could lead to bias. By making these assumptions clear, this framework helps create AI systems that are more fair and reliable.<a href="#cite_note-:04-99">[99]</a></li>

<li>Datasheets for Datasets: This framework emphasizes documenting the motivation, composition, collection process, and recommended use cases of datasets.<a href="#cite_note-:112-105">[105]</a> By detailing the dataset's lifecycle, datasheets enable users to assess its appropriateness and limitations.<a href="#cite_note-:112-105">[105]</a></li>

<li>Opening up ChatGPT: tracking openness of instruction-tuned LLMs: A community-driven public resource that evaluates openness of text generation models .<a href="#cite_note-106">[106]</a></li>

<li>Model Openness Framework: This emerging approach includes principles for transparent AI development, focusing on the accessibility of both models and datasets to enable auditing and accountability.<a href="#cite_note-107">[107]</a></li>

<li>European Open Source AI Index: This index collects information on model openness, licensing, and EU regulation of generative AI systems and providers. It is a non-profit public resource hosted at <a href="Radboud_University_Nijmegen" title="Radboud University Nijmegen">Radboud University Nijmegen</a>, the <a href="Netherlands" title="Netherlands">Netherlands</a>.<a href="#cite_note-108">[108]</a></li></ul>

As AI use grows, increasing AI transparency and reducing model biases has become increasingly emphasized as a concern.<a href="#cite_note-:19-95">[95]</a> These frameworks can help empower developers and stakeholders to identify and mitigate bias, fostering fairness and inclusivity in AI systems.<a href="#cite_note-:04-99">[99]</a><a href="#cite_note-:19-95">[95]</a> Using these frameworks can help the open-source community create tools that are not only innovative but also equitable and ethical.

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<ul><li><a href="Explainable_artificial_intelligence" title="Explainable artificial intelligence">Explainable artificial intelligence</a></li>

<li><a href="Artificial_intelligence_in_Wikimedia_projects" title="Artificial intelligence in Wikimedia projects">Artificial intelligence in Wikimedia projects</a></li>

<li><a href="Lists_of_open-source_artificial_intelligence_software" title="Lists of open-source artificial intelligence software">Lists of open-source artificial intelligence software</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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<div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2></div>

<ul><li><a rel="nofollow" class="external text" href="https://www.kialo.com/is-keeping-ai-closed-source-safer-and-better-for-society-than-open-sourcing-ai-62470">Is keeping AI closed source safer and better for society than open sourcing AI?</a>, interactive <a href="Argument_map" title="Argument map">argument map</a> on <a href="Kialo" title="Kialo">Kialo</a></li>

<li><a rel="nofollow" class="external text" href="https://oceanofai.com/">(Ocean of AI) AI Community</a></li></ul></div><div><div class="zim-footer">

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