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Open-source artificial intelligence

From Wikipedia, the free encyclopedia

Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share.[1] 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.[1]

Free and Open-Source Software (FOSS) licenses, such as the Apache License, MIT License, and GNU General Public License, outline the terms under which open-source artificial intelligence can be accessed, modified, and redistributed.[2]

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.[3][4] This approach supports collaboration and allows for shared advancements within the field of artificial intelligence.[3][4]

In contrast, closed-source artificial intelligence is proprietary, restricting access to the source code and internal components.[3] 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.[3][5][6]

Companies often develop closed products in an attempt to keep a competitive advantage in the marketplace.[6] 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.[6][4]

Popular open-source artificial intelligence project categories include large language models, machine translation tools, and chatbots.[7]

For software developers to produce open-source artificial intelligence resources, they must trust the various other open-source software components they use in its development.[8][9]

Open-source artificial intelligence has been speculated to have potentially increased risk compared to closed-source artificial intelligence as bad actors may remove safety protocols of public models as they wish.[4]

History

[edit]

While artificial intelligence has been developed through public research for most of history, the need emerged to distinguish between open-source and closed-source AI when industry research took over artificial intelligence research.

OpenAI was founded as a non-profit seeking to advance AI research while promoting safety by making its research available to the public.[10] In 2019, the company set up a “capped profit” structure to start receiving investments.[10] According to the company, the source code of the subsequently released GPT -2 was initially kept private, due to more risks it posed as the models became more powerful.[10] After OpenAI faced public backlash, however, it released the source code to GitHub three months after its release.[10]

The LF AI & Data Foundation, originally established as the LF Deep Learning Foundation in March 2018, expanded its focus in May 2019 to include various AI subfields, prompting a rebranding to the LF AI Foundation.[11] In October 2020, it merged with ODPi, an organization dedicated to advancing a big data software ecosystem, leading to its current name, the LF AI & Data Foundation, to emphasize the critical role of data in AI research and development.[11]

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 Nvidia, Amazon, Intel, and Microsoft.[12] Other large conglomerates like Alibaba, Tiktok, AT&T, and IBM have also contributed.[12] Research organizations such as NYU, University of Michigan AI labs, Columbia University, Penn State are also associate members of the LF AI & Data Foundation.[12]

In September 2022, the PyTorch Foundation was established to oversee the widely-used PyTorch deep learning framework, which was donated by Meta.[13] 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.[14]

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.[14] Upon its inception, the foundation formed a governing board comprising representatives from its initial members: AMD, Amazon Web Services, Google Cloud, Hugging Face, IBM, Intel, Meta, Microsoft, and NVIDIA.[14]

In 2024, Meta released a collection of large AI models, including Llama 3.1 405B, comparable to the most advanced closed-source models.[15] The company claimed its approach to AI would be open-source, detracting from other major tech companies.[15] Controversially, Meta has been criticized for not being truly open-source.[16]

Applications

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Machine learning

[edit]
See also: Scikit-learn, TensorFlow, and PyTorch

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.[17] Sci-kit Learn is known for its robust toolkit, offering accessible functions for classification, regression, clustering, and dimensionality reduction.[18] This library simplifies the ML pipeline from data preprocessing to model evaluation, making it ideal for users with varying levels of expertise.[18] Tensorflow, initially developed by Google, supports large-scale ML models, especially in production environments requiring scalability, such as healthcare, finance, and retail.[19] 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.[20]

Natural Language Processing

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Large language models

[edit]
Further information: Large language model

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.[21] Meta’s LLAMA (Large Language Model Meta AI) represents another breakthrough in open-source LLMs, offering a platform specifically designed to support research and domain-specific applications.[22] 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.[23]

LLaMA
[edit]
Main article: LLaMA

LLaMA is a family of large language models released by Meta AI starting in February 2023.[24] Meta claims these models are open-source software, but the Open Source Initiative disputes this claim, arguing that "Meta's license for the LLaMa models and code does not meet this standard; specifically, it puts restrictions on commercial use for some users (paragraph 2) and also restricts the use of the model and software for certain purposes (the Acceptable Use Policy)."[25]

Comparison of open-source large language foundation models
Model Developer Parameter count Context window Licensing Ref.
LLaMA Meta AI 7B, 13B, 33B, 65B 2048 [24]
Llama 2 Meta AI 7B, 13B, 70B 4k Custom Meta license [26][27]
Llama 3.1 Meta AI 8B, 70B, 405B 128K Meta Llama 3 Community License [28][29][30]
Llama 3.2 Meta AI 1B to 405B Research-only [31]
Mistral 7B Mistral AI 7 billion 8k Apache 2.0 [32][33]
Mixtral 8x22B Mistral AI 8×22B Apache 2.0 [31]
GPT-J EleutherAI 6 billion 2048 Apache 2.0 [34]
GPT-NeoX EleutherAI 20B MIT License [31]
Pythia EleutherAI 70 million - 12 billion Apache 2.0 (Pythia-6.9B only) [35][36]
T5 Google AI 60 million to 11 billion Apache 2.0 [31]
Gemma 2 Google DeepMind 2B, 9B, 27B Apache 2.0 [31]
OLMo Allen Institute for AI Various Apache 2.0 [31]
BLOOM BigScience 176 billion OpenRAIL-M [31]
StarCoder2 BigCode Various Apache 2.0 [31]
Falcon Technology Innovation Institute 7B, 40B Apache 2.0 [31]
Jamba Series AI21 Labs Mini to Large Custom [31]
Sea-Lion AI Singapore 7B Custom [31]
Qwen Series Alibaba Group 7B Custom [31]
Dolly 2.0 Databricks 12B CC BY-SA 3.0 [31]
Granite Series IBM 3B, 8B Apache 2.0 [31]
Phi-3 Series Microsoft Mini to Medium MIT License [31]
NVLM 1.0 Family Nvidia 72B CC BY-SA 3.0 [31]
RakutenAI Series Rakuten 7B Custom [31]
Grok-1 xAI 314B Apache 2.0 [31]

Machine Translation

[edit]
Further information: Neural machine translation

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.[37] Another notable model, OpenNMT, offers a comprehensive toolkit for building high-quality, customized translation models, which are used in both academic research and industries.[38] Alongside these open-source models, open-source datasets such as the WMT (Workshop on Machine Translation) datasets, Europarl Corpus, and OPUS have played a critical role in advancing machine translation technology.[39][40] These datasets provide diverse, high-quality parallel text corpora that enable developers to train and fine-tune models for specific languages and domains.[39]

Text-to-image models

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Further information: Text-to-image model
Comparison of text-to-image models
Model Developer Parameter count Licensing Ref.
Stable Diffusion 3.5 Stability AI 2.5B to 8B OpenRAIL-M [31]
IF DeepFloyd 400M to 4.3B Custom [31]

Computer vision models

[edit]
Further information: Computer vision

Open-source AI has led to considerable advances in the field of computer vision, with libraries such as OpenCV (Open Computer Vision Library) playing a pivotal role in the democratization of powerful image processing and recognition capabilities.[41] OpenCV provides a comprehensive set of functions that can support real-time computer vision applications, such as image recognition, motion tracking, and facial detection.[42] Originally developed by Intel, OpenCV has become one of the most popular libraries for computer vision due to its versatility and extensive community support.[41][42] 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 YOLO (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.[43][44]

Unlike the previous generations of Computer Vision models, which process image data through convolutional layers, newer generations of computer vision models, referred to as Vision Transformer (ViT), rely on attention mechanisms similar to those found in the area of Natural Language Processing.[45] 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.[45] 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.[46]

Comparison of computer vision models
Model Developer Parameter count Licensing Ref.
SAM 2.1 Meta 38.9M to 224.4M Apache 2.0 [31]
DeepLab Google Not disclosed Apache 2.0 [31]
Florence Microsoft 0.23B, 0.77B MIT License [31]
CLIP OpenAI 400M MIT License [31]

Robotics

[edit]
Main article: Open-source robotics

Open-source artificial intelligence has made a notable impact in robotics by providing a flexible, scalable development environment for both academia and industry.[47] The Robot Operating System (ROS) stands out as a leading open-source framework, offering tools, libraries, and standards essential for building robotics applications.[48] ROS simplifies the development process, allowing developers to work across different hardware platforms and robotic architectures.[47] Furthermore, Gazebo, an open-source robotic simulation software often paired with ROS, enables developers to test and refine their robotic systems in a virtual environment before real-world deployment.[49]

Healthcare

[edit]
Further information: Artificial intelligence in healthcare

In the healthcare industry, open-source AI has revolutionized diagnostics, patient care, and personalized treatment options.[50] Open-source libraries like Tensorflow and PyTorch have been applied extensively in medical imaging for tasks such as tumor detection, improving the speed and accuracy of diagnostic processes.[51][50] Additionally, OpenChem, an open-source library specifically geared toward chemistry and biology applications, enables the development of predictive models for drug discovery, helping researchers identify potential compounds for treatment.[52] NLP models, adapted for analyzing electronic health records (EHRs), have also become instrumental in healthcare.[53] 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.[53]

Military

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See also: Artificial intelligence arms race

Open-source AI has become a critical component in military applications, highlighting both its potential and its risks. Meta’s Llama models, initially restricted from military use, were adopted by U.S. defense contractors like Lockheed Martin and Oracle after unauthorized adaptations by Chinese researchers affiliated with the People’s Liberation Army (PLA) came to light.[54][55] 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.[55] These applications now include logistics, maintenance, and cybersecurity enhancements.[55]

Concerns

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Open-sourced development of AI has been criticized by researchers for additional quality and security concerns beyond general concerns regarding AI Safety.

Current open-source models underperform closed-source models on most tasks, but open-source models are improving faster to close the gap.[56]

Researchers have criticized open-source artificial intelligence for existing security and ethical concerns. An analysis of over 100,000 open-source models on Hugging Face and GitHub using code vulnerability scanners like Bandit, FlawFinder, and Semgrep found that over 30% of models have high-severity vulnerabilities.[57] Furthermore, closed models typically have fewer safety risks than open-sourced models.[4] The freedom to augment open-source models has led to developers releasing models without ethical guidelines, such as GPT4-Chan.[4]

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.[4] For example, Open-source AI may allow bioterrorism groups like Aum Shinrikyo to remove fine-tuning and other safeguards of AI models to get AI to help develop more devastating terrorist schemes.[58] The main barrier to developing real-world terrorist schemes lies in stringent restrictions on necessary materials and equipment.[4] 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.[4]

In July 2024, the United States released a presidential report saying it did not find sufficient evidence to restrict revealing model weights.[59]

References

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