Meta’s LLaMa 2 License and Legal Aspects of Open Source Licences in the AI Industry

Meta, formerly known as Facebook, has been making strides in the world of Artificial Intelligence (AI) with the release of its powerful LLaMa 2 model. However, there has been a significant controversy surrounding the labeling of LLaMa 2 as “open source.” The Open Source Initiative (OSI), a renowned authority on open-source software, has raised concerns about the accuracy of this claim. In this blog post, we will delve into the arguments presented by OSI and weigh all sides of the debate.

Defining Open Source and the OSI Perspective:

The OSI emphasizes the importance of adhering to the Open Source Definition (OSD), which outlines specific characteristics a license must possess to be considered open source. According to the OSD, an open-source license must not discriminate against users or restrict usage based on certain conditions (OSD points 5 and 6). Unfortunately, Meta’s LLaMa 2 license does not meet these standards as it imposes limitations on commercial use and certain purposes through the Acceptable Use Policy.

The Significance of Open Source:

Open-source licenses empower developers and users to utilize technology without being dependent on another party, promoting technological sovereignty. The ethos of open source lies in the idea that everyone can share and use the technology freely, regardless of their identity or background. However, the restrictions imposed by Meta’s LLaMa 2 license, especially the limitation on commercial use, undermine this ethos and raise questions about whether it can truly be considered open source.

Balancing Competitive Interests and Openness:

While OSI acknowledges Meta’s desire to limit LLaMa 2’s use for competitive purposes, it contends that such restrictions remove the license from the realm of true “Open Source.” The OSD explicitly forbids limitations on the field of use to accommodate unforeseen developments in the future. By prohibiting LLaMa 2’s use in certain areas, such as regulated substances or critical infrastructure, the license stifles potential benefits to society that might arise from these uses.

Addressing Practical Challenges:

The Meta policy’s requirement to “follow the law” may seem reasonable at first glance, but it poses practical challenges. The global legal landscape is diverse, with varying laws in different regions. This inconsistency could hinder developers from exploring the full potential of LLaMa 2, hindering innovation and progress.

Seeking Clarity in Open Source AI:

Open-source AI systems, including large language models, are relatively new human artifacts, similar to the emergence of software in the 1970s. The debate around LLaMa 2’s license highlights the need for a shared understanding of what open source means for AI technology. Achieving this understanding will foster greater collaboration and innovation in the AI community.

Meta’s LLaMa 2 license has sparked a discussion on the definition of open source in the context of AI systems. The OSI has pointed out valid concerns about the limitations imposed by the license, which diverge from the principles of openness and freedom associated with true open-source technology. It is essential for stakeholders in the AI domain to come together and establish a common understanding of open source in AI to ensure that the potential of these powerful technologies is harnessed for the greater good of society. As the AI landscape continues to evolve, striking the right balance between competitive interests and openness will be crucial in shaping the future of AI technology.

Our view on Open Source 

In light of the above , we believe that the definition of “Open Source” should adhere to the principles outlined by the Open Source Initiative (OSI) and the Open Source Definition (OSD). According to the OSD, an open-source license should grant users the following freedoms:

1. The freedom to use the software for any purpose without discrimination.
2. The freedom to study how the software works and access its source code.
3. The freedom to modify and customize the software to suit specific needs.
4. The freedom to distribute copies of the software to others.

These freedoms are designed to promote collaboration, transparency, and community-driven innovation. An open-source license should not impose restrictions on who can use the software, how it can be used, or for what purposes it can be utilized.

In the context of AI, open source should allow developers and researchers to access and leverage AI technologies without unnecessary limitations. While it is understandable for companies to want to protect their technology and prevent misuse, an open-source AI model should still be accessible to a broad audience, including commercial users, researchers, and the general public.

In essence, the definition of open source in the context of AI should be in line with the traditional principles of open source, promoting inclusivity, collaboration, and the free exchange of knowledge for the benefit of the wider community and society as a whole.

Further context: Understanding Open Source Models in Machine Learning: Pros, Cons, and Impact on AI Development

Open source has been an essential pillar of artificial intelligence, enabling collaboration, transparency, and accessibility in the machine learning (ML) community. As the demand for sophisticated AI models and frameworks grows, leading companies like Google and Microsoft have embraced open-source practices, releasing pre-trained ML models to the public.

Defining Open-Source Models

In the context of machine learning, open-source models are pre-trained machine learning algorithms accompanied by the underlying code. These models are made available to the public, facilitating both model inference and transfer learning. Complete with publicly accessible code and sometimes data, open-source models ensure full reproducibility and enable users to review, modify, and contribute to the solutions, aligning with the principles of open source.

Availability of Open-Source Models

Most open-source AI models are deep learning models, which benefit from extensive datasets and intricate architectures. Such models require significant time and hardware resources for training, making pre-trained open-source models highly valuable. For instance, models like YaLM 100B have been trained on massive datasets for extended periods using high-grade graphic cards.

Popular Platforms for Open-Source Models

Several platforms host collections of open-source deep learning models and code. Some prominent ones include:

1. Model Zoo: A comprehensive collection of deep learning code and models for various frameworks and applications.
2. TensorFlow Hub and PyTorch Hub: Framework-specific collections of open-source models.
3. Papers with Code: A repository for open-source machine learning algorithms, featuring state-of-the-art models.
4. Hugging Face: A rising star in open-source models, with a focus on natural language processing (NLP) applications.

Pros and Cons of Open-Source Machine Learning Models

Open-source models offer numerous benefits, contributing to their widespread adoption in AI applications:

1. Time and Cost Savings: Pre-trained models eliminate the expensive phase of data science workloads.
2. Quality: Open-source models are extensively tested and often achieve state-of-the-art performance.
3. Minimal Entry Requirements: Democratizes AI access, particularly for individuals and companies facing entry barriers.

However, open-source AI models also face criticism due to certain challenges:

1. Environmental Impact: The high computational requirements of training open-source models can be resource-intensive.
2. Lack of Regulation: Large web-scraped datasets used for training may lack clear ownership rights.
3. Lack of Comprehensive Testing: Open-source models may be released without complete testing, leading to unexpected or undesirable behaviors.

Productionizing Open-Source Machine Learning Models

As the AI community embraces open-source models, the focus shifts to productionizing these models effectively. This involves downloading the model library to the development and production environment, followed by two common paths:

1. Direct Use: Utilizing the pre-trained model for inference.
2. Transfer Learning: Extending the pre-trained model’s trainable parameters and layers, tuning it for specific proprietary data and use cases.

The philosophy of productionizing machine learning models using open-source principles is often referred to as “open-source model infrastructure.”Open-source models in machine learning have become a standard practice in the AI community, promoting collaboration, transparency, and innovation. While they offer numerous benefits, there are also challenges that need to be addressed. Striking a balance between accessibility and responsible usage of open-source models will be crucial as AI technology continues to advance and shape our future. By fostering open source practices, the AI community can build a more inclusive, efficient, and impactful AI ecosystem.

 Legal Issues Surrounding AI-Based Code Generators

AI-based code generators have emerged as powerful tools that leverage generative AI to assist code developers in auto-completing or suggesting code based on their inputs or tests. However, the adoption of such tools raises several potential legal issues that need to be carefully considered. This article will analyze three primary legal concerns associated with AI code generators and explore practical solutions to mitigate these challenges.

Legal Issues with  Training AI Models Using Open Source Code

AI code generators are typically trained on vast repositories of open source code, and a common misconception is that since open source code is freely available, there are no legal concerns. However, open source software comes with specific licenses, and compliance with these licenses is crucial.

Open source licenses grant users the freedom to copy, modify, and redistribute the open source code under certain conditions. Compliance obligations may include maintaining copyright notices, providing attribution, and including the license terms when redistributing the code. Some licenses may also require that any software derived from the open source code be licensed under the same open source license, and its source code must be made freely available.

Failure to comply with these obligations can lead to legal issues, such as breach of contract or copyright infringement claims. Specific factual circumstances, such as the method of training and use of the AI model, will determine the applicability of these legal concerns.

Use of AI Code Generator Output

Output from AI code generators may not automatically constitute copyright infringement, as open source licenses generally permit copying and redistribution of the code. However, if the code output does not satisfy the compliance obligations of the relevant open source license, it may lead to breach of contract claims. This breach could result in license termination and subsequent copyright infringement if the usage continues after termination.

Licensing Requirements for New Software Applications

If the code output from an AI code generator is governed by a restrictive open source license, using that code in a new software application may “taint” the entire application. This requires the application to be licensed under the same restrictive open source license and mandates the availability of its source code to recipients. Consequently, recipients can copy, modify, and redistribute the entire application without any charge.

AI-based code generators offer immense benefits in code development but come with potential legal challenges. Many companies have adopted cautious approaches by banning the use of AI code generators to avoid legal risks. However, it is possible to manage these issues effectively through the application of various known solutions.

Practical Solutions for Addressing Legal Concerns with AI Code Generators

AI-based code generators have revolutionized the software development process by automating code suggestions and completions. However, as with any advanced technology, legal challenges arise when dealing with open source code and licensing requirements. In this article, we will explore practical solutions to address these legal concerns and enable developers to safely use AI code generators without compromising on compliance.

Implementing Filters to Prevent the Output of Problematic Code

One effective solution to mitigate potential legal issues is to implement filters within AI code generators. These filters can be designed to recognize and flag code segments that may pose compliance risks. By analyzing the output and cross-referencing it with known open source licenses, filters can identify code snippets that might violate specific licensing conditions.

AI code generators can be programmed to prioritize suggestions that comply with the licensing terms and refrain from producing code that could result in tainting a software application. This approach minimizes the risk of inadvertently using code subject to restrictive open source licenses, reducing the possibility of infringement claims or license violations.

Employing Code Referencing Tools to Flag Issues

Code referencing tools offer another effective solution to address legal concerns. These tools can scan the generated code and compare it against vast databases of open source licenses and copyrighted code. By doing so, they can quickly identify any instances where proper attribution or compliance obligations are not met.

These tools provide developers with real-time feedback, allowing them to assess the licensing implications of the AI-generated code. Moreover, code referencing tools can highlight specific segments that may require additional attention, enabling developers to take necessary steps to ensure compliance before incorporating the code into their applications.

Utilizing Code Scanning Tools for Open Source Compliance

Code scanning tools play a crucial role in open source compliance by automatically analyzing source code to detect license violations and identify potential infringement risks. Integrating code scanning tools into the software development process ensures that developers can promptly identify and rectify any compliance issues related to AI-generated code.

These tools provide detailed reports on license information, dependencies, and any problematic areas, making it easier for developers to remain compliant with open source licenses. With code scanning tools, developers can confidently utilize AI-generated code while adhering to licensing requirements and avoiding unnecessary legal entanglements.

AI-based code generators hold great promise for revolutionizing the software development landscape. However, it is crucial to address the legal concerns associated with using open source code and maintaining compliance with licensing requirements. By implementing practical solutions such as filters to prevent problematic code output, code referencing tools to flag issues, and code scanning tools to assist with open source compliance, developers and companies can harness the power of AI code generators with confidence.

Incorporating these solutions into the software development process will ensure that AI-generated code is thoroughly vetted for compliance, reducing the risk of infringement claims, license violations, and the inadvertent release of proprietary software under open source licenses. As the AI industry continues to evolve, these proactive measures will become essential in facilitating seamless and legally compliant code development using AI code generators.

More context :Potential Solutions to Mitigate Open Source Legal Risks with AI Code Generators

1. Filters to Prevent the Output of Problematic Code

One effective solution to reduce legal risks is to implement filters within AI code generators. These filters can identify and suppress code suggestions that match public open source code available on platforms like GitHub. By enabling this filter, known open source code is filtered out, preventing developers from inadvertently using code that might require compliance obligations.

2. Code Referencing Tools to Flag Problematic Output

Code referencing tools play a vital role in helping developers assess the legality of AI-generated code. These tools can compare generated code with open-source training data and provide relevant information about the associated open-source project’s repository URL and license. Developers and legal counsel can then review and evaluate potential risks before incorporating the code into their applications.

3. Code Scanning Tools to Assist with Open Source Compliance

Utilizing code scanning tools can further ensure open source compliance. These tools automatically analyze code to detect any open source components, checking for compliance with licensing terms and obligations. By implementing code scanning, developers can verify that AI-generated code aligns with their open source policies and avoid any infringement or compliance issues.

Leading AI Code Generators and Their Solutions

1. Copilot

GitHub Copilot offers a filter to detect and suppress code suggestions that match public open source code on GitHub. This optional filter helps developers avoid potential legal risks associated with using known open source code. Additionally, Copilot provides a reference tool that links suggestions resembling open-source code to the relevant license and project URL, enabling developers to make informed decisions about their usage.

2. CodeWhisperer

CodeWhisperer, as a generative AI, creates code based on learned training data and context provided by developers. It can also flag or filter code suggestions similar to open-source training data, offering valuable insights to developers and legal teams about potential compliance obligations.

Independent Open Source Code Scanning

Companies can employ established open source code scanning tools, such as Black Duck, to ensure compliance with licensing terms. By scanning code output from AI code generators, developers can identify any open source components and address compliance requirements before releasing their software.

Incorporating Solutions into Corporate AI Policies

To leverage AI code generators while managing legal risks, companies should consider creating robust AI use policies. These policies may include:

• Permitting employee use of AI code generators with appropriate safeguards
• Whitelisting approved AI code generators with suitable features for compliance
• Requiring the use of business accounts with mandated filter settings
• Implementing open source code scans to verify compliance and identify potential risks

How can Josh and Mak International assist its clients in the Artificial Intelligence Industry?

AI-based code generators offer remarkable efficiency and productivity gains for developers, but they also bring legal complexities. By adopting responsible AI use policies and integrating practical solutions like filters, code referencing tools, and code scanning, companies can harness the potential of AI code generators while maintaining compliance with open source licenses. Developing comprehensive AI use and open source policies is essential to ensure smooth and legally compliant AI implementation within an organization. If you need assistance with developing these policies or addressing any AI-related legal issues, our team at Josh and Mak International is here to help. Reach out to us for expert guidance on navigating the ever-evolving landscape of AI technology and the law.

Legal Compliance Program for AI-Based Code Generators

 As more of our client organizations are  incorporating AI-based code generators into our development processes, it is crucial to establish a comprehensive legal compliance program to ensure responsible and legally compliant use of these powerful tools. The program outlined below has some suggestions to mitigate potential legal risks associated with open source code, licensing, and copyright issues while enabling our developers to leverage the advantages of AI code generators.

1. Policy Development: Develop a clear and concise AI Use Policy that outlines the organization’s stance on AI code generators. This policy should include guidelines for developers on using AI code generators responsibly and complying with open source licenses. The policy should also mandate adherence to the legal compliance program.
2. Employee Training: Conduct regular training sessions for all employees involved in using AI code generators. Training should focus on understanding open source licenses, recognizing potential legal issues, and familiarizing developers with the implemented solutions to address open source problems. Training sessions should also emphasize the importance of responsible AI use.
3. Whitelisting Approved AI Code Generators: Create a list of approved AI code generators with suitable features, such as filters and code referencing tools, to prevent the output of problematic code. Whitelisting helps ensure that developers use reliable and compliant AI tools, reducing potential legal risks.
4. Mandatory Filter Usage: For developers accessing AI code generators through business accounts, mandate the use of filters to prevent suggestions that match public open source code. This measure helps maintain compliance with open source licenses and avoids potential tainting of proprietary software.
5. Code Referencing and Review: Encourage developers to use code referencing tools provided by AI code generators. This enables them to assess suggestions that resemble open source code, identify the relevant license, and evaluate potential compliance obligations. Legal counsel should review any flagged code suggestions before adoption.
6. Independent Open Source Code Scanning: Implement regular open source code scanning to analyze code output from AI code generators. This scanning ensures compliance with open source licenses and helps identify any potential risks or non-compliant code. Results of code scans should be documented and reviewed by relevant stakeholders.
7. Updating Open Source Policies: Revise and update existing open source policies to address AI code generator issues explicitly. Incorporate guidelines on using AI-generated code in compliance with open source licenses. These updated policies should be aligned with the overall legal compliance program.
8. Legal Consultation and Support: Establish a channel for developers to seek legal consultation and support regarding open source compliance and AI code generator usage. Legal experts should be available to assist with any legal questions or concerns that may arise during the development process.

By implementing such a legal compliance program, organisations in the field of artificial intelligence can adopts AI-based code generators responsibly and in compliance with open source licenses. Such a program can empower our client developers to leverage AI technology while minimizing legal risks and reinforcing corporate commitment to ethical and lawful AI use. Regular review and updates to the compliance program will ensure that the organization stays abreast of evolving legal requirements and best practices in the AI industry.

FAQ: Is Chat GPT Open Source?

Many people arecurious about whether Chat GPT, the highly advanced AI chatbot developed by OpenAI, is open source?

Chat GPT, the remarkable AI chatbot powered by OpenAI’s language model GPT-3.5, has been making waves since its launch at the end of the previous year. Its astonishingly human-like responses and impressive capabilities have garnered significant attention. Regrettably, Chat GPT is not open source. OpenAI has chosen not to make the language model GPT-3.5, upon which the chatbot is built, available for public access or modification. This means that developers and users cannot access or alter the underlying source code of Chat GPT.

OpenAI’s Transition to “Capped-Profit” Organization: In the past, OpenAI operated as a non-profit organization, regularly releasing projects and code to the public. However, things have changed, and the company has made the transition to a “capped-profit” organization, retaining ownership of their infrastructure.

This shift in approach has sparked discussions within the AI community, with notable figures, such as co-founder and former board member Elon Musk, expressing their thoughts on the matter. Musk criticized the transformation, stating that OpenAI, once synonymous with being “Open,” has now become closed source and appears to be “effectively controlled by Microsoft.” OpenAI, on the other hand, defends its transition to a capped-profit organization. The change was necessary to enable increased investments and cover the substantial computing costs arising from Chat GPT’s immense popularity and usage.

Exploring Open-Source Alternatives to Chat GPT

Although Chat GPT and GPT-3.5 are not open source, there are viable open-source alternatives available for those seeking similar AI capabilities:

1. GPT-J: An open-source alternative to OpenAI’s GPT-3 language model, developed by EleutherAI in 2021. GPT-J contains 6 billion parameters, making it a powerful alternative.
2. GPT-NeoX: Another open-source language model released by EleutherAI, boasting an impressive 20 billion parameters, making it even more robust than GPT-J.

Chat GPT is indeed not open source, and developers looking to access or modify its source code directly may find it restrictive. However, the AI community has fostered open-source alternatives like GPT-J and GPT-NeoX, providing developers with exciting possibilities for creating and customizing advanced AI chatbots.Also, While Chat GPT’s availability might not be fully open, the ever-evolving landscape of open-source AI models ensures that the development and innovation in the field of AI remain accessible to a wider audience.