Open Source vs Closed Source AI

Extracted Attributes

• Criticism

  Some systems labeled 'open' have significant use restrictions, leading to 'openwashing' accusations.

• Cost (Open-source AI)

  Typically free to acquire, but may involve costs for support or advanced features, and significant investment in setup/customization/maintenance.

• Cost (Closed-source AI)

  Involves licensing and access costs, higher up-front or subscription costs.

• Example Cost Comparison

  ChatGPT-4 (closed-source) ~$10-30 per million tokens; Llama-3-70-B (open-source) ~$0.60-0.70 per million tokens (approx. 10x cheaper).

• Definition (Open-source AI)

  AI system freely available to use, study, modify, and share, including datasets, code, and model parameters.

• Definition (Closed-source AI)

  Proprietary AI system with restricted access to source code and internal components, modifiable/distributable only by the owning company.

• Key Advantage (Open-source AI)

  Widespread access, collaboration, shared advancements, cost-effectiveness, flexibility, customization, transparency, community-driven development, ethical AI solutions.

• Key Advantage (Closed-source AI)

  Intellectual property protection, competitive advantage, professional support, ease of use, frequent updates, better internal security, proprietary advancements, reliability.

• Key Disadvantage (Open-source AI)

  Potential for bad actors to remove safety protocols, may have fewer updates or weaker data security, requires significant investment in setup/customization/maintenance, requires technical expertise.

• Governing Licenses (Open-source AI)

  Apache License, MIT License, GNU General Public License

• Key Disadvantage (Closed-source AI)

  Higher up-front/subscription costs, customization limitations, low transparency, issues of dependence, privacy, opaque algorithms, corporate control, potentially slower beneficial innovation.

• Common Applications (Open-source AI)

  Large language models, machine translation tools, chatbots.

Timeline

• DeepSeek, a China-based AI startup, released its powerful open-source R1 Model, which rivals proprietary technology from OpenAI, intensifying the Open Source vs Closed Source AI debate and highlighting AI Model Security concerns. (Source: Related Documents)

  Unknown (recent event)

Open-source artificial intelligence

Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. 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. 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. 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. This approach supports collaboration and allows for shared advancements within the field of artificial intelligence. In contrast, closed-source artificial intelligence is proprietary, restricting access to the source code and internal components. 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. Companies often develop closed products in an attempt to keep a competitive advantage in the marketplace. 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. Popular open-source artificial intelligence project categories include large language models, machine translation tools, and chatbots. For software developers to produce open-source artificial intelligence (AI) resources, they must trust the various other open-source software components they use in its development. 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. 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. There also is a debate about the openness of AI systems as openness is differentiated – an article in Nature suggests that some systems presented as open, such as Meta's Llama 3, "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 "openwashing" systems that are better understood as closed. There are some works and frameworks that assess the openness of AI systems as well as a new definition by the Open Source Initiative about what constitutes open source AI.

Web Search Results

• Open-Source vs. Closed-Source AI Applications in Higher Education

  Open-source AI applications, such as Llama by Meta or Mixtral by Mistral AI, have publicly accessible model architecture and weights that users can modify, customize, and distribute. Their open nature allows for innovation and community-driven development. In contrast, closed-source AI applications have private model architectures and weights, limiting customization and redistribution to select vendors or organizations. Users can often purchase closed-source versions of open-source applications [...] 1. Cost-effectiveness: Open-source software does not have licensing fees, reducing financial barriers and increasing accessibility for resource-constrained institutions. 2. Flexibility and customization: Open-source AI applications can be tailored to an institution’s specific needs, facilitating customization and scalability for more adaptable and versatile solutions. [...] 1. Cost: The benefits of the closed-source model come with higher up-front or subscription costs compared to open-source alternatives. 2. Customization limitations: Institutions may encounter restrictions on the extent to which they can tailor the model to their needs, including added features, integrations, or data sources. With most closed-source models, the vendor controls the level of customization.

• Open-Source AI vs. Closed-Source AI: What's the Difference?

  In such cases, open-source AI is a much better choice. It is more flexible, gives you more control, and makes more sense if you have a custom training dataset. Fine-tuning an open-source AI model is the best option for every organization with big goals that closed-source AI models might not help you achieve. We primarily fine-tune open-source AI models (like Llama 2) for our clients. We use closed-source models while we collect more data and configure a more suitable open-source AI. [...] Open AI models have publicly available code that allows anyone to access and modify the model. This approach provides better transparency and collaboration but can lead to fewer updates and weaker data security. While open-source AI models are typically free to use, keep in mind that there might be costs associated with support or advanced features of the model. [...] Closed-source AI models have a proprietary code that’s restricted to the developing organization, which limits the customizability and collaborative potential. Such an approach leads to low transparency with limited insight into data handling practices, but the updates are typically frequent, and security is slightly better since it’s managed internally. Closed-source AI models almost always involve licensing and access costs.

• Navigating The Generative AI Divide: Open-Source Vs. Closed ...

  Of course, budget considerations will often be a big factor in any decision. While open-source tools may be free to acquire, working with them could involve significant investment in setup, customization, user training and maintenance. Closed-source, while more expensive, will often include all of the professional support and assistance needed to get started off the shelf. This could make it more cost-effective in the long term for businesses without a large technical staff. [...] There are, though, advantages to this model for the end user. As commercial products, closed-source AI tools have to be accessible and easy to use; otherwise, vendors will have a hard time selling them. In theory, they’ll make them as user-friendly as possible and offer customer and technical support services. One reason that businesses will choose closed-source over open-source tools, despite the additional cost, is that they expect it to be reliably maintained and supported. [...] Before making a decision, it’s essential to evaluate the technical expertise in your business and the cost and local availability of third-party support. Open-source offers great potential for flexibility and customization, but businesses that lack the capability to deploy it might find closed-source tools to be a better fit.

• Open-Source LLMs vs Closed: Unbiased Guide for Innovative ...

  Such transparency strengthens the integrity of AI applications, making open-source LLMs a trusted choice for developers and businesses focused on ethical AI solutions. The Case for Closed Source LLMs ------------------------------- While open-source LLMs have community, collaboration, and transparency on its side, closed-source LLMs offer unique proprietary advancements and security. ### Proprietary Advancements [...] HatchWorks’ verdict: Within both open-sourced and closed source models, there’s a range of costs and accessibility. For example, ChatGPT-4 as a closed source model is about $10 per million token input and $30 per million token output while Llama-3-70-B, also an open-sourced model, is 60 cents per million token input and 70 cents per million token output. That’s about 10x cheaper with very little performance difference between them. [...] As LLMs have evolved, two types have emerged. Those are open-source models and closed source models. Open-source models are publicly available, allowing anyone to use, modify, and distribute the software. Closed-source models, on the other hand, are often proprietary models, with their source code bases accessible only to the organization that developed them or those willing to pay for access. The emphasis is on protecting intellectual property and monetizing the technology.

• Open Source AI vs. Proprietary AI: Pros and Cons for Developers

  In the Open Source AI vs. Proprietary AI debate, there is no one-size-fits-all answer. The choice between these two types of AI platforms depends on the specific needs of the project, the resources available, and the priorities of the development team. Open source AI offers unparalleled flexibility, customization, and community support, making it ideal for projects that require specialized solutions and a high degree of innovation. However, it also requires developers to take on greater [...] ## Open Source AI vs. Proprietary AI: Ease of Use and Commercial Support In contrast to open source AI, proprietary AI platforms are typically developed and maintained by commercial entities. These platforms, such as IBM Watson, Microsoft Azure AI, and Google Cloud AI, offer a different set of advantages, particularly in terms of ease of use and professional support. For developers who prioritize convenience and reliability, proprietary AI may be an attractive option. [...] One of the most significant advantages of open source AI is the level of flexibility and control it offers to developers. Open source AI frameworks and tools, such as TensorFlow, PyTorch, and OpenAI’s GPT models, are available to the public, allowing developers to modify and extend the code as needed. This freedom is crucial for developers who need to tailor AI models to specific use cases or who want to experiment with new ideas without being restricted by the limitations of proprietary