Closed source AI

Extracted Attributes

• Cost

  Higher (licensing fees, ongoing costs)

• Nature

  Proprietary system

• Control

  High (over the system)

• Features

  Often advanced and high-performing

• Prevalence

  Predominant approach in the West

• Ease of Use

  High (vendor support, infrastructure)

• Flexibility

  Limited

• Integration

  Streamlined with other proprietary software

• Primary Goal

  Maintain competitive advantage

• Transparency

  Low (limited insight into data handling, opaque algorithms)

• Vendor Support

  Dedicated and timely

• Customizability

  Limited

• Development Cycles

  Often rapid

• Source Code Access

  Restricted

• License Flexibility

  Potential for (avoids open-source complexities)

• Security Management

  Internal (can be better)

• Updates and Maintenance

  Regular

• Potential Risks/Concerns

  Dependence, privacy, opaque algorithms, corporate control, slowed innovation

• Internal Components Access

  Restricted

Timeline

• Debate about the openness of AI systems, including the distinction between truly open and 'open-washing' systems. (Source: Wikipedia)

  Ongoing

• US vs. China AI competition, with the West predominantly pursuing a closed-source AI approach. (Source: Document 66f0f31a-b1f2-4f2c-a6dc-b1eaf051cfeb)

  Ongoing

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 wider access to AI technology, allowing more individuals and organizations to participate in AI research and development. In contrast, closed-source artificial intelligence is proprietary, restricting access to the source code and internal components. 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. Some large language models are released as open-weight, which means that their trained parameters are publicly available, even if the training code and data aren't.

Web Search Results

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

  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. [...] Another great example is OpenAI’s CLIP, which is an open-source AI model that connects images and text to enable tasks like classification and zero-shot learning. ## What Are Closed-Source AI Models? Closed-source models are proprietary systems that keep their code confidential. By restricting access to underlying code, closed-source AI model developers ensure more control over the system. [...] Limited customization — Closed-source AI models have restrictions on modifying and adapting the model to fit specific needs, which leads to less tailored solutions. Higher costs — Licensing fees and ongoing costs add up, which often makes closed-source AI models less budget-friendly. As one of our clients put it, using closed-source AI models “adds up to a pretty big sum quickly when you’re getting loads of users.”

• Emerging AI: Open vs Closed Source - Center Forward

  The discussions surrounding closed and open AI approaches reveal a complex landscape of benefits and challenges. Closed AI systems offer advantages such as faster development cycles, ease of use, license flexibility, and increased control, making them appealing for enterprises focused on maintaining competitive advantages. Conversely, open AI fosters increased scrutiny, better recruitment, and a greater understanding of models while facilitating bias identification and accelerating scalability [...] ### The Debate Breakdown Some of the key benefits of closed AI to businesses and users include: Faster Development Cycles: + Closed AI systems often have rapid cycles that enhance security and performance. Ease of Use: + Vendors provide infrastructure and support services to facilitate quicker adoption by enterprise applications. Potential for License Flexibility: + Avoids legal complexities and restrictions associated with open-source systems. [...] It remains to be seen whether open- or closed-source generative AI will prevail or if both will continue to coexist, as seen in various other tech sectors. Organizations looking to adopt generative AI currently have access to both closed- and open-source models. Closed-source solutions often deliver higher performance and more user-friendly interfaces, albeit at a higher cost. In contrast, open-source models are typically offer more affordability and accountability. They can be deployed on

• The Rise of Closed Source AI Tool Integrations - EclipseSource

  The trend towards closed source AI tool integrations is a growing concern in the software development world. While AI has the potential to revolutionize how developers work, the lack of transparency, limited customizability, and restricted flexibility pose significant challenges. It is crucial to advocate for solutions that uphold the values of openness and transparency, ensuring that the tools we rely on empower rather than constrain the developer community. In response, the open-source [...] Many AI-powered development tools today are proprietary, and this closed nature extends beyond the underlying Large Language Models (LLMs) to the tool integrations themselves. This means that not only are the AI models often closed source, but the components that interface with these models (a.k.a. “agents”) are also proprietary. These black-box components operate within the IDE where the most sensitive intellectual property of companies often resides. The inability to inspect and understand [...] EclipseSource is at the forefront of technological innovation, ready to guide and support your AI initiatives. Our comprehensive services in AI integration are designed to provide the specialized know-how necessary to develop customized, AI-enhanced solutions that elevate your tools and IDEs to new levels of efficiency and innovation. Explore how we can assist in integrating AI into your tools with our AI technology services. Reach out to begin your AI integration project with us.

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

  1. Reliable vendor support: Closed-source AI vendors generally provide dedicated and timely support, regular updates, and maintenance services. [...] 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 [...] 2. Streamlined integrations and advanced features: Closed-source applications are often designed to seamlessly integrate with other proprietary software, minimizing compatibility and integration issues. Vendor investments in research and development also usually result in advanced features and enhanced performance compared to open-source alternatives.

• Understanding the Difference Between Open-Source and Closed ...

  Closed-source AI models are owned and managed by a company. You can use them through an interface or API, but you can’t change how they work behind the scenes. These models are designed to be easy and ready to use. Like choosing a pre-built bike—you can pick the color and type (road bike or mountain bike), but you can’t change the internal mechanics. For most people and businesses, this is perfect because it works right out of the box. [...] Choosing between open-source and closed-source AI models comes down to what you need and what you're comfortable managing. Open-source models offer the freedom to customize and adapt the AI to your exact needs, but they require more technical expertise. On the other hand, closed-source models are ready to use, with little setup required, but you sacrifice some flexibility.