There are two routes in developing artificial intelligence systems, technologies, or applications. These are the open-source route and the closed-source or proprietary route. Closed-source AI is the go-to approach of profit-oriented organizations seeking to build and deploy AI algorithms or AI models and related tools either as products or as internal tech resources and part of their tech stack. However, due to the advancements in the different fields of artificial intelligence and their expanding practical application, open-source AI has been gaining prominence with communities of volunteers, non-profit organizations, and even for-profit organizations launching artificial intelligence projects that take advantage of open collaboration.
Open-Source AI vs Closed-Source AI Comparison: Understanding the Difference Between the Two Routes of Developing AI Tech
The general difference between open-source AI and closed-source AI is straightforward. The former has source codes that are available to the public while the source codes of the latter are private and remain an intellectual property or trade secret of its developer. The following are the more specific differences between the two:
1. Source Code
It is important to reiterate the fact that the source code of AI-related products built through an open-source approach is free and available to the public for inspection, testing, modification, and redistribution. The source code of products developed through a closed-source approach is private or inaccessible to the public to protect the intellectual property of its developer and prevent unauthorized use and reproduction.
2. Development Process
Another difference between open-source AI and closed-source AI is the specific facets of their respective development processes. To be specific, in an open-source approach, an organization or individual is responsible for providing a collaborative platform for contributors to develop algorithms, models, or end-use artificial intelligence applications. This is in contrast with a closed-source approach in which the development process is part of internal or outsourced research and development activities of an organization.
3. Updates and Support
Furthermore, as part of the product lifecycle, updates and support for an AI project built through an open-source approach come from the particular platform provider and the community of developers. This has notable advantages but can also have disadvantages stemming from the availability of resources and the long-term feasibility of the project. A closed-source project from an established developer provides more reliable updates and support.
4. Accessibility
Remember that the source code of open-source AI is available for modification and redistribution under a so-called open-source license. Projects tend to be accessible to the public for testing and practical application. The accessibility of closed-source AI, on the other hand, particularly those algorithms, models, or applications intended for the end-user market, is limited because its developer either sells them through a one-time-payment pricing scheme, a metered approach in pricing and distribution, or a subscription-based business model.
5. Security and Safety
The level of security and overall safety represents another difference between open-source AI and closed-source AI. The latter is considered more secure because the source code is private and there is a strong incentive for the developer to keep its data and its user data private. The former has security and safety issues. The availability of the source code for inspection, modification, and redistribution makes it more susceptible to misuse and abuse.
6. Performance
Features or capabilities are relative to the quality and commitment of developers. Access to relevant resources is also a determining factor of performance. There are AI models developed through open collaboration that tend to outperform proprietary AI models. There are also proprietary models that perform better than open-source models. Projects built by a larger community of contributors tend to provide decent features and capabilities. The same is true when it comes to AI products from established companies.
7. Implementation Requirements
Adapting or implementing an open-source AI product can be more suitable for individuals or small organizations with a smaller resource pool. It is important to note that utilizing this product may still require technical expertise. An AI product developed through a closed-source approach is more suitable for mid-sized to large-sized organizations that want a readily deployable solution and require enterprise-level features and support.
Summary of Key Points and Conclusion: A Recap on the Main Differences Between Open-Source AI and Closed-Source AI
Open-source AI and closed-source AI are two different approaches to developing and using artificial intelligence tech. The former involves source codes that are made available to the public and can be modified by anyone, while the latter involves source codes that are proprietary and controlled by a specific owner. Open-source AI offers lower costs, greater transparency, and more control, but may require more technical skills and pose ethical or privacy risks. Closed-source AI offers more support, resources, and security, but may be expensive, less transparent, and limited customization options. Choosing between open-source and closed-source approaches depends on the needs and goals of the individual or organization.
