Open Source vs. Open Architecture - Helping Government Understand and Choose

The terms open-source AI and open architecture AI refer to different aspects of how AI systems are designed and shared. This article attempts to explain the differences so government buyers can determine which best meets their business needs.

Open-source software (OSS) is software made available with its source code, allowing anyone to view, modify, and distribute it. This openness fosters collaboration, transparency, and flexibility.

Open architecture refers to the design philosophy of AI systems where the components are modular, interoperable, and designed to work with a variety of other systems. It focuses on flexibility and extensibility rather than just open access to the source code.

Key Differences

• Open-source AI focuses on free access to the code and components for broader use and modification.
• Open architecture AI focuses on design principles that prioritize modularity, flexibility, and integration, which may or may not include open-source elements.

In practice, the two can overlap—for instance, an open-source AI system could also be open architecture if it’s designed modularly.

Knowing What You're Buying Matters

Open-source AI refers to AI systems or models whose source code, datasets, or other components are freely available for anyone to use, modify, and distribute.

• The source code is typically shared under licenses (e.g., MIT, Apache, or GPL) that specify the terms for use and redistribution.
• Community-driven: Development is often collaborative, with contributions from developers around the world.
• Transparency: Users can inspect how the AI model works and verify its functionality.

Advantages

• Promotes innovation and collaboration.
• Enables independent verification and auditing.
• Reduces costs for organizations that can build on existing work.

Drawbacks

• Less control over how the technology is used (e.g., misuse by bad actors).
• Technical support and updates depend on the community or contributors.

Choose Open-Source AI when your agency requires full control over the system internal functions. Budget constraints can also make free solutions attractive. And, if the agency values a collaborative, community-driven approach.

Open architecture refers to the design philosophy of AI systems where the components are modular, interoperable, and designed to work with a variety of other systems. It focuses on flexibility and extensibility rather than just open access to the source code.

Key Features

• Modular design: The system is built in a way that allows adding, replacing, or upgrading components.
• Interoperability: Compatible with various platforms, frameworks, and third-party tools.
• Not necessarily open source: While open architecture encourages collaboration, it doesn’t require the source code to be publicly available.
• Example: An AI platform designed with APIs and plugins that integrate with other tools, such as NVIDIA’s AI architecture or Microsoft’s Azure AI ecosystem.

Advantages

• Flexibility for organizations to customize the system to their needs.
• Easier integration with existing workflows or tools.
• Encourages vendor-neutral ecosystems.

Drawbacks

• May still have proprietary components, limiting transparency.
• Requires technical expertise to integrate effectively.

Choose Open Architecture AI if your agency needs modularity and integration with other tools. This is the right choice if your agency prioritizes compatibility over access to the tool's source code.

Summing It Up

Transparency in AI solutions is critical for government use because it ensures accountability, builds public trust, and safeguards fairness and legality. Governments wield significant influence, so how they use AI can have widespread social, economic, and ethical implications. It could be your agency wants to combine proprietary and open components.

Often, the two approaches can complement each other. For example, you might use an open-source AI framework like PyTorch within an open architecture AI platform for a flexible, transparent, and scalable solution.

The good news is there are many proven AI solutions already in use in both the private and public sectors. Perhaps the best method of choosing involves speaking with counterparts dealing with similar goals, objectives and challenges, and learning from their experience using AI solutions that are transparent, collaborative, and innovation built upon modular, flexible and accessible technology.