Weights, Models, and Open-Source | Explained | CAI #28

Technical Concepts Made Easy

Open-source AI, open models, and open weights are related concepts that refer to different levels of accessibility and transparency in artificial intelligence systems. Here's a detailed explanation:

Open Weights:

This is the most basic level of openness in AI models. It means the numerical parameters (weights) that a model has learned during training are publicly available. Anyone with sufficient expertise can download and use these weights on their own computing infrastructure. This allows users to run the model without needing to train it from scratch, which is often expensive and time-consuming.

Open Model:

An open model goes a step further than just open weights. It typically includes:

1. The model's weights

2. The code used to run or fine-tune the model

3. Documentation on how the model was developed and evaluated

Open models fall on a spectrum of openness depending on how much information is provided. The more components and development details are available, the more "open" the model is considered.

Open-Source AI:

This is the most comprehensive level of openness. For an AI model to be truly open source, it should meet all the criteria for open-source software, which includes:

1. Freely available source code

2. Permission to modify and distribute the code

3. No restrictions on who can use it or for what purpose

4. Transparent development process

In the context of AI, true open-source models would also include:

1. The training data

2. Detailed information on data collection and processing

3. Model architecture and design choices

4. Evaluation methods and results

It's important to note that many models claiming to be "open source" may not meet all these criteria. For example, some models release their weights but restrict commercial use, which doesn't align with traditional open-source principles.

Benefits of openness in AI:

1. Drives innovation and competition

2. Enables scrutiny and auditing of models

3. Allows for fine-tuning and customization

4. Reduces costs for users

5. Increases transparency and trust

Challenges:

1. Potential misuse of powerful models

2. Difficulty in fully replicating large models due to resource constraints

3. Balancing openness with commercial interests

While "open" in AI often refers to the availability of model weights, true openness exists on a spectrum.

The more components and information are freely available, the more open and transparent the AI system is considered to be.

References:

https://cset.georgetown.edu/article/open-foundation-models-implications-of-contemporary-artificial-intelligence/

https://www.iguazio.com/glossary/open-source-model/

https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2024/07/open-weights-foundation-models

https://www.nature.com/articles/d41586-024-02012-5

https://www.techtarget.com/searchenterpriseai/feature/Attributes-of-open-vs-closed-AI-explained

https://opensource.org/blog/compelling-responses-to-ntias-ai-open-model-weights-rfc

https://www.euronews.com/next/2024/02/20/open-source-vs-closed-source-ai-whats-the-difference-and-why-does-it-matter

https://www.linkedin.com/pulse/gen-ai-open-source-vs-weights-whats-difference-andrew-c-madson-z6sbc