Let's Explain Open Source vs Closed Source AI

Hey folks,

Today we're diving into an AI topic that a lot of people are talking about currently and that is the difference between Open Source and Closed Source AI. We'll be using OpenAI and Mistral as examples to illustrate.

Open Source AI vs Closed Source AI

Open Source AI refers to the application of open source practices to the development of AI resources. This means that the code, models, and training data are made publicly available, often as open source software. This approach encourages innovation and allows for a wider range of applications and solutions.

On the other hand, Closed Source AI, also known as proprietary AI, refers to AI systems that are developed and controlled by a single organisation. These systems are designed to perform specific tasks or provide solutions within a limited scope. The AI models, training data, and underlying codebase are kept private, protecting the research and development of AI technologies.

Advantages and Disadvantages

Open Source AI

Open Source AI fosters a "maker" culture that gives companies greater freedom to innovate with AI, and to leverage the ideas of peers for embedding AI in the business. It allows for greater scrutiny of underlying code, models, and data, often resulting in improved explainability and security. Openness about data sources might protect enterprises against intellectual property and copyright infringement as the legal landscape evolves.

However, regulators concerned about giving political adversaries an advantage, for example, tend to oppose open AI. Also, open source AI, while not as fast as closed AI, requires trust in the various other open-source software components used in its development.

Closed Source AI

Closed AI tends to be faster and can be used via various cloud services. It offers precise results within its defined scope and can be better at specific tasks than open source counterparts. It also avoids legal issues surrounding open source systems and restrictions on reuse.

However, a closed approach to AI application development tends to stifle growth. It doesn't help create feedback loops that solve problems and improve products, foster development of much-needed AI talent, or inspire trust in AI models and predictions.

OpenAI and Mistral: Case Studies

OpenAI is a prime example of an open source AI project. They build generative models using deep learning, leveraging large amounts of data to train an AI system to perform tasks in text, image, and audio processing. Their models are advanced language processing tools that can generate, classify, and summarize text with high levels of coherence and accuracy.

Mistral AI, on the other hand, is an example of a hybrid approach. Their first Large Language Model (LLM), Mistral-7B-v0.1, is an AI algorithm trained on massive amounts of data that can generate coherent text and perform various natural language processing tasks. While the raw model weights are distributed with Bittorrent and on Hugging Face, the specifics of its model architecture and training data are kept private.

In conclusion, the choice between Open Source AI and Closed Source AI depends on the desired application and the level of flexibility required. While both approaches have their merits, the decision to use one over the other has become a topic of fierce debate. As we continue to explore the world of AI, it's crucial to understand these differences and their implications.

Hope you learned something today!

Best,

Tom