Navigating the Use of Public AI Models Without Compromising Your Domain Knowledge

As organizations increasingly leverage public AI models like GPT-3, Stable Diffusion, and others, the issue of domain-specific knowledge absorption becomes a critical concern. This article explores how to use such public models while ensuring that you do not inadvertently share your unique knowledge or data with the AI developers. Understanding the nuances of model training and ownership is crucial for maintaining control over your intellectual property.

The Nature of AI Training and Domain Knowledge

Domain knowledge is not something that can be simply "pushed" into an AI model through user input. The knowledge is absorbed during the model’s training phase. If you use a public AI model that was trained by someone else, you are processing your data through a model that has already incorporated knowledge from its training dataset. However, this does not mean your proprietary knowledge will necessarily be absorbed or retained by the model itself. The model is not "learning" your domain-specific insights in the traditional sense but rather processing your input through a lens shaped by its pre-existing training.

Training Your Model with Your Data

One way to ensure that your domain knowledge is preserved and utilized is by training your own model with your proprietary data. This approach can be significantly resource-intensive, involving substantial computer time and storage to run the training process. By doing so, you create a version of the model that is uniquely tailored to your specific domain and use case. However, it's important to note that when you use this model, it makes decisions and predictions based on the data and insights it has learned during the training phase. Once trained, you maintain full control over the model and its proprietary knowledge, as you provide the necessary resources for the model's development.

Collaborative Training Models

While the most common approach involves training models in-house, there is a theoretical possibility of collaborating with another entity. In such scenarios, you provide the training data while another entity builds and trains the neural network. The question of who ends up owning the trained model and who bears the cost would be governed by a legally binding contract between the two parties. To my knowledge, this practice is not widespread, but as AI technologies advance, it is an area that may become more feasible.

Sharing User Input for Future Training

Another layer of complexity arises when the input to the system is highly similar to the training data. For instance, a chatbot trained on vast amounts of text might start absorbing knowledge from user interactions. In such cases, companies might collect and incorporate user input into their training datasets for future model versions. This practice is not uncommon and can be a double-edged sword. While it enhances the model's performance and adaptability, it also raises concerns about data privacy and ownership.

Addressing Ethical and Legal Concerns

Another challenge lies in the ethical and legal use of training data. Many current AI systems are built using training datasets for which consent was not explicitly obtained from the original creators, copyright holders, or photographers. While organizations are becoming more mindful of these issues, there is a risk that similar oversight might be applied to user data. Ensuring that user data is handled ethically and transparently is paramount to maintaining trust and compliance with data protection regulations.

Conclusion

Using public AI models while safeguarding your domain knowledge requires a deep understanding of the training process and the legal and ethical implications involved. By training your own model with your proprietary data, or through collaborative efforts with trusted entities, you can create a model that respects your intellectual property while delivering results tailored to your specific needs. As the field of AI develops, it's essential to stay informed about best practices and regulations to ensure you protect your valuable knowledge and data.