Here are a few suggestions:

Distributed training: Instead of training a model on a single server, break the training process into smaller parts and distribute them across multiple server rooms connected via the internet. This can reduce the computational load on any one machine and make the process more resilient to individual machine failure.

Federated learning: Use federated learning, a decentralized approach to training machine learning models, where each server room trains a local version of the model on its own data and periodically aggregates the updates with a central server to produce a global model. This minimizes the need to share sensitive data across different entities, as each server room retains control over its own data.

Blockchain-based consensus: Use blockchain technology to create a decentralized consensus mechanism for controlling access to training data and models. This can ensure that the right incentives are aligned for contributing to the training process and help prevent the abuse of sensitive information.

Decentralized data marketplace: Create a decentralized data marketplace where data owners can sell access to their data to AI developers, who can then use it to train models. This can incentivize data owners to contribute their data while also helping to minimize data privacy concerns.

Encrypted computation: Use encrypted computation techniques, such as homomorphic encryption, to perform machine learning computations on encrypted data. This can help protect sensitive information while still allowing models to be trained and run on decentralized servers.

These are just a few examples of how to create a decentralized system for training and running AI models. By distributing the computational load and using decentralized consensus mechanisms, it is possible to minimize externalities and reduce the risk of alignment and incentive issues.
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