Decentralized & Collaborative AI on Blockchain
This addresses the issue of data centralization and model obsolescence for users and developers in AI applications, though it appears incremental as it builds on existing blockchain and incentive mechanisms.
The paper tackles the problem of centralized AI by proposing a decentralized framework for collaborative dataset building and continuous model updates using blockchain smart contracts, resulting in a publicly shared model that is free for inference.
Machine learning has recently enabled large advances in artificial intelligence, but these tend to be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and re-train them. We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model. This model will be shared publicly on a blockchain where it can be free to use for inference. Ideal learning problems include scenarios where a model is used many times for similar input such as personal assistants, playing games, recommender systems, etc. In order to maintain the model's accuracy with respect to some test set we propose both financial and non-financial (gamified) incentive structures for providing good data. A free and open source implementation for the Ethereum blockchain is provided at https://github.com/microsoft/0xDeCA10B.