  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basis; and published models can quickly become out of date without effort to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.
