DataBright: Towards a Global Exchange for Decentralized Data Ownership and Trusted Computation

Understand

It is safe to assume that, for the foreseeable future, machine learning, especially deep learning will remain both data- and computation-hungry.

• In this paper, we ask: Can we build a global exchange where everyone can contribute computation and data to train the next generation of machine learning applications? We present an early, but running prototype of DataBright, a system that turns the creation of training examples and the sharing of computation into an investment mechanism.
• Unlike most crowdsourcing platforms, where the contributor gets paid when they submit their data, DataBright pays dividends whenever a contributor's data or hardware is used by someone to train a machine learning model.
• The contributor becomes a shareholder in the dataset they created.