# What's the role of Numbers in AI?

Better data, better AI.

In the future we envision, human knowledge will play a crucial role in enhancing AI. The key challenge lies in constructing a long-term, authentically professional, and sustainable data system.

Data labelling is and will become a critical process in AI development, and presents three significant hurdles:

1. It's complex and resource-intensive.
2. Precise labelling demands more than just general knowledge; it requires specialized domain expertise.
3. The inherent biases in labels call for continuous improvement.

To tackle these issues, Numbers Protocol has introduced an open-source and decentralized network. This innovative platform allows professionals to maintain ownership of their data and provides incentives for them to supply structured provenance data as labels throughout the [assetization process](../numbers-protocol/defining-web3-assets/assetization-process).&#x20;

This strategy holds substantial promise for developing a sustainable, professional, and long-term system of data labelling - though it would be more suitable to be called "human guidance for AI".

