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:
It's complex and resource-intensive.
Precise labelling demands more than just general knowledge; it requires specialized domain expertise.
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.
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".