The development of AI is not only a technical issue but also a matter of social governance. How can different countries and institutions collaborate without sharing sensitive data? How can we ensure model fairness while protecting privacy?
Lagrange's Proofs of Training provide a solution. It allows multiple parties to jointly train models while generating an encrypted proof that certifies the legitimacy of the training process and the trustworthiness of the results. This offers a new model for multinational banking alliances, scientific collaborations, and government audits.
In the July engineering update, DeepProve-1 has already verified the reasoning of GPT-2, which means that the path to 'verifiable training' is gradually being realized. The standards for future AI governance will no longer rely on institutional commitments but will be based on mathematical proof.
The role of the $LA token cannot be ignored. It serves as both an economic incentive tool and a vehicle for network governance, ensuring the long-term stability of the verification system.
This is not just a technological advancement but a reconstruction of the 'global trust model'.
