Three Top Applications of Lagrange DeepProve
AI large models are becoming stronger, but there is a problem that remains unsolved: how to make the AI's decision-making process "traceable and trustworthy"? For example, if the investment portfolio recommended by AI suffers losses, how do you know it hasn't miscalculated? Lagrange #Lagrange 's DeepProve provides the answer - the world's fastest zkML system, allowing the AI reasoning process to generate "on-chain verifiable" zero-knowledge proofs, equivalent to giving AI a "trustworthy seal of approval".
This is not an overstatement; its value can be seen in three scenarios:
- Financial Risk Control: Banks use AI to assess loan qualifications, and DeepProve can prove that "AI indeed used compliant data and algorithms", protecting customer privacy while reassuring regulatory agencies;
- Medical Diagnosis: After AI analyzes medical records and provides treatment plans, it can prove that "diagnosis is based on the patient's real data and does not leak privacy", addressing the challenge of medical data sharing;
- On-Chain Gaming: The attributes of AI-generated items and battle results, once proven and put on-chain, cannot be tampered with, preventing cheating and ensuring players believe in the "fairness of results".
The strength of DeepProve lies in its "speed" - it is several times faster than similar zkML systems and can handle the reasoning of complex models, meaning it won't slow down the experience in practical applications. The $LA token is the "lubricant" for all this: using DeepProve requires paying with LA, nodes generate proofs and earn LA, creating a positive cycle where "the more developers use it, the more stable the value of $LA ".
As the integration of AI and Web3 deepens, "trustworthy" will become a core competitive advantage. @Lagrange Official uses DeepProve to demonstrate: the future of AI must not only be smart but also "clear and transparent". #lagrange may be the key that truly makes AI "trustworthy".
