As artificial intelligence becomes increasingly integrated into everyday life, there is an unprecedented demand for the credibility, transparency, and safety of AI results.
The emergence of @inference_labs is precisely in this context, proposing an AI infrastructure based on cryptographic verification.
Inference Labs uses zero-knowledge cryptographic protocols such as Proof of Inference to enable each AI inference output to be mathematically proven correct without revealing model details or user data. This design makes AI outputs in critical scenarios like medical diagnosis, financial decision-making, and automation systems auditable and verifiable.
This technological advancement in real life means that when AI makes important judgments, people no longer just trust the results blindly but can verify their authenticity and reliability based on objective standards. This helps reduce risks caused by AI errors or biases and accelerates the adoption of AI in sensitive fields.
@KaitoAI #Yap @easydotfunX
View Original
This page may contain third-party content, which is provided for information purposes only (not representations/warranties) and should not be considered as an endorsement of its views by Gate, nor as financial or professional advice. See Disclaimer for details.
As artificial intelligence becomes increasingly integrated into everyday life, there is an unprecedented demand for the credibility, transparency, and safety of AI results.
The emergence of @inference_labs is precisely in this context, proposing an AI infrastructure based on cryptographic verification.
Inference Labs uses zero-knowledge cryptographic protocols such as Proof of Inference to enable each AI inference output to be mathematically proven correct without revealing model details or user data. This design makes AI outputs in critical scenarios like medical diagnosis, financial decision-making, and automation systems auditable and verifiable.
This technological advancement in real life means that when AI makes important judgments, people no longer just trust the results blindly but can verify their authenticity and reliability based on objective standards. This helps reduce risks caused by AI errors or biases and accelerates the adoption of AI in sensitive fields.
@KaitoAI #Yap @easydotfunX