On the development path of zero-knowledge machine learning (zkML), we have encountered many challenges. Pioneers have faced issues such as insufficient Computing Power and privacy protection. Recently, the Lagrange team claimed that through their DeepProve technology, they not only solved these problems but also significantly increased processing speed. However, we can't help but ask: Is the foundation of this fast-paved road solid enough?

In the field of medical diagnosis, the accuracy of proof is crucial; in financial risk control, the authenticity of data cannot be compromised. If we really want to move forward at full speed on this zkML path, we must ensure its reliability. Because once a significant error occurs, the losses it causes may be unbearable.

Although zkML technology has broad prospects, we still need to be cautious. We must not only focus on the rapid development of the technology but also ensure its stability and reliability in practical applications. Only in this way can zkML truly unleash its revolutionary potential in various fields, bringing us safe and efficient intelligent solutions.