What is the most troubling issue in the field of AI?
It’s not the lack of computing power, nor is it that the models aren’t large enough; rather, it’s that we can never be sure whether the answers provided by AI are trustworthy.
It’s like letting a black box make decisions—would you dare to use it?
LAGRANGE's DeepProve-1 system offers a stunning solution.
What attracts me most about this project is not the technology itself, but that it addresses a fundamental issue:
How to make the reasoning process of AI transparent and verifiable.
Through zero-knowledge proof technology, DeepProve-1 can prove that the output of AI is derived from correct calculations without revealing the details of the model.
This reminds me of the confusion I felt when I first entered the AI industry in 2010.
At that time, we often said, "the larger the model, the better the results," but no one could guarantee what was actually happening inside the model.
Now, DeepProve-1 has finally made a breakthrough in this issue.
Especially in fields like finance and healthcare, which have extremely high reliability requirements, this system is like a lifesaver.
What surprised me the most is its complete support for the Transformer architecture.
It’s worth noting that today’s mainstream large models are basically based on Transformers, but previously no one could fully verify their reasoning processes.
The LAGRANGE team has not only achieved this but also ensured compatibility with mainstream model formats, which is invaluable in practical applications.
However, the challenges are not small.
The computational overhead of zero-knowledge proofs has always been a significant drawback, and DeepProve-1 needs to continue optimizing its performance for large-scale applications.
But regardless, this is already an important milestone in the field of AI trustworthiness.
