AI trustworthiness has recently become a hot topic in this track. Different technical approaches are competing in the market, with Inference Labs and Mira Network often being compared, but their solutions differ quite significantly.

Inference Labs adopts a mathematically rigorous technical approach. They are mainly focused on zero-knowledge machine learning (zkML), with the core logic being to enable verification of AI model reasoning processes through zero-knowledge proof technology while protecting computational privacy. Simply put: AI provides an answer, but you can verify its accuracy without seeing the full algorithm. This approach requires high computational power but achieves a high level of trustworthiness and security.

In contrast, Mira Network employs a different approach. They focus more on distributed computing networks and incentive mechanism design, ensuring the trustworthiness of AI services through network consensus. Both paths aim to solve the core issue of "how to make people trust AI," but their technical stacks and implementation logic are completely different. zkML is more like a mathematical proof, while distributed networks are more like collective endorsement. Each has its advantages and disadvantages, and the market will ultimately determine the better solution.