It is clear to everyone that the biggest barrier to implementing large AI models in vertical areas such as finance, healthcare, and law is the problem of the "illusion" of results that do not meet the accuracy requirements in real-world use cases. How to solve this? Recently, @Mira_Network launched a public test network, offering a set of solutions, so I will explain what it is about:

Firstly, large AI model tools have cases of "illusions" that everyone can experience, the reasons for this are mainly two:
The data for training AI LLM is not sufficiently complete, although the volume of data is already very large, it still cannot cover some niche or professional information, in such cases AI is prone to "creative supplementation," which in turn leads to some real-time errors;
AI LLMs essentially rely on "probabilistic sampling", which involves identifying statistical patterns and correlations in training data, rather than true "understanding". Therefore, the randomness of probabilistic choice, inconsistency of training outcomes, and reasoning can lead to AI errors when processing high-precision factual queries;
How to solve this problem? An article describing methods for joint validation by multiple models to enhance the reliability of LLMs was published on the ArXiv platform of Cornell University.
A simple understanding is to first allow the base model to generate results, and then combine several verification models to conduct a "majority voting analysis" in order to reduce the "illusions" that arise in the model.
In a series of tests, it was found that this method can increase the accuracy of AI output to 95.6%.
Therefore, a distributed verification platform is undoubtedly needed to manage and verify the collaboration process between the core model and the verification model. The Mira Network is such an intermediary network, specifically designed for the verification of AI LLMs, which builds a reliable level of verification between the user and the underlying AI models.
Thanks to the existence of this network, verification level integrated services can be implemented, including privacy protection, accuracy assurance, scalable design, standardized API interfaces, and other integrated services, as well as the ability to deploy AI in various subdivided application scenarios can be enhanced by reducing the output illusion of AI LLM, which is also a practice in the implementation process of the AI LLM project by the distributed Crypto verification network.
For example, Mira Network shared several cases in the fields of finance, education, and ecology of blockchain to confirm:
After the integration of Mira on the Gigabrain trading platform, the system can add another level of verification for the accuracy of market analysis and forecasts by filtering out unreliable offers, which can enhance the accuracy of AI trading signals, making the application of AI LLMs in DeFi scenarios more reliable.
Learnrite uses mira to validate standardized test questions generated by artificial intelligence, allowing educational institutions to leverage AI-generated content at scale without compromising the accuracy of educational test content to uphold rigorous educational standards;
The Kernel blockchain project uses the LLM consensus mechanism from Mira, integrating it into the BNB ecosystem, creating a decentralized validation network (DVN) that ensures a certain level of accuracy and security of AI computations on the blockchain.