How does Mira Network use decentralized networks to cure the stubborn "illusion" disease of big models?

Everyone knows that the biggest obstacle to the implementation of AI models in vertical application scenarios such as finance, medical care, and law: the "illusion" problem in AI output results cannot match the practical application scenarios that require accuracy. How to solve it? Recently, @Mira_Network launched a public test network and gave a set of solutions. Let me tell you what happened:

First of all, there are "illusions" in AI big model tools, and everyone can perceive them. There are two main reasons:

1. The training data of AI LLMs is not complete enough. Although the data is already huge, it still cannot cover some information in niche or professional fields. At this time, AI tends to do "creative completion" and then lead to some real-time errors;

2. AI LLMs work essentially relies on "probability sampling", which recognizes statistical patterns and correlations in training data, rather than truly "understanding". Therefore, the randomness of probability sampling, inconsistency of training and inference results will lead to bias in AI in dealing with high-precision factual problems;

How to solve this problem? A method to improve the reliability of LLMs results through multiple models was published on the Cornell University ArXiv platform.

To understand simply, it is to let the main model generate the results first, and then integrate multiple verification models to conduct a "major voting analysis" on the problem, thereby reducing the "illusion" generated by the model.

In a series of tests, this method can increase the accuracy of AI output to 95.6%.

In this case, a distributed verification platform is definitely needed to manage and verify the collaborative interaction process between the main model and the verification model. Mira Network is such a middleware network that specializes in building AI LLMs verification, building a reliable verification layer between the user and the basic AI model.

With the existence of this verification layer network, integrated services including privacy protection, accuracy guarantee, scalable design, standardized API interfaces can be implemented, and the possibility of AI implementation in various segmented application scenarios can be expanded by reducing the illusion of AI LLMs output. It is also a practice in the implementation of AI LLMs projects by Crypto distributed verification network.

For example, Mira Network shared several cases in finance, education, and blockchain ecosystems that can be proved:

1. After a trading platform integrates Mira, the system can add a link to verify the accuracy of market analysis and prediction, filter out unreliable suggestions, improve the accuracy of AI trading signals, and make AI LLMs more reliable in the DeFai scenario;

2. Learnrite uses mira to verify the standardized test questions generated by AI, allowing educational institutions to use AI to generate content on a large scale without affecting the accuracy of educational test content, so as to maintain strict educational standards;

3. The blockchain Kernel project uses Mira's LLM consensus mechanism to integrate it into the BNB ecosystem and creates a decentralized verification network DVN, so that the accuracy and security of AI computing execution on the blockchain can be guaranteed to a certain extent.

above.

In fact, Mira Network provides middleware consensus network services, which is definitely not the only way to enhance AI application capabilities. In fact, through training enhancement on the data side, interaction enhancement of multimodal large models, and privacy computing enhancement through potential cryptography technologies such as ZKP, FHE, and TEE are all optional paths. But in comparison, Mira's solution is that it is implemented quickly and can be directly effective.