I have been reading Miras whitepaper and one thing keeps sticking with me Mira's whitepaper and the problem of AI hallucinations and biases. Mira's whitepaper says that these are not just problems with a model or its data. They are a problem that needs a solution that involves the whole system.
Every large language model has a problem when it is being trained. If you make it more precise it will have systemic bias.. If you try to reduce the bias it will be less precise. Miras whitepaper helped me understand this. It says that hallucinations are when a model is not precise and gives answers that're not consistent or are too confident. On the hand bias is when a model is not accurate and gives answers that are not true.
No matter how data or computing power you use there is a limit to how accurate a model can be. You cannot eliminate all errors.
This trade-off is important. Of trying to make one big model that is perfect what if we treated what AI models say as claims that need to be checked? This makes sense especially when you think about cryptocurrency. You should not trust one AI model. You should build a system that checks what it says.
Mira does that. It takes what an AI model says and breaks it down into claims. Then it uses models to check each claim. It is like a test to see if something is true.
I remember one example from Miras whitepaper. Lets say we want to check if the statement "The Earth revolves around the Sun and the Moon revolves around the Earth" is true. Mira would break this down into two claims. One claim is about the Earth and the Sun and the other claim is about the Moon and the Earth. Each claim is sent to models and they each give an answer. Then the answers are combined to see if most of the models agree. If they do Mira creates a certificate that says the claim is true. This certificate also says which models agreed so you know who checked it.
This is similar to how blockchains work. Of trusting one ledger we trust many nodes. Mira does the thing but for what AI models say. It uses a system that rewards models for being honest and penalizes them for being dishonest.
I also like that Mira uses different models to check claims. It does not rely on one model to do everything. This is good because different models have strengths and weaknesses. This makes the system more reliable.
The goal of Mira is not to be perfect. It is to be more reliable by using models to check claims. Miras whitepaper says that this can make the system 95% or more accurate.
And that is the point: this is not about making AI perfect. It is about building a system where truth comes from sources, not just one.
Mira fits perfectly into the idea of cryptocurrency. We did not create Bitcoin because we wanted a bank. We created it because we wanted a way to transfer value without needing to trust someone. Mira is doing the thing but for truth. It is saying, do not trust one model verify it. Give models the power to check.
That is the idea that I keep thinking about. Mira might not just be a way to check what AI models say. It might be a part of making AI work, on its own without needing a central controller.. That makes it worth paying attention to no matter what happens to its token.