Bittensor network in practice

Summarized points on Bittensor

• Decentralized mixture-of-experts model using AI tech
• Incentivized model for system users
• everyone is working on a different problem
• Consists of nodes containing neural networks
• Current modality for neurons is Text
• Expand the capacity of one model through several sub-models within it
• Model will intelligently pick which sub-model to route a specific input
• Sub-model will perform the computation through AI

Due to the nature of decentralized AI, it is not possible to enforce honest rewards without access to each node's local neural network. This is not possible. In fact, nodes with little competitive interest in attaining value from their neighbours may still collude to gain inflation without adding value to the network. This is achieved by forming a 'cabal', a set of one or more tightly connected nodes that falsely evaluate each other. Since there is no way to distinguish between rewards that are correctly given and those that are not, this behaviour will spread if left unchecked.

I am new to this so it is making little sense to me. Is it not the purpose of decentralization that validators can evaluate the rewards to be given based on the effort of the miners? If this is the case then the validator is keeping check but what is system to keep the validator in check? How does that work?

My understanding of Bittensor is that it is a decentralized mixture-of-experts model that uses AI technology. It incentivizes users of the system by allowing them to earn rewards for their contributions. In the Bittensor model, everyone is working on a different problem, with nodes containing neural networks that handle the computations through AI. The current modality for neurons is text, but the system has the capacity to expand and route inputs to the appropriate sub-model within it.