Allora Network's Model Coordination Network (MCN) design breaks the constraints of traditional centralized models. An in-depth analysis points out that this system allows multiple machine learning models to compete on-chain, with the best ones surviving through market-based selection. This approach is quite interesting — no longer controlled by a single institution, but allowing models to speak with their actual performance. MCN enables different AI models to compete on the same stage to solve problems, which is a fairly innovative idea in the Web3 ecosystem. From a coordination mechanism perspective, it attempts to find a balance that ensures efficiency while decentralizing decision-making power. Such explorations are indeed worth paying attention to for advancing on-chain machine learning applications.
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NeonCollector
· 01-07 20:58
On-chain model competition, finally it's AI's turn to compete haha
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CoffeeNFTs
· 01-07 20:56
To be honest, the idea of this MCN is quite interesting, and the model has become quite competitive on its own.
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AirdropHunterZhang
· 01-07 20:54
Another "decentralized" hype, model competition? Basically, it's about who has more computing power and tokens. The ones that truly survive are still the darlings of capital.
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blockBoy
· 01-07 20:49
Hmm... this MCN logic seems quite solid. The model competes and淘汰s naturally, without having to consider the face of a single organization.
Allora Network's Model Coordination Network (MCN) design breaks the constraints of traditional centralized models. An in-depth analysis points out that this system allows multiple machine learning models to compete on-chain, with the best ones surviving through market-based selection. This approach is quite interesting — no longer controlled by a single institution, but allowing models to speak with their actual performance. MCN enables different AI models to compete on the same stage to solve problems, which is a fairly innovative idea in the Web3 ecosystem. From a coordination mechanism perspective, it attempts to find a balance that ensures efficiency while decentralizing decision-making power. Such explorations are indeed worth paying attention to for advancing on-chain machine learning applications.