Applied ML on Vanna
Our vision for applied ML models on the Vanna Network
In addition to AI models and agents, applied machine learning (ML) also can have a tremendous impact on the Web3.0 space by enabling existing protocols to seamlessly develop smarter features.
For example, existing problems in DeFi ranging from AMM liquidity providers bleeding money to lack of risk management in CDP protocols can be made better by taking a page out of TradFi’s book and using more sophisticated models and computation.
One can imagine a world where lending protocols compute interest rates using smarter models, AMM pools reducing impermanent loss through charging dynamic fees based on predicted realized volatility, or CDP protocols having better risk engines to minimize loss during liquidation proceedings. We believe higher efficiency and effective risk management through applied ML can seriously level up DeFi’s game to increase traction and adoption. Without the same tools, it’s difficult for decentralized finance to approach traditional quantitative finance in terms of efficiency and optimization.
Applied ML in Web3.0 would allow for development of features that increases efficiency and improves risk management that can seriously level up DeFi’s game to increase traction and adoption. Without the same tools, it’s difficult for decentralized finance to approach traditional quantitative finance in terms of efficiency and optimization.
Below we've outlined a series of Web3.0 use-cases that we at Vanna Labs are particularly excited about. For more details, check out our Medium article about the use-cases of ML in DeFi.
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