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@ sara
2023-06-02 03:24:27Verified ML inference opens up a new design space for smart contracts. Some crypto-native applications include:
DeFi
Verifiable Off-Chain ML Oracles. Continued adoption of generative AI may help push industries to implement signature schemes for their content (e.g. a news publication signing an article or image). Signed data is ZK-ready and makes the data composable and trustworthy. ML models can process this signed data off-chain to make predictions and classifications (e.g. classifying the outcome of an election or a weather event). These off-chain ML oracles could be used to trustlessly settle real-world prediction markets, insurance protocol contracts, and more by verifying the inference and publishing the proof on-chain.
ML-parameterized DeFi applications. Many facets of DeFi could be more automated. For example, a lending protocol could use an ML model to update parameters in real time. Today, lending protocols primarily trust off-chain models run by organizations to determine the collateral factor, LTV, liquidation threshold, etc., but a better alternative could be community-trained open-source models that can be run and verified by anyone.
Automated trading strategies. A common way of showcasing the return profile of a financial model strategy is for the MP to provide various backtests to investors. However, there is no way to verify the strategist is following the model when executing a trade — instead, the investor must trust the strategist is actually following the model. zkML offers a solution where the MP can provide proof of the financial model inference when deploying into a specific position. This could be particularly useful for DeFi managed vaults.
Security
Fraud monitoring for smart contracts. Rather than having slow human governance or centralized actors control the ability to pause a contract, an ML model could be used to detect possible malicious behavior and enact a pause.
Traditional ML
A decentralized, trustless implementation of Kaggle. A protocol or marketplace could be created that allows a MC or other interested party to verify the accuracy of a model without the MP disclosing the model weights. This can be useful for selling models, running competitions around model accuracy, etc.
Decentralized prompt marketplaces for generative AI. Prompt creation for generative AI has evolved into a sophisticated craft where the best output generating prompts are often complex with many modifiers. External parties could be willing to purchase these sophisticated prompts from a creator. zkML can be used in two ways here: 1) verifying the outputs of a prompt to ensure to a potential purchaser that the prompt does in fact create the desired images, and 2) allowing the prompt owner to maintain ownership of the prompt after a purchase by keeping it obfuscated from the purchaser while still generating verified images for them.
Identity
Replacing the private key with privacy-preserving biometric authentication. Private key management remains one of the largest frictions in web3 UX. Abstracting the private key via facial recognition or other unique factors is one possible solution from zkML.
Fair airdrops and contributor rewards. An ML model could be used to create detailed personas of users that determine airdrop allocations or contribution rewards based on multiple factors. This can be particularly powerful when combined with an identity solution. In this scenario, one possibility could be having users run an open-source model that evaluates their participation in an application along with higher context participation like governance forum posts to infer an allocation for them. They then provide this proof to the contract to receive their token allocation.
Web3 Social
Filtering for web3 social media. The decentralized nature of web3 social applications will result in higher volumes of spam and malicious content. Ideally, a social media platform could use an open-source ML model that is agreed upon by the community and publish proofs of the model’s inference when electing to filter a post. For more on this, check out Daniel Kang’s zkML analysis of the Twitter algorithm here.
Advertising / Recommendations. As a social media user, I may be willing to view personalized advertisements but wish to keep my preferences and interests private from advertisers. I could elect to run a model on my tastes locally that feeds into media applications to serve me content. In doing this, advertisers in this scenario may be willing to pay for the end-user to do this, however it is likely these models would be much less sophisticated than targeted advertising models in production today.
Creator Economy / Gaming
In-game Economic Rebalancing. ML models could be used to dynamically adjust token issuance, supply, burn, voting thresholds, etc. One possible model would be an incentivized contract that rebalances an in-game economy if a certain rebalancing threshold is met and a proof of inference is verified.
New types of on-chain games. Co-operative human versus AI games and other innovative takes on on-chain games could be created where a trustless AI model serves as a non-playable character. Each action the NPC takes is published to the chain with a proof that anyone can verify to determine the correct model is being run. In the case of Modulus Labs’ Leela vs. the World, the verifier wants to ensure the stated 1900 ELO AI is picking the chess moves and not Magnus Carlson. Another example is AI Arena, a Super Smash Brothers style AI fighting game. Players in high stakes competitive environments would want to be sure that there is no interference or cheating involved with their trained models.