zk+ML, the new direction of the zk track?

特邀专栏作者

2023-05-25 13:00

This article is about 2529 words, reading the full article takes about 4 minutes

"Decentralization, crypto technology and trust are the most basic infrastructure of Web 3"

The recent explosion of Worldcoin has also created enough momentum for a Web 3+AI narrative. Worldcoin belongs to the concept of zkML, derived from zk+ML (zero-knowledge proof and machine learning), and is also an emerging combination that has been talked about recently. zk Needless to say, technology, and ML is a subfield of AI. AI+Web3 has been a very popular narrative in the industry before, but at present, there is no good concept or use case to seamlessly connect the two, and At the recent Montenegro conference, Vitalik also highly praised zkSNARK, coupled with the explosion of Worldcoin, it is predictable that zkML will stand out.

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Web 3 + ML

zkML combines zero-knowledge proof and machine learning. In fact, outside of Web 3, ML is no longer a new word. This technology has been widely used in some fields, such as natural language processing (NLP), autonomous driving, e-commerce, etc. All fields have reached a higher level through ML technology, and even in some fields ML has already occupied a dominant position, so the future zkML is also the general trend, and embedding ML in smart contracts will also provide smart contracts with more complex and smarter processing methods .

By adding ML capabilities, smart contracts can become more autonomous and dynamic, allowing them to act based on real-time on-chain data rather than static rules. Smart contracts will be more flexible and adapt to more scenarios, including those that may not have been anticipated when the contract was originally created. In short, ML capabilities will amplify the automation, accuracy, efficiency, and flexibility of any smart contract we put on-chain.

Currently, one of the reasons why ML is not widely adopted in crypto is that it is computationally expensive to run these models on-chain, such as fastBERP - a class of NLP language models, which needs to use about 1800 MFLOPS (million floats) for adoption. point arithmetic), which cannot be run directly on the EVM. While application models need to make predictions based on real-world data, in order to have ML-scale smart contracts, contracts must obtain such predictions;

The second reason is the need to deal with the trust framework of ML models. There are two main points. One is its privacy: as mentioned earlier, model parameters are usually private, and in some cases, model inputs also need to be kept secret, which naturally There are some trust issues between the model owner and the model user; the second is the algorithmic black box, and ML models are sometimes called "black boxes" because they involve many automated steps in the calculation process that are difficult to understand or explain. These steps involve complex algorithms and large amounts of data that lead to indeterminate and sometimes random outputs, making algorithms prime for bias and even discrimination. And zk technology can solve this trust problem very efficiently.

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Use cases for zkML in crypto

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DeFi

Verifiable off-chain machine learning oracles

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ML Parameterized DeFi

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Automated Trading Strategies

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security area

Fraud Monitoring for Smart Contracts

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DID and Social

Replace private keys with biometric authentication (which is what Worldcoin currently does)

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Personalized Recommendation and Content Filtering for Web3 Social Media

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Creator Economy and Gaming

In-game economy rebalancing

ML models can be used to dynamically adjust token issuance, supply, destruction, voting thresholds, etc. One possible model is an incentive contract that can rebalance the in-game economy if a certain rebalancing threshold is reached and proof of reasoning is verified.

New type of chain game

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