Will zkML be the new direction of the zk track?

In the wild and anarchic Web3 world, decentralization, crypto technology and trust are the most basic infrastructure.

Written by: Leo

The recent explosion of Worldcoin has also created enough momentum for a Web3+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 technology Needless to say, ML is a sub-field 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. Recently, At the Montenegro conference, Vitalik also highly praised zkSNARK, coupled with the explosion of Worldcoin, so it is predictable that zkML will stand out.

You may not be familiar with zkML. This article mainly clears up the fog on zkML for you, focusing on the introduction, use cases and some potential projects of zkML. Because there are not many zkML use cases at present, I hope you can seize the opportunity and learn about it in advance. New concepts and use cases, get ready.

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.

So at this time, zkSNARK appeared along the trend. The zk technology in zkML mostly refers to zkSNARK. zkSNARK provides us with a solution: anyone can run a model off-chain and generate a concise and verifiable proof that the expected The model does produce a specific result, and this proof can be published on-chain and captured by smart contracts and enhance their intelligence. ML models typically require three parts: training data, model architecture, and model parameters. As long as the trained model passes reasoning verification, it can open up an updated design space for smart contracts. (Model training and reasoning will not be described too much)

Use cases of zkML in crypto

And the smart contract after adding zkSNARK +ML will also have many use cases, the following are its use cases:

DeFi

Verifiable off-chain machine learning oracle

Combining zkSNARKs with verified reasoning of ML models, these off-chain ML oracles can be used to reliably solve real-world prediction markets, secure protocol contracts, etc. by verifying reasoning and publishing evidence on-chain.

ML Parameterized DeFi

Many subdivisions of DeFi can actually be automated. For example, lending protocols could use ML models to update parameters in real time. Today's lending protocols mainly trust off-chain models run by organizations to determine collateral coefficients, LTV, liquidation thresholds, etc. ML can provide a better alternative, community-trained open-source models that anyone can run and verify.

Automated Trading Strategies

One way to verify the return of a trading strategy is to have the MP provide investors with various backtests, it is impossible to verify that the strategist followed the model when executing the trade, but zkML can provide a solution for this, the MP can be deployed to a specific location Provides proof of validation of financial model reasoning.

Security Domain

FRAUD MONITORING FOR SMART CONTRACT

Instead of manual governance or centralized actors controlling the ability to suspend contracts, ML models can be used to detect possible malicious behavior and enforce suspension procedures.

DID and Social

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

Private key management remains another headache for Web3 users. Extracting private keys via facial recognition or other biometrics is one possible solution for zkML, and Worldcoin is applying this in exactly the same way, with its Orb device to determine if someone is a real person without trying to fake KYC, and Using zk technology to ensure that the output of its ML models does not reveal users' personal data, this is achieved through various camera sensors and machine learning models that analyze facial and iris features.

Personalized recommendation and content filtering for Web3 social media

Similarly, some Web 3 social media can easily obtain user preferences and data, show us some spam and fake links, and many fake links lead to theft of user wallets, etc., but we can avoid many unnecessary content and email links through zkML technology .

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

Cooperative human-AI games and other innovative on-chain games can be created, where a trustless AI model acts as an NPC, and all actions of the NPC are posted on-chain with a Proof of the model.

zkML ecological potential project

Since zkML is still in the early stage of development, there are not many projects that can be found. The following are the potential projects found for everyone:

Worldcoin

Worldcoin will not be described too much, everyone should be familiar with it, please refer to "If Worldcoin succeeds, what impact will it have on the encryption industry?"

Modulus Labs

Modulus Labs is one of the more diverse projects in zkML, the technology needed to build AI on the chain. Work on both use cases and related research. On the application side, Modulus Labs has developed RockyBot (an on-chain trading bot) and Leela vs. the World (a chess game), where real people play against a verifiable on-chain instance of the Leela chess engine.

Human

Giza is a protocol dedicated to developing the economy through AI. It can deploy AI models on the chain using a completely trustless method. It is supported by StarkWare cooperation and finally realizes a market that provides an alternative path for AI development.

Zkaptcha

Zkaptcha focuses on the robot problem in Web3, protects smart contracts from robot attacks, uses zero-knowledge proofs to create smart contracts that are resistant to Sybil attacks, and provides verification code services for smart contracts. Currently, the project enables end users to generate a proof of human work by completing captchas. In the future, Zkaptcha will inherit zkML and launch a service similar to existing Web 2 captchas, but can also analyze behaviors such as mouse movements to determine user Is it real.

Conclusion

At present, there are not many products in the field of combining zkML and crypto, and some problems will be encountered in the process of building such products, and zkML and crypto may need more improvement and optimization in the future. However, with the combination of zkSNARK and ML, we have reason to believe that the power of zkML can bring better prospects and development to crypto, and we also expect more products in this field. zk technology and crypto provide security for the operation of ML Credible environment, and in the future, in addition to product innovation, it may also give birth to the innovation of crypto business model, because in this wild and anarchic Web 3 world, decentralization, crypto technology and trust are the most important Basic facilities.