zk-ML

To redefine the vision of a decentralized and verifiable world computer into a reality by embedding cryptographically verified off-chain artificial intelligence to EVM compatible networks. The challenge? Today's public blockchains do not support artificial intelligence. Running off-chain Inference without verification put operator's intellectual property at stake, and requires users to trade-off trustlessness and again rely on centralized points of failure. But we're changing the game. With zk-VIN, we are ready to introduce web3 like never before – smart, verified intelligence. Over recent years, zero-knowledge proofs have played a pivotal role in blockchain technology for two primary reasons: (1) they enhance the scalability of compute-limited networks by handling transactions off-chain and subsequently confirming the outcomes on the mainnet; and (2) they bolster user confidentiality by facilitating veiled transactions, which are only accessible to those with knowledge of the concealed details. Given the nature of blockchains, the significance of these features is evident. For instance, a decentralized platform like Ethereum cannot amplify its throughput or block size without imposing excessive demands on validator computational power, bandwidth, and response time. This necessitates the use of rollups in their many forms. Additionally, since all transactions are transparent to everyone, there's a growing need for on-chain privacy solutions. However, the utility of zero-knowledge proofs isn't confined to these two aspects. They also offer a third set of advantages: the efficient validation that any computation, not just those associated with an off-chain version of the EVM, has been executed accurately. This potential extends well beyond the realm of blockchains.

Innovations in systems that harness the power of zero-knowledge proofs for concise computational verification are paving the way for users to expect the same level of trustworthiness and verifiability from all digital products, especially machine learning models. The increasing computational demands of blockchains have spurred research in zero-knowledge proofs, leading to the development of new and novel proving systems that are more memory-efficient and offer swifter proving and validation times. As a result, it's now feasible to validate machine learning algorithms.