Opinion Can androids envision zeroknowledge scenarios in their dreams
Disclaimer: The author’s views and opinions expressed in this article are their own and do not represent the views and opinions of crypto.news’ editorial team.
In the iconic opening scene of the movie Blade Runner, a character named Holden conducts a fictional version of the Turing test to determine if Leon is a replicant, which is a humanoid robot. To administer the test, Holden tells Leon a story with the intention of eliciting an emotional response. As Holden continues with this hypothetical story, Leon becomes increasingly agitated, revealing his non-human nature.
While we may not be in the realm of Blade Runner in the real world just yet, the integration of AI and machine learning into our lives calls for assurances that the AI models we rely on are genuine. This is where zero-knowledge proofs come into play. At their core, ZK proofs allow one party to prove to another that a specific computation was executed correctly without revealing the actual data or requiring the verifier to redo the calculations. It’s similar to solving a sudoku puzzle, where verifying the solution is easier than solving it.
The value of this property becomes evident when computational tasks occur off-chain to prevent network congestion and high fees. With ZK proofs, these off-chain tasks can still be verified without burdening blockchains, which have computational limitations since all nodes must verify each block. In summary, ZK cryptography is essential for scaling AI machine learning securely and efficiently.
Machine learning, a subset of AI, is known for its extensive computational requirements, as it involves processing vast amounts of data to simulate human adaptation and decision-making. ML models have the potential to revolutionize nearly every industry, but they also push the boundaries of computation. So how do we ensure that ML models are authentic when using blockchains, where on-chain operations can be costly?
We need a reliable method to trust AI models and ensure they haven’t been tampered with or falsely advertised. When querying ChatGPT about our favorite sci-fi films, we generally trust the model being used, and minor fluctuations in response quality are acceptable. However, in industries like finance and healthcare, accuracy and reliability are crucial. A single mistake could have far-reaching negative economic consequences.
This is where ZK plays a crucial role. By leveraging ZK proofs, ML computations can occur off-chain while still being verified on-chain. This opens up new possibilities for deploying AI models in blockchain applications. Zero-knowledge machine learning, or ZKML, enables cryptographic verification of ML algorithms and their outputs while keeping the algorithms themselves private. This bridges the gap between AI’s computational demands and blockchain’s security guarantees.
One of the most exciting applications of ZKML is in decentralized finance (DeFi). Imagine a liquidity pool where an AI algorithm manages asset rebalancing to maximize yield while refining its trading strategies. ZKML can perform these calculations off-chain and use ZK proofs to verify the legitimacy of the ML model, ensuring it’s not substituted with another algorithm or someone else’s trades. At the same time, ZK can protect users’ trading data, preserving financial confidentiality even if the ML models used for trading are public. The result is secure AI-driven DeFi protocols with ZK verifiability.
As AI becomes increasingly central to human activity, concerns about tampering, manipulation, and adversarial attacks continue to grow. AI models, especially those making critical decisions, must be resistant to attacks that could compromise their outputs. We want AI applications to be safe, but we also need to create a trustless environment where the models themselves are easily verifiable.
In a world where AI models are ubiquitous, we rely on AI to guide us through our lives. As the number of models increases, so does the potential for attacks that undermine the integrity of these models. This is particularly concerning when the output of an AI model may not be what it appears to be.
By integrating ZK cryptography into AI, we can begin to build trust and accountability in these models. Just as an SSL certificate or security badge in a web browser provides reassurance, there will likely be a symbol for AI verifiability, guaranteeing that the model being interacted with is genuine.
In Blade Runner, the Voight-Kampff test aimed to distinguish replicants from humans. In today’s AI-driven world, we face a similar challenge of distinguishing authentic AI models from potentially compromised ones. In the realm of crypto, ZK cryptography could serve as our Voight-Kampff test—a robust and scalable method to verify the integrity of AI models without compromising their inner workings. This ensures that the AI guiding our digital lives is precisely what it claims to be.
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Rob Viglione is the CEO and co-founder of Horizen Labs, a development studio behind various leading web3 projects, including zkVerify, Horizen, and ApeChain. He is deeply interested in web3 scalability, blockchain efficiency, and zero-knowledge proofs. Rob focuses on developing innovative solutions for zk-rollups to enhance scalability, cost savings, and efficiency. He holds a Ph.D. in Finance, an MBA in Finance and Marketing, and a Bachelor’s degree in Physics and Applied Mathematics. Rob currently serves on the Board of Directors for the Puerto Rico Blockchain Trade Association.
