Opinion Can androids envision zeroknowledge scenarios
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In the iconic opening scene of the movie Blade Runner, a character named Holden uses a fictional version of the Turing test to determine if Leon is a replicant, a humanoid robot. Holden tells Leon a story to provoke an emotional response, asking him to imagine encountering a tortoise in the desert. As the story progresses, Leon becomes increasingly agitated, revealing his non-human nature.
While we haven’t reached the dystopian world of Blade Runner in reality, the integration of AI and machine learning in our lives raises the need for assurance that the AI models we rely on are genuine. This is where zero-knowledge proofs come into play. Zero-knowledge proofs allow one party to prove to another that a computation was executed correctly without revealing the data or requiring the verifier to redo the calculations. It’s similar to verifying a sudoku puzzle solution.
The value of zero-knowledge proofs is particularly evident when computational tasks occur off-chain to avoid overwhelming a network and incurring high fees. With zero-knowledge proofs, these tasks can still be verified without burdening blockchains, which have limitations due to the need for all nodes to verify each block. In essence, zero-knowledge cryptography is essential for securely and efficiently scaling AI and machine learning.
Machine learning, a subset of AI, is known for its intensive computational requirements. ML models process vast amounts of data to simulate human adaptation and decision-making. From image recognition to predictive analytics, ML models have the potential to transform various industries. However, they also push the boundaries of computation. How can we ensure that ML models are genuine and trustworthy using blockchains, considering the high costs of on-chain operations?
We need a way to trust AI models, ensuring they haven’t been tampered with or falsely represented. While it may not be disastrous if the quality of responses from a ChatGPT model about sci-fi films deteriorates, industries like finance and healthcare require accuracy and reliability. A single mistake could have far-reaching negative economic consequences.
This is where zero-knowledge proofs play a crucial role. By leveraging zero-knowledge proofs, ML computations can be executed off-chain while still being verified on-chain. This opens up new possibilities for deploying AI models in blockchain applications. Zero-knowledge machine learning (ZKML) allows for cryptographic verification of ML algorithms and outputs while keeping the algorithms themselves private. This bridges the gap between AI’s computational demands and blockchain’s security guarantees.
One exciting application of ZKML is in decentralized finance (DeFi). Imagine a liquidity pool managed by an AI algorithm that optimizes asset rebalancing to maximize yield and refines its trading strategies over time. ZKML can perform these calculations off-chain and use zero-knowledge proofs to ensure the legitimacy of the ML model, protecting users’ trading data while allowing the models to be public. The result is secure AI-driven DeFi protocols with verifiability.
As AI becomes increasingly central to human activities, concerns about tampering, manipulation, and adversarial attacks grow. AI models, especially those making critical decisions, must be resistant to attacks that could compromise their outputs. We not only want AI applications to be safe but also to create a trustless environment where the models are easily verifiable.
In a world where AI models are prevalent, we are heavily reliant on AI. With the proliferation of models, the potential for attacks that undermine their integrity also increases. This is particularly concerning when the output of an AI model may not be as it seems.
By integrating zero-knowledge cryptography into AI, we can begin to build trust and accountability in these models. Similar to an SSL certificate or security badge in a web browser, there may be a symbol of AI verifiability, assuring users that they are interacting with the expected model.
In Blade Runner, the Voight-Kampff test aimed to distinguish replicants from humans. In our AI-driven world, we face a similar challenge of differentiating authentic AI models from potentially compromised ones. In the realm of cryptocurrencies, zero-knowledge cryptography can serve as our Voight-Kampff test, a robust and scalable method to verify the integrity of AI models without compromising their inner workings. This way, we not only ask if androids dream of electric sheep but also ensure that the AI guiding our digital lives is exactly what it claims to be.
Rob Viglione is the co-founder and CEO of Horizen Labs, a development studio behind various web3 projects. He is deeply interested in web3 scalability, blockchain efficiency, and zero-knowledge proofs. His work focuses on developing innovative solutions for zk-rollups to enhance scalability, cost savings, and efficiency. With 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.
