Opinion Can androids envision zeroknowledge in their dreams
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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 whether Leon, a humanoid robot called a replicant, is human or not. Holden tells Leon a story in an attempt to elicit an emotional response. As the story progresses, Leon becomes increasingly agitated, revealing his non-human nature.
While we may not be in the Blade Runner world just yet, the integration of AI and machine learning into 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 specific computation has been executed correctly without revealing the underlying data or requiring the verifier to redo the calculations. In other words, it allows for efficient verification without compromising privacy.
This property is particularly valuable when computational tasks are performed off-chain to prevent network congestion and high fees. With zero-knowledge proofs, these off-chain tasks can still be verified without burdening blockchains, which have limited computational capacity due to the need for all nodes to verify each block. Therefore, zero-knowledge cryptography is essential for securely and efficiently scaling AI and machine learning.
Machine learning, a subset of AI, is known for its significant computational requirements. ML models require extensive data processing 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. Therefore, it is crucial to verify the authenticity of ML models using blockchains, even though on-chain operations can be expensive.
We need a reliable way to trust AI models and ensure they have not been tampered with or falsely advertised. While it may not be catastrophic if there is a decrease in response quality when querying ChatGPT about sci-fi films, industries like finance and healthcare require accuracy and reliability. A single mistake could have far-reaching negative consequences.
This is where zero-knowledge proofs play a critical role. By leveraging zero-knowledge proofs, ML computations can be performed off-chain while still being verifiable 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 their outputs while keeping the algorithms themselves private. This bridges the gap between the computational demands of AI and the security guarantees of blockchain.
One exciting application of ZKML is in decentralized finance (DeFi). Imagine a liquidity pool where an AI algorithm manages asset rebalancing to maximize yield and refine trading strategies. ZKML can perform these calculations off-chain and then use zero-knowledge proofs to verify the legitimacy of the ML model. This ensures that the algorithm used is the intended one and not a different algorithm or someone else’s trades. At the same time, ZK protects users’ trading data, preserving financial confidentiality even when using public ML models for trading. The result is secure AI-driven DeFi protocols with verifiability through zero-knowledge proofs.
As AI becomes increasingly integral to human activities, concerns about tampering, manipulation, and adversarial attacks grow. AI models, especially those involved in critical decision-making, must be resistant to attacks that could compromise their outputs. We not only want AI applications to be safe in the traditional sense but also to create a trustless environment where the models themselves are easily verifiable.
In a world where AI models are prevalent, we rely on AI for various aspects of our lives. As the number of models increases, so does the potential for attacks that undermine the integrity of the models. 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 start building trust and accountability in these models. Similar to an SSL certificate or security badge in a web browser, there may be a symbol for AI verifiability, indicating that the model being interacted with is genuine.
In Blade Runner, the Voight-Kampff test was used to distinguish replicants from humans. In today’s AI-driven world, we face a similar challenge of differentiating authentic AI models from potentially compromised ones. In the crypto space, zero-knowledge cryptography can serve as our Voight-Kampff test, providing a robust and scalable method to verify the integrity of AI models without compromising their inner workings. This way, we not only ask whether androids dream of electric sheep but also ensure that the AI guiding our digital lives is exactly what it claims to be.
