As an analyst with a background in both finance and physics, I find myself increasingly intrigued by the intersection of blockchain technology, AI, and zero-knowledge proofs. The potential for these technologies to transform industries while maintaining security and efficiency is truly fascinating.
In the famous opening scene of Blade Runner, a character named Holden administers a fictional interpretation of the Turing test to gauge if Leon is a replicant (a humanoid robot). For the test, Holden tells Leon a story to elicit an emotional reaction. “You’re in a desert, walking along in the sand, when all of a sudden you look down…you look down and see a tortoise, Leon. It’s crawling toward you…” As Holden keeps telling this hypothetical story, Leon gets more and more agitated until it’s obvious he is not human.
In the actual world, we’re not quite at the level depicted in “Blade Runner,” but as AI and machine learning become increasingly prevalent in our daily lives, it’s essential to ensure that the AI systems we rely on are genuinely what they claim to be.
Zero-knowledge proofs serve as a method for one party to confirm to another that a certain computation has been carried out accurately, without revealing the original data or making the second party perform the calculations again (due to their efficiency). To illustrate this, consider a sudoku puzzle. Solving it can be challenging, but checking the solution is straightforward and doesn’t require repeating the entire process.
This property is particularly useful when complex computations happen outside the network, preventing it from becoming overloaded and incurring high fees. Using Zero-Knowledge proofs, these off-chain computations can still be confirmed without straining blockchains, which have limited computational power due to the requirement that every node verifies each block. In essence, we rely on Zero-Knowledge cryptography to securely and efficiently scale AI machine learning across a network.
ZK verifies ML models so we can scale AI safely
Machine learning, a branch of artificial intelligence, is renowned for its high computational requirements, as it needs enormous data processing to mimic human adaptation and decision-making. From identifying images to forecasting analytics, machine learning models are poised to revolutionize almost every sector—and they already have in many cases. However, these models also strain the boundaries of computation. But how can we guarantee and certify that these machine learning models are genuine using blockchain technology, where on-chain operations can become costly?
To ensure confidence in AI models, we require a reliable method for validating their authenticity, assuring us that the model isn’t manipulated or misrepresented. In casual contexts like discussing favorite sci-fi films, trusting the AI model may not be of utmost importance if its responses occasionally falter. However, in crucial sectors such as finance and healthcare, precision and dependability are paramount. A single error could trigger a chain reaction of adverse economic consequences globally.
ZK technology plays a crucial part in this setup. By employing Zero-Knowledge proofs, machine learning calculations can be performed off-chain yet verified on-chain. This innovation paves the way for AI models to be utilized in blockchain projects. In essence, Zero-Knowledge Machine Learning (ZKML) offers cryptographic confirmation of ML algorithms and their outcomes while keeping the underlying algorithms secret, thus addressing the computational requirements of AI and ensuring the security promises of blockchain technology.
One of the most exciting ZKML applications is DeFi. Imagine a liquidity pool where an AI algorithm manages the rebalancing of assets to maximize yield while refining its trading strategies along the way. ZKML can execute these calculations off-chain and then use ZK proofs to ensure an ML model is legitimate, rather than some other algorithm or another person’s trades. At the same time, ZK can protect users’ trading data so that they retain financial confidentiality, even if the ML models they’re using to make trades are public. The result? Secure AI-driven DeFi protocols with ZK verifiability.
We need to know our machines better
As AI takes on a larger role in our daily lives, worries about interference, deceit, and hostile actions persistently escalate. Particularly for AI systems making crucial decisions, it’s essential that they can withstand attacks designed to distort their results. It goes without saying that we prioritize the security of AI tools. This isn’t just about traditional AI safety (avoiding unforeseen behavior from models), but also about establishing a system where the model’s integrity can be independently verified, fostering trust in its outcomes.
In a time when artificial intelligence (AI) models are abundant, they significantly influence our daily lives. With an increasing number of these models, there’s also a rising risk of attacks that compromise their authenticity. This is especially concerning in situations where the AI-generated output may appear deceptive or misleading.
By incorporating Zero-Knowledge (ZK) cryptography into Artificial Intelligence systems, we can establish trust and accountability in these models right from the start. Just as an SSL certificate or a security seal on your web browser assures you about website authenticity, there will probably be a symbol for AI verifiability that ensures the model you’re engaging with is the one you intended to interact with.
In the movie “Blade Runner”, the Voight-Kampff test was used to discern replicants from humans. Similarly, in today’s AI-dominated era, we encounter a parallel dilemma: identifying genuine AI models from those that may be corrupted or malfunctioning. In the realm of cryptography, Zero-Knowledge (ZK) cryptography could serve as our modern-day Voight-Kampff test—a strong, scalable tool for verifying AI model integrity without revealing their internal mechanics. Thus, we’re not just questioning if robots can dream but also guaranteeing that the AI guiding our digital existence is indeed what it claims to be.
Rob Viglione is the co-founder and CEO of Horizen Labs, the development studio behind several leading web3 projects, including zkVerify, Horizen, and ApeChain. Rob 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, create cost savings, and drive 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.
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2024-10-23 14:00