Niraj Pant and Anish Agnihotri from Ritual discuss enhancing on-chain intelligence with AI, exploring AI-Crypto intersections, AI integration possibilities, and Ritual's open AI infrastructure goals. They touch on AI in blockchain, ZKML evolution, AI integration for decision-making, and Ritual's role in DeFi protocols for on-chain intelligence, provers network, and AI optimizations.
Integrating AI models into blockchain protocols can automate decision-making processes and enhance on-chain operations.
Ritual aims to bring AI technologies to blockchain systems, streamlining actions like lending, governance, and risk assessments.
Ritual is exploring solutions for balancing AI efficiency with human intervention to optimize security and usability in blockchain operations.
Deep dives
AI and Cryptography Intersection
Ritual delves into the intersection of artificial intelligence (AI) and cryptography, focusing on how these fields can mutually benefit each other. AI models can enhance blockchain protocols by automating decision-making processes currently performed manually. The potential for AI to improve on-chain processes while leveraging the security and transparency inherent in blockchain systems is a key area of focus.
Bringing AI to Blockchain
Ritual aims to bring AI technologies to blockchain systems, enabling more efficient and optimized decision-making mechanisms. By integrating AI models into crypto protocols, Ritual seeks to streamline and automate various actions, such as lending, governance, and risk assessments. This integration can enhance the scalability and effectiveness of blockchain applications, bridging the gap between traditional finance and decentralized technologies.
Balancing Average and Worst-Case Scenarios
In navigating the complexities of blockchain and AI integration, Ritual is exploring solutions that strike a balance between average case usability and worst-case security considerations. While AI can handle routine operations efficiently, there is still a need for human intervention and governance to address extreme scenarios and prevent potential adversarial actions. Ritual's goal includes developing mechanisms that can autonomously manage typical transactions while offering fallback options for exceptional situations, optimizing both efficiency and security in blockchain operations.
Ritual's Focus on Efficient On-Chain Execution of AI Models
Ritual aims to streamline decision-making processes within DeFi protocols by offloading work like risk modeling and ML onto chain for efficient execution. By employing parameter guards and safe measures, the models can run on chain with a safety net to ensure correct execution. This approach minimizes the need for numerous governance proposals, enhancing efficiency and providing tangible results, positioning Ritual as a general-purpose AI execution layer.
Integration of Different Types of Nodes in Ritual's Network
Ritual envisions a network with diverse node classes like compute nodes, AI inference nodes, and proving nodes, each specializing in distinct operations. By decentralizing tasks among varying node types, Ritual fosters a dynamic marketplace where participants can offer specialized resources, catering to different compute needs efficiently. This multi-class node structure allows for optimized pricing based on performance criteria, offering flexibility and scalability for diverse applications and user requirements.
This week, Anna and Tarun chat with Niraj Pant and Anish Agnihotri from Ritual. They kick off by revisiting the AIxCrypto intersection before diving into the Ritual product and its goals around developing open access AI infrastructure. They explore the opportunities that open up when you bring ML to smart contracts.
zkSummit11 happens in 2 weeks, if you haven’t got your tickets yet head over to the zkSummit website to apply now. The event will be held on 10 April in Athens, Greece.
