AI + Blockchain
Intersection of artificial intelligence and blockchain
Key Glossary Terms
Essential vocabulary for this topic
AI Agent
intermediateSoftware that uses artificial intelligence to perceive information, make decisions, and perform actions toward defined objectives.
AI Provenance
advancedMethods for recording and verifying the origin, history, authorship, training context, or transformations of AI models and outputs.
Agentic Economy
advancedAn emerging economic model where autonomous software agents discover services, transact, negotiate, or coordinate with limited human intervention.
Autonomous Agent
advancedA software agent capable of planning and executing actions with limited direct human control.
Decentralized AI
intermediateAI systems whose models, compute, data, incentives, governance, or ownership are distributed across multiple participants.
Decentralized Compute
intermediateComputing capacity supplied and coordinated across independent providers, often using blockchain settlement or incentives.
Research Papers
Academic and industry research
Fundamentals of Decentralized AI
Binance Research
This technical report from Binance Research outlines the foundational pillars of the intersection between artificial intelligence and blockchain technology. It defines decentralized AI as a system where AI models, training data, and compute resources are managed by a distributed network rather than a single entity. The report examines three distinct layers: the compute layer (decentralized GPU networks), the data layer (decentralized storage and labeling), and the model layer (decentralized model weights and training). It provides a high-level overview of zero-knowledge proofs (ZKP) as a mechanism for verifying AI model integrity without revealing private training data. The research notes that the main value proposition of decentralized AI lies in censorship resistance, democratized access to compute, and global collaboration on open-source AI. The report serves as a primer for understanding the infrastructural demands of the emerging Web3 AI ecosystem and highlights potential regulatory challenges regarding data privacy and jurisdictional liability in global decentralized networks.
The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead
Anonymous
This paper presents a state-of-the-art overview of the convergence between artificial intelligence and blockchain technology, exploring how these two transformative technologies can form a symbiotic relationship. The authors discuss how blockchain provides the ledger for decentralized AI systems, facilitating transparent model training, secure data sharing, and immutable recording of model results. The paper also explores the integration of autonomous agents in Web3, where smart contracts act as the 'legal' and 'operational' interfaces for AI decision-making. Through a review of current implementations, the research highlights the challenges of interoperability and standard-setting in a nascent industry. The findings suggest that the integration is moving towards 'intelligent decentralized applications' (iDApps) where AI handles complex logic on-chain. The paper advocates for standardized frameworks to ensure that AI agents behave predictably when interacting with smart contract protocols, emphasizing the necessity of interoperability between different blockchain ecosystems and AI model formats to achieve scale.
AI-enhanced blockchain technology: A review of advancements and opportunities
D. Ressi
This review paper offers a comprehensive survey of the convergence between artificial intelligence and blockchain technology. The author categorizes recent advancements into three main domains: AI-driven optimization of blockchain consensus mechanisms, AI-enhanced smart contract security, and the development of decentralized AI marketplaces. The paper synthesizes literature to explain how machine learning algorithms can predict mining difficulty, detect anomalies in transaction patterns, and optimize shard management in scaling solutions. Furthermore, it addresses the technical challenges of reconciling AI's computational requirements with the resource-constrained environment of blockchain nodes. The research findings indicate that AI can significantly reduce the energy consumption of Proof-of-Work systems through predictive resource allocation. The paper concludes by highlighting future research trajectories, specifically focusing on privacy-preserving machine learning and the integration of federated learning models within decentralized networks. It serves as a foundational resource for researchers looking to understand the interplay between autonomous agents and distributed ledgers.