Fundamentals of Decentralized AI
Binance Research
Abstract
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.
Key Findings
- 1Decentralized AI architecture is best understood via its compute, data, and model layers.
- 2Zero-knowledge proofs facilitate verifiable computing, a critical component for AI in Web3.
- 3Democratized access to high-end GPUs is the primary driver for decentralized compute networks.
- 4Censorship resistance is a key motivator for the development of open-source decentralized AI models.
Topics
Citation
BibTeX
@misc{fundamentalsof2024,
title = {Fundamentals of Decentralized AI},
author = {Binance Research},
year = {2024},
howpublished = {\url{https://public.bnbstatic.com/static/files/research/fundamentals-of-decentralized-ai.pdf}},
}Knowledge Explorer
Explore Related Concepts
See how Fundamentals of Decentralized AI connects to glossary terms, books, and other research in the Knowledge Graph.