What is Decentralized AI (DeAI)?

Decentralized AI (DeAI) refers to the integration of artificial intelligence with blockchain and decentralized technologies. It encompasses a spectrum of applications, from tokenized AI marketplaces to running AI models directly on decentralized networks as smart contracts. The core idea is to move away from centralized control of AI models, data, and compute, instead leveraging distributed networks for greater transparency, security, and trust1 2.

Key Features and Layers of DeAI

Fully Onchain AI: The most robust form of DeAI involves both training and inference of AI models happening entirely on decentralized networks, providing the same security and trust guarantees as blockchain smart contracts1.
Onchain Inference: Models are trained off-chain but deployed on decentralized networks for inference, ensuring verifiability and trust in the results1.
Decentralized Training: Platforms enable collaborative, distributed training of AI models across multiple nodes, reducing reliance on centralized data centers2 3.
Decentralized Data and Compute: Data storage and computational resources are provided by decentralized networks, making AI development more open and censorship-resistant2 3.
Infrastructure Protocols: Foundational protocols support the operation and coordination of decentralized AI systems, ensuring interoperability and scalability2 3.

Example Ecosystem Layers and Projects

Layer	Example Projects	Description
Decentralized Training	Pluralis Research, Nous Research, Gensyn	Distributed model training frameworks
Data Providers & Storage	Filecoin, Arweave, Masa	Decentralized data storage and access
Compute Providers	Aethir, io.net, Akash	Distributed compute resources for AI workloads
Infrastructure Protocols	Bittensor, ASI, Ritual	Protocols for coordination and interoperability

Why DeAI?

Transparency & Trust: By running AI on decentralized infrastructure, results and processes can be audited and verified by anyone1.
Security & Privacy: Decentralized systems can enhance data privacy and reduce single points of failure1.
Open Collaboration: Anyone can contribute data, compute, or models, fostering innovation and reducing barriers to entry2 3.

Applications

Decentralized AI marketplaces
Onchain AI-powered dApps
Verifiable and auditable AI services
Decentralized physical AI (robots, IoT, spatial intelligence)4 5

DeAI is a rapidly evolving field, with new projects and protocols emerging to address the challenges of decentralizing AI development, deployment, and governance2 3 4 5+1.

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