Principles of DeAI
Decentralized AI focuses on the execution of AI tasks across a distributed infrastructure rather than centralized data centers. The technical objective is to improve system resilience and mitigate the risks associated with centralized compute control.
Core Initiatives
- Bittensor: A protocol utilizing market-based incentives to coordinate the production of machine intelligence across a network of independent nodes.
- Petals (GitHub): A framework for collaborative inference and fine-tuning of large-scale models by distributing parameter shards across consumer-grade hardware.
- Federated Learning: A distributed machine learning approach that enables model training on local datasets without data exchange, preserving data privacy.
Precursors & Distributed Computing
- SETI@home: A pioneer in volunteer computing that demonstrated the feasibility of utilizing millions of home computers for massive signal processing.
- Folding@home: A distributed computing project for simulating protein dynamics, showcasing the power of P2P resources for scientific research.
- IPFS (InterPlanetary File System): A protocol for decentralized data storage that is often used as the storage layer for decentralized AI assets.
- Golem Network: A decentralized marketplace for computing power, targeting generalized rendering and AI workloads.
Operational Goal
The implementation of DeAI provides a governance layer for autonomous systems. Utilizing decentralized consensus protocols facilitates Epistemic Redundancy, where decisions are validated by multiple independent entities, reducing single-node bias and failure risks.