Harness the collective intelligence of a distributed network to unlock unparalleled AI capabilities.
Revolutionizing AI with the Power of Web3
Harness the collective intelligence of a distributed network to unlock unparalleled AI capabilities.
Why and how?
Decentralization and Ownership
Decentralization and Ownership
Decentralization and Ownership
A decentralized LLM promotes a more democratic access to powerful AI tools. It moves away from the model where a few large corporations own and control the most advanced AI technologies
Privacy and Security
Decentralization and Ownership
Decentralization and Ownership
By leveraging blockchain technology, the system can offer enhanced data privacy and security features, protecting user data and ensuring that the model's training data is not misused
Economic Model
Decentralization and Ownership
Decentralized Infrastructure
The use of tokens to incentivize data contribution and to manage access and usage creates a self-sustaining economic model. It can reward contributors fairly and ensure the long-term viability of the project
Decentralized Infrastructure
Decentralized Infrastructure
Decentralized Infrastructure
At its core, the system would be built on a decentralized network, possibly using a blockchain or a distributed ledger technology (DLT). Each node in the network could host a part of the LLM, contributing to processing power and storage. Smart contracts would manage the operation of the model, including updates, access permissions, and usage tracking.
Data Collection and Training
Decentralized Infrastructure
Data Collection and Training
Data collection could be community-driven, with users contributing data to train the model. Contributions could be incentivized through token rewards. Data privacy can be ensured through encryption and by using zero-knowledge proofs to verify data integrity without exposing the data itself.
Model Training and Updates
Decentralized Infrastructure
Data Collection and Training
The training process would be distributed among nodes. It will be achieved through federated learning, where the model is trained locally on nodes and only the model updates (and not the training data) are shared across the network. Smart contracts automate the aggregation of the updates in order to improve the model.