1.4 Devolved AI’s Unique Selling Points (USPs)

Contributions with Impact:

Devolved AI transcends traditional contribution models by empowering stakeholders to actively shape the platform. Users are encouraged to provide feedback to AI models, enhancing the learning process and directly influencing model development. This collaborative environment ensures that every contribution, regardless of scale, is recognized, quantified, and rewarded, fostering a continuous exchange of value within the community.

Proof of Value (PoV) Protocol:

At the heart of Devolved AI lies the groundbreaking PoV protocol, a dynamic system for measuring and rewarding diverse contributions. PoV acknowledges a wide range of activities, including AI model training, data validation, computational resource provision, governance participation, and community growth efforts. This innovative algorithm meticulously evaluates and rewards each contribution, ensuring a fair and transparent ecosystem where every effort is valued.

Reinforcement Learning from Human Feedback (RLHF):

RLHF is pivotal in evolving our AI models, including Athena, to align with human values and expectations. By integrating human insights directly into the AI learning process, RLHF refines Athena's decision-making capabilities, ensuring the development of more intuitive and user-aligned AI models. This method enhances the AI’s ability to reflect human values and preferences, resulting in better and more ethical AI solutions.

Federated Learning and Distributed Training System:

Our Federated Learning and Distributed Training System leverage decentralized GPU resources from the community, enabling collaborative and privacy-preserving AI model training. This hybrid approach allows us to utilize a global network of potentially tens of millions of GPUs, accelerating the learning process and democratizing AI development. This system enables anyone to contribute to training our central AI while also allowing users to fine-tune the core model using their proprietary data. By maintaining data privacy and security, users can create specialized models tailored to their needs.

Transparent and Trustworthy Data Storage:

Utilizing the decentralized Stratos infrastructure, Devolved AI ensures that all training data is stored with utmost transparency and security. Cryptographic hashes stored on Argochain provide immutable verification of data integrity, enhancing trust within the community and ensuring that all data used in training is verifiable and secure.

Integration of Athena / Athena2 into Layer-1:

Athena, our advanced NLP engine, is seamlessly integrated into Devolved AI's Layer-1 blockchain. This integration enables developers to leverage Athena for creating decentralized applications (dApps) and services, making cutting-edge NLP capabilities accessible to all and fostering innovation within the ecosystem.

Governance and DAO Structure:

Devolved AI's governance model empowers AGC token holders to actively participate in shaping the platform's future. The decentralized autonomous organization (DAO) structure ensures collaborative decision-making and a shared sense of ownership, allowing the community to steer the direction of the platform democratically.

EVM (Ethereum Virtual Machine) Compatibility:

Argochain's EVM compatibility extends our ecosystem's accessibility to Ethereum's vast developer community and rich suite of development tools. This compatibility enhances interoperability and fosters innovation within our platform by enabling seamless integration with existing Ethereum-based solutions.

Network Manager:

Our custom network manager ensures the decentralized distribution of resources across all participants in the Devolved AI ecosystem. This tool optimizes the use of decentralized GPU resources, ensuring efficient and fair resource allocation to support the continuous development and deployment of AI models and applications.