Three dragons playing in the water: Dynex, the Panlong, handles the aggregation and output of computing power, Bittensor, the Chuanyunlong, handles the collaboration between AIs, and Commune, the Jiulong, handles the connection between AI and blockchain.
References below: @ShogunMasterRoy @fififilin
Meet the pioneer of blockchain-machine learning integration
Dynex $DNX : Leveraging decentralized technology to virtualize GPUs into neural networks that can perform tasks at unprecedented speed and efficiency, in some cases even surpassing quantum computing. It emphasizes solving real-world problems with unlimited use cases.
Bittensor $TAO : Introducing a decentralized network dedicated to machine learning, focused on creating global neural networks. It emphasizes incentivizing data sharing and model training across distributed ledgers.
Commune AI $COM : Proposes a modular framework for ML that emphasizes the interoperability and reusability of ML models. It aims to create a more open and collaborative machine learning environment, moving away from a platform-centric model.
Technical architecture: Laying the foundation
Dynex $DNX : Made possible by a proprietary neuromorphic superchip algorithm called DynexSolve, which leverages GPUs and compute networks to solve real-world problems with unlimited use cases.
Bittensor $TAO: Built around a unique neural blockchain network. It utilizes specialized nodes (neurons) to communicate and collaborate on machine learning tasks, incentivized by a custom token system.
Commune AI $COM : Developed around the "Modulus" framework, it focuses on modular and interoperable ML components. This allows for greater flexibility and scalability when integrating various machine learning tools and environments.
Module/node structure: core unit
Dynex $DNX :Each GPU miner runs independently and contributes to the entire machine learning process. Miners are rewarded based on their computing power contribution.
Bittensor $TAO ：Each node in the network operates independently and contributes to the overall machine learning process. Nodes are rewarded based on their contributions, thus fostering a competitive yet cooperative ecosystem.
Commune AI $COM : Introduces modular blocks as core units that are highly general and support multiple inputs and outputs. This encourages the development of more adaptable and scalable machine learning models.
Data Management and Storage: Protecting and Utilizing Information
Dynex $DNX : The customer data is AES-256 encrypted and submitted to the Dynex platform. The data is split according to the size of the customer data (e.g. 2TB) and the required number of epochs is used to obtain good training results. Since the number of training cycles required for the neuromorphic layer is very small, the total running time of the training will be very short compared to training on a GPU.
For example, a QRBM can be trained where epoch 1 is trained using 1/10 of the data, epoch 2 is trained using the next 1/10 of the data, and so on.
Bittensor $TAO ：Focused on decentralized data storage and management, leveraging the inherent security features of blockchain. This ensures data integrity and accessibility across the network.
Commune AI $COM : Provides a powerful file system for modules, enabling organized and efficient data management. This enhances the deployment and maintenance process of machine learning modules.
API and user interaction: connecting users and technology
Dynex $DNX : Provides a computing power market operation interface that is compatible with github CodeSpace. It is convenient for users to easily access and complete ML.
Bittensor $TAO : Provides a unique API for interacting with neural blockchain networks, allowing users to easily access and contribute to ML processes.
Commune AI $COM: Features a comprehensive Modulus manager API for supervising module activities, providing a user-friendly and intuitive interface for managing ML operations.
Modulus: Open Modular Design
Modulus allows users to wrap any machine learning or processing using its modular blocks.
Modules can use the queue server to connect locally or over the wire to put and get objects from the global queue.
Modulus API allows external users to run module actors locally via JSON gRPC.
· Developers can create custom payment models that fit their use cases and can charge any token they want. · Modulus uses python libraries to interact with different blockchains, allowing smart contracts to interact with other non-smart contract modules. Modules can also connect different smart contracts across different chains, allowing python developers to customize cross-chain synchronization.
Commune currently has nearly 50 modules, and anyone can create them for free without registering on the chain.
Interoperability and Connectivity: Extending the Network
Dynex $DNX : Focused on neural network computing power. Cooperated extensively with third-party applications. Has cooperated with medical service agreement ETECA.
Bittensor $TAO : Each neuron/node in the network can connect and communicate with other neurons/nodes to form a cohesive and dynamic ML network.
Commune AI $COM: Emphasizes seamless connections between local and remote modules, promoting a more collaborative and interactive ML environment. Supports modular architecture and encourages code reuse. (Each module is represented as a folder containing the main python script and its configuration file. The module folder can be built into a single file system representing the module ontology/tree, which can be easily reused and shared). Supports horizontal expansion, developers can easily expand by adding instances or nodes, and take advantage of the scalability and elasticity of cloud resources to adapt to different workloads.
Security and governance: protecting the ecosystem
Dynex $DNX :DynexSolve’s proprietary neuromorphic superchip algorithm is patent pending.
Bittensor $TAO : Implement strong security measures to protect the integrity of neural networks and their data.
Commune AI $COM : Focuses on secure module access control and implements smart contracts to ensure compliance and governance in module interactions.
Token Economics and Incentive Structures: Driving Participation
Dynex $DNX: DNX is the native currency of the system, with no pre-mining and no ICO. 70% of the computing fees paid by commercial customers are injected into the block for miners to mine.
Bittensor $TAO: TAO token is the core of the ecosystem, incentivizing nodes to contribute to the ML network. The $TAO token plays a central role in its ecosystem. It incentivizes nodes (neurons) to contribute to the machine learning process, creating a token-based economy that rewards data sharing and model training. This approach encourages active participation, but may also bring challenges related to token distribution and value stability.
Commune AI $COM: While its core operations do not rely on a native token, it allows developers to monetize their modules and interactions in a variety of ways, providing economic incentives for participation.
$COM's approach to token economics is less direct, as its core operations do not rely on a native token. However, it allows for the monetization of modules and interactions, which can provide economic incentives for developers and contributors. This approach focuses more on the utility and market value of individual modules rather than a centralized token economy.
Scalability and Future Prospects: Looking Ahead
Dynex $DNX :Turn your computer or dormant GPU mining rig into a neuromorphic machine and earn POUW benefits: Let's solve real-world problems through mining together.
Bittensor $TAO : Aims to scale its neural networks to create a truly global and decentralized machine learning ecosystem.
Commune AI $COM : Focused on expanding its modular framework to accommodate a wider range of ML tools and applications, aiming for a more open and collaborative ML future.
Conclusion: Dynex $DNX, Bittensor $TAO, and Commune AI $COM all represent significant advancements in the integration of blockchain and machine learning.
Dynex decentralizes computing power, making mining no longer a waste of energy; Bittensor focuses on creating open source collaboration that incentivizes AI through token economy; and Commune AI emphasizes a more open and modular approach to provide flexibility and scalability for ML development. All three projects have the potential to revolutionize the way machine learning and blockchain technologies merge and interact.
