Artificial intelligence (AI) has become a common term in everydays lingo, while blockchain, though often seen as distinct, is gaining prominence in the tech world, especially within the Finance space. Concepts like "AI Blockchain," "AI Crypto," and similar terms highlight the convergence of these two powerful technologies. Though distinct, AI and blockchain are increasingly being combined to drive innovation, complexity, and transformation across various industries.

The integration of AI and blockchain is creating a multi-layered ecosystem with the potential to revolutionize industries, enhance security, and improve efficiencies. Though both are different and polar opposite of each other. But, De-Centralisation of Artificial intelligence quite the right thing towards giving the authority to the people.

The Whole Decentralized AI ecosystem can be understood by breaking it down into three primary layers: the Application Layer, the Middleware Layer, and the Infrastructure Layer. Each of these layers consists of sub-layers that work together to enable the seamless creation and deployment of AI within blockchain frameworks. Let's Find out How These Actually Works......

TL;DR

  • Application Layer: Users interact with AI-enhanced blockchain services in this layer. Examples include AI-powered finance, healthcare, education, and supply chain solutions.

  • Middleware Layer: This layer connects applications to infrastructure. It provides services like AI training networks, oracles, and decentralized agents for seamless AI operations.

  • Infrastructure Layer: The backbone of the ecosystem, this layer offers decentralized cloud computing, GPU rendering, and storage solutions for scalable, secure AI and blockchain operations.

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💡Application Layer

The Application Layer is the most tangible part of the ecosystem, where end-users interact with AI-enhanced blockchain services. It integrates AI with blockchain to create innovative applications, driving the evolution of user experiences across various domains.

  •  User-Facing Applications:

  1.    AI-Driven Financial Platforms: Beyond AI Trading Bots, platforms like Numerai leverage AI to manage decentralized hedge funds. Users can contribute models to predict stock market movements, and the best-performing models are used to inform real-world trading decisions. This democratizes access to sophisticated financial strategies and leverages collective intelligence.

  2. AI-Powered Decentralized Autonomous Organizations (DAOs): DAOstack utilizes AI to optimize decision-making processes within DAOs, ensuring more efficient governance by predicting outcomes, suggesting actions, and automating routine decisions.

  3. Healthcare dApps: Doc.ai is a project that integrates AI with blockchain to offer personalized health insights. Patients can manage their health data securely, while AI analyzes patterns to provide tailored health recommendations.

  4. Education Platforms: SingularityNET and Aletheia AI have been pioneering in using AI within education by offering personalized learning experiences, where AI-driven tutors provide tailored guidance to students, enhancing learning outcomes through decentralized platforms.

  • Enterprise Solutions:

  1. AI-Powered Supply Chain: Morpheus.Network utilizes AI to streamline global supply chains. By combining blockchain's transparency with AI's predictive capabilities, it enhances logistics efficiency, predicts disruptions, and automates compliance with global trade regulations.

  2.  AI-Enhanced Identity Verification: Civic and uPort integrate AI with blockchain to offer advanced identity verification solutions. AI analyzes user behavior to detect fraud, while blockchain ensures that personal data remains secure and under the control of the user.

  3. Smart City Solutions: MXC Foundation leverages AI and blockchain to optimize urban infrastructure, managing everything from energy consumption to traffic flow in real-time, thereby improving efficiency and reducing operational costs.

🏵️ Middleware Layer

The Middleware Layer connects the user-facing applications with the underlying infrastructure, providing essential services that facilitate the seamless operation of AI on the blockchain. This layer ensures interoperability, scalability, and efficiency.

  • AI Training Networks:

Decentralized AI training networks on blockchain combine the power of artificial intelligence with the security and transparency of blockchain technology. In this model, AI training data is distributed across multiple nodes on a blockchain network, ensuring data privacy, security, and preventing data centralization.

  1. Ocean Protocol: This protocol focuses on democratizing AI by providing a marketplace for data sharing. Data providers can monetize their datasets, and AI developers can access diverse, high-quality data for training their models, all while ensuring data privacy through blockchain.

  2. Cortex: A decentralized AI platform that allows developers to upload AI models onto the blockchain, where they can be accessed and utilized by dApps. This ensures that AI models are transparent, auditable, and tamper-proof.

  3.  Bittensor: The case of a sublayer class for such an implementation can be seen with Bittensor. It's a decentralized machine learning network where participants are incentivized to put in their computational resources and datasets. This network is underlain by the TAO token economy that rewards contributors according to the value they add to model training. This democratized model of AI training is, in actuality, revolutionizing the process by which models are developed, making it possible even for small players to contribute and benefit from leading-edge AI research.

  •  AI Agents and Autonomous Systems:

In this sublayer, the focus is more on platforms that allow the creation and deployment of autonomous AI agents that are then able to execute tasks in an independent manner. These interact with other agents, users, and systems in the blockchain environment to create a self-sustaining AI-driven process ecosystem.

  1. SingularityNET: A decentralized marketplace for AI services where developers can offer their AI solutions to a global audience. SingularityNET’s AI agents can autonomously negotiate, interact, and execute services, facilitating a decentralized economy of AI services.

  2. iExec: This platform provides decentralized cloud computing resources specifically for AI applications, enabling developers to run their AI algorithms on a decentralized network, which enhances security and scalability while reducing costs.

  3.  Fetch.AI: One class example of this sub-layer is Fetch.AI, which acts as a kind of decentralized middleware on top of which fully autonomous "agents" represent users in conducting operations. These agents are capable of negotiating and executing transactions, managing data, or optimizing processes, such as supply chain logistics or decentralized energy management. Fetch.AI is setting the foundations for a new era of decentralized automation where AI agents manage complicated tasks across a range of industries.

  •   AI-Powered Oracles:

Oracles are very important in bringing off-chain data on-chain. This sub-layer involves integrating AI into oracles to enhance the accuracy and reliability of the data which smart contracts depend on.

  1. Oraichain: Oraichain offers AI-powered Oracle services, providing advanced data inputs to smart contracts for dApps with more complex, dynamic interaction. It allows smart contracts that are nimble in data analytics or machine learning models behind contract execution to relate to events taking place in the real world.

  2.  Chainlink: Beyond simple data feeds, Chainlink integrates AI to process and deliver complex data analytics to smart contracts. It can analyze large datasets, predict outcomes, and offer decision-making support to decentralized applications, enhancing their functionality.

  3.  Augur: While primarily a prediction market, Augur uses AI to analyze historical data and predict future events, feeding these insights into decentralized prediction markets. The integration of AI ensures more accurate and reliable predictions.

⚡ Infrastructure Layer

The Infrastructure Layer forms the backbone of the Crypto AI ecosystem, providing the essential computational power, storage, and networking required to support AI and blockchain operations. This layer ensures that the ecosystem is scalable, secure, and resilient.

  •  Decentralized Cloud Computing:

The sub-layer platforms behind this layer provide alternatives to centralized cloud services in order to keep everything decentralized. This gives scalability and flexible computing power to support AI workloads. They leverage otherwise idle resources in global data centers to create an elastic, more reliable, and cheaper cloud infrastructure.

  1.   Akash Network: Akash is a decentralized cloud computing platform that shares unutilized computation resources by users, forming a marketplace for cloud services in a way that becomes more resilient, cost-effective, and secure than centralized providers. For AI developers, Akash offers a lot of computing power to train models or run complex algorithms, hence becoming a core component of the decentralized AI infrastructure. 

  2. Ankr: Ankr offers a decentralized cloud infrastructure where users can deploy AI workloads. It provides a cost-effective alternative to traditional cloud services by leveraging underutilized resources in data centers globally, ensuring high availability and resilience.

  3. Dfinity: The Internet Computer by Dfinity aims to replace traditional IT infrastructure by providing a decentralized platform for running software and applications. For AI developers, this means deploying AI applications directly onto a decentralized internet, eliminating reliance on centralized cloud providers.

  •  Distributed Computing Networks:

This sublayer consists of platforms that perform computations on a global network of machines in such a manner that they offer the infrastructure required for large-scale workloads related to AI processing.

  1.   Gensyn: The primary focus of Gensyn lies in decentralized infrastructure for AI workloads, providing a platform where users contribute their hardware resources to fuel AI training and inference tasks. A distributed approach can ensure the scalability of infrastructure and satisfy the demands of more complex AI applications.

  2.  Hadron: This platform focuses on decentralized AI computation, where users can rent out idle computational power to AI developers. Hadron’s decentralized network is particularly suited for AI tasks that require massive parallel processing, such as training deep learning models.

  3.  Hummingbot: An open-source project that allows users to create high-frequency trading bots on decentralized exchanges (DEXs). Hummingbot uses distributed computing resources to execute complex AI-driven trading strategies in real-time.

  • Decentralized GPU Rendering:

In the case of most AI tasks, especially those with integrated graphics, and in those cases with large-scale data processing, GPU rendering is key. Such platforms offer a decentralized access to GPU resources, meaning now it would be possible to perform heavy computation tasks that do not rely on centralized services.

  1. Render Network: The network concentrates on decentralized GPU rendering power, which is able to do AI tasks—to be exact, those executed in an intensely processing way—neural net training and 3D rendering. This enables the Render Network to leverage the world's largest pool of GPUs, offering an economic and scalable solution to AI developers while reducing the time to market for AI-driven products and services.

  2.  DeepBrain Chain: A decentralized AI computing platform that integrates GPU computing power with blockchain technology. It provides AI developers with access to distributed GPU resources, reducing the cost of training AI models while ensuring data privacy.

  3.   NKN (New Kind of Network): While primarily a decentralized data transmission network, NKN provides the underlying infrastructure to support distributed GPU rendering, enabling efficient AI model training and deployment across a decentralized network.

  • Decentralized Storage Solutions:

The management of vast amounts of data that would both be generated by and processed in AI applications requires decentralized storage. It includes platforms in this sublayer, which ensure accessibility and security in providing storage solutions.

  1. Filecoin : Filecoin is a decentralized storage network where people can store and retrieve data. This provides a scalable, economically proven alternative to centralized solutions for the many times huge amounts of data required in AI applications. At best. At best, this sublayer would serve as an underpinning element to ensure data integrity and availability across AI-driven dApps and services.

  2.  Arweave: This project offers a permanent, decentralized storage solution ideal for preserving the vast amounts of data generated by AI applications. Arweave ensures data immutability and availability, which is critical for the integrity of AI-driven applications.

  3.  Storj: Another decentralized storage solution, Storj enables AI developers to store and retrieve large datasets across a distributed network securely. Storj’s decentralized nature ensures data redundancy and protection against single points of failure.

🟪 How Specific Layers Work Together? 

  • Data Generation and Storage:

    Data is the lifeblood of AI. The Infrastructure Layer’s decentralized storage solutions like Filecoin and Storj ensure that the vast amounts of data generated are securely stored, easily accessible, and immutable. This data is then fed into AI models housed on decentralized AI training networks like Ocean Protocol or Bittensor.

  • AI Model Training and Deployment: The Middleware Layer, with platforms like iExec and Ankr, provides the necessary computational power to train AI models. These models can be decentralized using platforms like Cortex, where they become available for use by dApps.

  •  Execution and Interaction: Once trained, these AI models are deployed within the Application Layer, where user-facing applications like ChainGPT and Numerai utilize them to deliver personalized services, perform financial analysis, or enhance security through AI-driven fraud detection.

  • Real-Time Data Processing: Oracles in the Middleware Layer, like Oraichain and Chainlink, feed real-time, AI-processed data to smart contracts, enabling dynamic and responsive decentralized applications.

  • Autonomous Systems Management: AI agents from platforms like Fetch.AI operate autonomously, interacting with other agents and systems across the blockchain ecosystem to execute tasks, optimize processes, and manage decentralized operations without human intervention.

🔼 Data Credit

> Binance Research

> Messari

> Blockworks

> Coinbase Research

> Four Pillars

> Galaxy

> Medium