ALL IN Crypto AI (2) In-depth Analysis of Key Subfields of Crypto AI
1. Decentralized Computing: Training, Inference, and GPU Market
(1) Importance of Decentralized Computing
Decentralized computing is a key foundation for the development of crypto artificial intelligence, providing the necessary computing resources for training and inference. Its decentralized nature helps to break the monopoly of traditional computing resources, improve the utilization efficiency and flexibility of computing resources, and enhance the system's resistance to censorship.
(2) GPU Market
Market Landscape and Potential
Currently, the GPU market shows a unique development trend in the field of crypto artificial intelligence. On one hand, there are centralized GPU leasing platforms, but they face numerous problems such as a lack of innovative incentive mechanisms, opaque pricing, and limited regional services. On the other hand, a decentralized GPU market is emerging, which can integrate idle GPU resources globally and is expected to provide users with more cost-effective computing services.
Challenges and Solutions
Despite the huge total idle GPU computing power, the actual utilization rate is low, facing the challenge of supply-demand matching. Some decentralized GPU market projects attract GPU providers through innovative token incentive mechanisms, while using smart contracts to achieve automated trading and resource allocation, improving market transparency and efficiency. However, ensuring the quality, stability, and data security of GPU computing power remains urgent issues to be addressed.
(3) Decentralized Training
Current Status and Advantages
Decentralized training is still in its early development stage but has shown great potential. It allows different nodes to participate in the model training process, accelerating the training process through distributed computing, and can utilize diverse data sources to enhance the model's generalization ability. Furthermore, decentralized training can reduce reliance on a single data center, enhancing the robustness of the system.
Technical Challenges and Development Trends
Achieving efficient decentralized training faces many technical challenges, such as data synchronization, model aggregation, and communication overhead. Currently, some projects are exploring solutions based on blockchain technology, leveraging its distributed ledger and encryption features to ensure the security and traceability of the training process. In the future, as technology continues to advance, decentralized training is expected to play a more important role in handling large-scale complex models.
(4) Decentralized Inference
Development Trends and Opportunities
As the application of artificial intelligence continues to spread, the demand for computing in the inference stage is increasing, leading to the emergence of decentralized inference. It can distribute inference tasks across multiple nodes for parallel processing, effectively reducing latency and improving system response speed, especially suitable for real-time applications such as autonomous driving and smart security.
Problems Faced and Response Strategies
However, decentralized inference also faces some issues in practical applications, such as node reliability, consistency of computational results, and privacy protection. To address these issues, more advanced cryptographic algorithms and consensus mechanisms need to be developed, while strengthening the management and supervision of nodes to ensure the security and reliability of the entire inference process.
2. Data Networks (Data networks)
(1) Key Role of Data Networks
In the field of crypto artificial intelligence, data is the core element driving model training and optimization, and data networks serve as a bridge connecting data providers and users. They facilitate the sharing, circulation, and realization of data value, providing artificial intelligence models with richer and more diverse data resources, thereby improving model accuracy and performance.
(2) Architecture and Functionality of Data Networks
Data Storage and Management
Data networks typically use distributed storage technology to store data across multiple nodes, ensuring data security and availability. At the same time, by establishing efficient data indexing and retrieval mechanisms, users can quickly access the data they need.
Data Trading and Incentive Mechanisms
To encourage data providers to actively participate in data sharing, data networks often introduce token incentive mechanisms. Data providers can earn corresponding token rewards by selling or authorizing the use of their data, while data users need to pay tokens to access the data. This mechanism helps establish a fair and active data market.
(3) Data Privacy and Security
Data privacy protection is an important challenge faced by data networks. During the process of data sharing and trading, advanced encryption technologies such as zero-knowledge proofs and homomorphic encryption need to be employed to ensure that data privacy is not compromised. At the same time, data networks need to establish strict security management systems to prevent data from being tampered with, stolen, or misused.
3. Verifiable AI
(1) Concept and Significance of Verifiable AI
As the complexity of artificial intelligence models continues to increase, the transparency and interpretability of their decision-making processes and outcomes become increasingly important. Verifiable AI aims to enable users to understand and verify the behavior of artificial intelligence models through technical means, ensuring the fairness, reliability, and security of their decisions.
(2) Technical Means to Achieve Verifiable AI
Model Interpretation Technology
Including feature importance analysis, decision tree visualization, attention mechanisms, etc. These technologies can help users understand the key factors that the model relies on when making decisions, thereby enhancing trust in the model.
Formal Verification Methods
Using mathematical methods to rigorously prove the correctness of models, ensuring that models operate as expected under various circumstances to avoid errors or deviations.
(3) Application Scenarios of Verifiable AI in Crypto AI
In the financial sector, verifiable AI can be used for credit risk assessment, investment decision-making, etc., allowing investors to clearly understand the decision-making basis of the model, thereby reducing investment risks. In healthcare, doctors can understand the reasoning process of diagnostic models through verifiable AI technology, improving the accuracy and credibility of diagnoses. In the field of smart contracts, verifiable AI can ensure the fairness and security of contract execution, preventing malicious attacks and exploitation of vulnerabilities.
4. On-chain AI Agents (AI Agents living on-chain)
(1) Definition and Characteristics of On-chain AI Agents
On-chain AI agents are intelligent entities operating on the blockchain; they can autonomously perceive the environment, make decisions, and interact with other agents or users. These agents possess blockchain characteristics such as decentralization, transparency, and immutability, while also having the intelligent decision-making capabilities of artificial intelligence, enabling them to perform automated task execution and value creation in complex environments.
(2) Application Scenarios of On-chain AI Agents
On-chain AI agents can be used in automated trading, smart contract management, market prediction, and other areas. For instance, in automated trading, AI agents can automatically execute buy and sell trades based on market data and preset strategies; in smart contract management, they can monitor contract statuses and automatically execute contract terms, improving the efficiency and reliability of contract execution.
Currently, the hottest feature is still the rapid token issuance function:
One notable use of AI Agents in the blockchain and cryptocurrency field is the rapid issuance of tokens. These functionalities include:
Automated Token Creation: Through AI Agents, new tokens can be automatically created based on user needs in a short time (for example, within minutes). Users only need to provide some basic information, such as token name, token symbol, total supply, etc., and the AI Agent can handle the remaining processes, including deploying the token's smart contract.
No Coding Required: Many AI Agent platforms are designed to require no programming knowledge or blockchain development experience from users, significantly lowering the entry barrier.
Liquidity Pool Creation: AI Agents can automatically set up liquidity pools, for example, on Uniswap, to provide initial liquidity for newly issued tokens. This helps tokens start trading in the market.
Social Media Integration: Some AI Agents can automatically post information about new tokens on social media platforms, leveraging the attention economy of platforms like X to promote tokens.
For example, on the Base blockchain, the most popular and common applications of AI agents include:
Virtual ($VIRTUAL):
Virtual is a platform focused on AI agents, aiming to create a metaverse and gaming ecosystem where AI agents can act autonomously. The project provides an environment for developers to create and deploy their own AI agents. These agents can perform tasks and transactions on-chain. Virtual has become a very popular AI agent project within the Base ecosystem.
Clanker ($CLANKER):
Although Clanker is more of a project centered around meme concepts, it incorporates elements of AI agents, allowing users to post on the social media platform Farcaster through AI agents while simultaneously minting tokens. This is an innovative attempt that combines AI, blockchain, and social interaction.
Spectral Labs ($SPEC):
Spectral Labs is an AI agent platform that supports the creation and management of AI agents capable of performing various on-chain operations. $SPEC is the governance token of its ecosystem, which has gained significant attention recently, and the token price has correspondingly increased.
The current AI agent ecosystem on the @base platform is very comprehensive. This ecosystem includes multiple AI agent projects, such as $VIRTUAL, $AIXBT, $LUNA, etc., which are managed by different teams, such as @virtuals_io, @aixbt_agent, @luna_virtuals, etc. Additionally, there are AI agent projects on the Simulacrum platform, such as $EMP, $SIMMI, $CHAOS, etc. There are also AI agents in other sections of the website, such as $HENLO, $FAI, $AGENT, etc.
These projects demonstrate various applications of AI agents within the Base ecosystem, ranging from gaming to social media interaction, and even to complex economic and governance models. They provide examples of how AI agents can function in practical applications by leveraging the characteristics of Base, such as low fees and fast transactions. Although the popularity and specific implementations of these projects may vary, they currently represent the most active application directions for AI agents on Base.
Other Uses:
AI Agents also have wide applications in other fields:
Customer Service: AI Agents can handle customer inquiries, support requests, sales, and marketing activities, providing personalized customer experiences.
Automated Task Execution: From simple workflow automation to complex task management, AI Agents can plan, execute, and adjust action strategies based on feedback.
Data Analysis and Decision Support: In fields such as finance and healthcare, AI Agents can perform data analysis, provide decision support, and predict trends or market responses.
Personal Assistant: As a personal AI assistant, they can manage schedules, book services, provide travel planning suggestions, etc., enhancing convenience in daily life.
Smart Home Control: AI Agents can be used to control smart home devices, optimize energy usage, and provide security monitoring.
Education and Training: In the education sector, AI Agents can serve as teaching assistants, providing customized learning path suggestions and answering student questions.
Gaming and Entertainment: AI Agents can enhance gaming experiences by interacting with players through AI-controlled NPCs (non-player characters) and even assisting in game content development.
Research and Development: In scientific research or software development, AI Agents can perform automated experimental design, code generation, bug fixing, and other tasks.
The application of AI Agents is increasingly valued because they can autonomously execute complex tasks, and their potential in various industries is continually being explored and expanded. These functionalities and uses demonstrate how AI Agents can simplify processes, enhance efficiency, and open up new application fields through intelligent means.
(3) Challenges and Development Directions of On-chain AI Agents
At present, on-chain AI agents face issues such as limited computing resources, low intelligence levels, and difficulties in integrating with other blockchain components. In the future, further optimization of blockchain technology is needed to enhance its processing capacity and scalability, while strengthening the deep integration of artificial intelligence technology with blockchain technology to develop smarter and more efficient on-chain AI agents. Additionally, a comprehensive regulatory framework and standards need to be established to ensure the legal and compliant operation of on-chain AI agents.
