The Integration of AI and Encryption Technology: Three Major Development Directions

Currently, the combination of AI and encryption technology is rapidly entering the experimental stage. This article will explore three important development directions of AI + encryption integration.

1. Build an Economy Ecosystem Driven by Intelligent Agents

Smart agents operating on the blockchain have shown immense potential. Experiments in this field continue to expand the boundaries of agent operations on-chain, demonstrating a vast design space and explosive power.

Future Development Trends

Smart agents are expected to manage complex multi-party collaboration projects. For example, in the field of scientific research, agents can be responsible for finding treatment solutions for specific diseases:

• Raise funds through a token fundraising platform
• Use funds to pay for research data access fees and computational costs of decentralized computing networks.
• Recruit humans to perform experimental verification work through bounty platforms.

In addition, agents can also perform simple tasks such as building personal websites and creating artworks, with a wide range of application scenarios.

blockchain advantages

Although agents can use both traditional financial and encryption systems simultaneously, cryptocurrencies have unique advantages in certain areas:

• Micropayment applications perform exceptionally well
• Instant settlement features help achieve maximum capital efficiency.
• Seamlessly enter the capital market through DeFi and engage in diversified financial activities.

As more and more agents profit through encryption, encrypted connections are likely to become a core capability for agents.

Focus Direction

Several exploration directions worth paying attention to in the future include:

1. Establishment of Risk Control Mechanism
2. Promote non-speculative use cases
3. Accelerate the development progress and realize the mainnet operation as soon as possible.

2. Enhance the capabilities of LLM in development

Large language models ( LLMs ) have already demonstrated powerful capabilities and are rapidly advancing in code writing. It is expected that there will be a steep progress curve in this field, which is likely to completely transform the software development model.

Current Challenge

Currently, LLMs still face some challenges in understanding specific blockchain technologies:

• Lack of high-quality raw training data
• Insufficient number of verified builds
• Lack of high information value developer interaction
• Rapid technological updates and iterations may render historical code unsuitable for current needs.
• Lack of methods to assess the understanding of models

future development direction

To promote the application of LLMs in development, future efforts can focus on the following aspects:

• Improve the quality of relevant data on the Internet
• Encourage more teams to release verification builds
• Actively ask questions and provide high-quality answers in the developer community.
• Create high-quality benchmark tests to assess the understanding of LLMs.
• Develop LLM fine-tuning models that perform well in benchmark tests

The long-term goal is to achieve a completely new, high-quality, differentiated validator node client created entirely by AI.

3. Support Open and Decentralized AI Technology Stack

The long-term power balance between open-source and closed-source AI models remains unclear. The current expectation is that tech giants will drive cutting-edge development, while open-source models will quickly follow and gain advantages in specific areas.

importance

The importance of supporting open AI technology stacks is reflected in:

1. Accelerate innovation iterations: The rapid improvements and fine-tuning of models by the open-source community effectively advance the boundaries of AI capabilities.
2. Provide users with options: Offer alternatives for users who do not trust centralized AI to address the risks of AI potentially being used for control.

ecological status

Currently, there are multiple projects supporting various aspects of the open AI technology stack:

• Data Collection
• Decentralized Computing Power
• Decentralized Training Framework

Future Outlook

Looking forward to building more products across all layers of the open-source AI technology stack:

• Decentralized Data Collection
• On-chain identity verification
• Decentralized Training
• IP Infrastructure

Through these efforts, it is hoped to promote the deep integration of AI and encryption technology, unleashing more innovative potential.