Risks, Challenges and Future Development of Decentralized AI

Introduction

Decentralized AI has many advantages, but it also faces many risks and challenges. As the third article in this series, this article will analyze these challenges for you and look forward to the future development direction of decentralized AI.

We also welcome entrepreneurs and project parties in this direction to contact us.

Opportunities for the Development of AI Agent

AI Agent is the natural evolution of large models. By introducing memory mechanisms, task decomposition and planning capabilities, AI Agent can perceive the environment, make autonomous decisions and perform complex tasks.

Although the existing large models can generate text and solve problems, they do not have complete task planning and execution capabilities. AI Agent will make up for this shortcoming and improve AI's performance in complex tasks.

If AI is nuclear energy, then it should not be in the hands of only a few people. Decentralized AI Agent will ensure the fairness and transparency of AI technology through blockchain and encryption technology.

In the future agent society, decentralized AI will become an inevitable trend to solve the problems faced by the existing centralized AI system.

Data labeling development opportunities:

Data preparation includes data collection, cleaning, labeling and enhancement. AI's diverse demand for data increases the reliance on high-precision and highly customized data labeling. The lengthy work cycle and high labor costs of data labeling limit the development of the AI ​​industry.

Web3 can reach a large number of AI data collection and labeling staff in various regions around the world through economic incentive mechanisms, allowing them to benefit from data contributions. • Providers: Data providers can issue and sell their own data tokens to generate revenue.

• Consumers: Purchase or earn the required data tokens to gain access.

• Marketplaces: An open, transparent and fair data trading market provided by Ocean Protocol or a third party, which can connect providers and consumers worldwide and provide data tokens of various types and fields.

• Network: A decentralized network layer provided by Ocean Protocol.

• Curator: A role in an ecosystem responsible for screening, managing, and reviewing data sets. They are responsible for reviewing information such as the source, content, format, and license of the data set to ensure that the data set meets the standards and can be trusted and used by other users.

• Verifier: refers to the role in an ecosystem that is responsible for verifying and auditing data transactions and data services.

Summary:AI Agent anddecentralized data annotation are two of the most popular directions in DeAI, and many entrepreneurial teams are developing in these areas.

Risks and Challenges of Decentralized AI

• Web3’s Limitations in Enabling AI: Due to the limited number of encrypted users of Web3, the radiation range of the economic incentive mechanism is relatively small. This limits the rapid development of decentralized AI, which requires more user participation and acceptance.

• Challenges of zero-knowledge proof technology: issues such as quantization accuracy, hardware requirements, and adversarial attacks. Zero-knowledge proof technology (ZKP) has long-term significance in achieving model verifiability, but it still faces technical difficulties and implementation challenges.

• Attractiveness of cost advantage: If the supply of computing power resources in the market is eased, the value and cost advantage of decentralized computing power networks will be weakened. This requires decentralized AI to continuously improve efficiency and reduce costs to maintain its competitiveness.

• The efficiency and cost issues of combining AI with cryptography: The efficiency of performing privacy computing tasks using zero-knowledge proof technology or fully homomorphic encryption (FHE) technology is much lower than that of plaintext execution. Due to the high computing requirements of AI, the addition of cryptography technology will further increase the cost and may be difficult to implement.

• AI deep fake problems: The communication bottleneck problem in AI model training is significant. Frequent exchange of model parameters and gradient information consumes a lot of network bandwidth and generates high communication overhead. At the same time, the synchronization problems of each node will also affect the training results, requiring frequent data verification and synchronization operations.

• The popularity of AI has led to an increased risk of deep fraud. In the scenario where Web3 and AI are cross-enabled, it is necessary to guard against the risk of AI forgery.

Decentralized AI The future development direction

Model layer: As AIAgent becomes more common, users will rely onAIAgent to help them complete tasks in the future, which is the key to connecting the model layer and the application layer. Models are gradually forming diversified platforms, and the cost of large models is constantly decreasing. It will still take time to produce "dark horse" applications.

Training layer: Decentralized training of AI models is possible, but because the demand for inference is far greater than the demand for training, the training layer will rely more on centralized computing power.

Computing power layer: Decentralized computing power effectively reduces the cost of GPU use, and enterprise-level GPU meets the current computing power requirements. In the future, when the end-side model is implemented, consumer GPU will be useful.

Data layer: It is becoming increasingly difficult to obtain public data, and decentralized data collection and data annotation will become an important way to source and process data for future AI models.

Conclusion

Decentralized AI is an emerging technological trend, although the road is full of challenges, its development potential is huge. With the continuous advancement of technology and the gradual maturity of the market, decentralized AI is expected to play a greater role in the future. We need to continue to pay attention to these challenges and find innovative solutions to promote the development of decentralized AI. Among them, we believe that decentralized AI has its place in the four aspects of model, training, data, and computing power, especially DeAI is one of the most visible and most valuable directions.