Decentralized AI: Blockchain-driven Intelligent New Era

AI technology is profoundly impacting our daily lives, from rapid document analysis to creative brainstorming, and even helping us transform into our favorite film and television characters. However, despite the many conveniences that AI brings, it has also raised a series of serious concerns.

The most advanced and powerful AI models are currently controlled by a few tech giants, and their internal operating mechanisms are often opaque. We know very little about the sources of training data for these models, the decision-making processes, and the beneficiaries after model upgrades. The contributions of creators often go unrecognized and unrewarded, while potential biases may insidiously seep in. These powerful tools that shape our future seem to be operating quietly behind the scenes.

This situation has sparked a backlash among people. Concerns over privacy violations, the spread of misinformation, lack of transparency, and the monopolization of AI training and profit distribution by a few companies are intensifying. These worries are prompting calls for the establishment of AI systems that are more transparent, prioritize privacy protection, and allow for more open participation.

Decentralized AI (DeAI) has emerged to provide new ideas for solving these problems. Such systems decentralize data, computation, and governance, making AI models more responsible, transparent, and inclusive. Contributors can receive fair compensation, and the community can collectively decide the direction of these powerful tools.

The essence of Decentralization AI and its differences from traditional AI

Currently, mainstream AI systems often adopt a centralized architecture, with a single company responsible for data collection, model training, and output control. This model usually does not accept public oversight, making it difficult for users to understand the model's construction process and potential biases.

In contrast, decentralized AI adopts a completely different approach. Data is stored in a decentralized manner across various nodes, and the model is managed collectively by the community or protocol, with an open and transparent updating process. In this model, the AI system is built on public collaboration, with clear rules and participation incentives, rather than being controlled by a closed black box.

For example, traditional AI is like a museum run by a private foundation. Visitors can appreciate the exhibits and even see their data being presented artistically, but they cannot participate in the decision-making process, nor will they be recognized or compensated for their contributions. The operation of the museum is opaque, and most inside information is not visible to the outside world.

Decentralized AI is more like an outdoor art exhibition collaboratively created by a global community. Artists, historians, and ordinary citizens contribute ideas, share data, and participate in curation. Every contribution is traceable and transparent, and participants are rewarded for enhancing the exhibition. This structure helps strengthen user rights protection and improve system accountability, which is urgently needed in the current AI field.

The importance of Decentralization AI

Traditional centralized AI models have serious issues. When a few companies control the models, they decide what the models learn, how they behave, and what access permissions they have, which brings the following risks:

• Excessive concentration of power: The direction of AI development is controlled by a few companies, lacking public oversight.
• Algorithmic Bias: Limited data sources and perspectives may lead to unfairness and exclusion in the system.
• Users lose control: People contribute data but have no power to decide its use, nor do they receive the rewards they deserve.
• Innovation is limited: Centralized control restricts the diversity and experimental space of the model.

Decentralized AI opens up the possibility of building more transparent, fair, and innovative AI systems by distributing ownership and control. Global contributors can collaboratively shape the models, ensuring they reflect a broader range of perspectives. Transparency plays a key role, with many decentralized AI systems adopting open-source principles, making code and training methods public to facilitate auditing of models, identifying issues, and building trust.

It is important to note that open-source AI is not synonymous with decentralized AI. Models can be open-source but still rely on centralized infrastructure or lack necessary privacy protection mechanisms. The common core features of both are transparency, accessibility, and community participation. In decentralized AI, users can participate without giving up control of their data, making them more likely to contribute actively and benefit from it. Decentralization is not a panacea, but it opens new pathways for building AI systems that better serve the public interest and are not influenced by private enterprises.

The working principle of Decentralized AI

Decentralized AI replaces centralized control with a distributed system. Model training, optimization, and deployment take place in an independent node network, avoiding single points of failure, enhancing transparency, and encouraging broader participation.

The key technologies supporting Decentralization AI include:

• Federated Learning: Allows AI models to learn from data on local devices without needing to upload sensitive information to a central server, only sharing model updates.
• Distributed Computing: Distributing the training and execution load of AI models across multiple machines in the network to enhance speed, efficiency, scalability, and system resilience.
• Zero-Knowledge Proof (ZKP): A cryptographic tool that can verify the correctness of data or operations without revealing specific content, ensuring the security and trustworthiness of distributed systems.

Blockchain technology provides important support for Decentralization AI. Smart contracts can automatically execute preset rules, such as payments or model updates, without manual intervention. Oracles serve as a bridge between the Blockchain and the external world, providing real-world data inputs. Decentralized storage allows training data and model files to be distributed across the network, making it more resistant to tampering, censorship, and single points of failure than traditional servers.

The advantages of Decentralization AI

Decentralized AI is not only a technological revolution but also a shift in values. It builds systems that embody common human values such as privacy, transparency, fairness, and participation. Through decentralization, it achieves the following advantages:

• Better privacy protection: Ensure data privacy through technologies such as federated learning, local device training, and zero-knowledge proofs.
• Built-in Transparency: Open systems facilitate auditing, tracking decision-making processes, and identifying potential biases.
• Shared Governance: The community collectively formulates rules, incentive mechanisms, and the direction of model evolution.
• Fair economic incentives: Contributors are rewarded for providing data, computing resources, or model improvements.
• Reduce bias: More diverse contributors bring inclusive perspectives, lowering the risk of blind spots.
• Stronger system resilience: No single point of failure, making the system harder to breach or shut down.

Challenges and Limitations

Despite the enormous potential of Decentralization AI, it also faces numerous challenges:

• Scalability: Large model training requires massive computing power, and distributed coordination may lead to a decrease in speed or an increase in complexity.
• Resource-intensive: AI models themselves are resource-consuming, and distributed operation may further exacerbate bandwidth and energy consumption pressure.
• Regulatory uncertainty: The differences in regulations across regions are significant, and the liability of decentralized systems is relatively complex.
• Fragmentation risk: The lack of centralized regulation may lead to non-uniform standards and uneven participation.
• Security and reliability issues: Trustless systems may still be vulnerable to attacks, such as data manipulation, model poisoning, etc.
• Complex user experience: Managing private keys and operating multiple interfaces may hinder adoption.

These are real problems that exist, but they are not insurmountable. With the continuous advancement of technology and the gradual improvement of the ecosystem, we have reason to believe that these challenges will eventually be resolved.

Decentralization AI's practical application

Decentralized AI is no longer limited to theoretical discussions; several Web3 projects are demonstrating how distributed intelligence can drive practical applications. Here are a few representative projects that are building decentralized AI:

• Acurast: Allows users to turn idle devices (such as old phones) into a part of the decentralized cloud, earning rewards by providing unused computing power.
• OriginTrail: Operates on a decentralized knowledge graph to connect and organize trusted data in supply chain, education, and other fields, creating a public facts repository.
• Phala: Building a privacy layer for Web3, allowing developers to run smart contracts in a confidential computing environment to protect sensitive data.
• PEAQ: Providing infrastructure for the machine economy, allowing people and devices to earn rewards by completing real tasks, creating a machine version of the gig economy.
• Bittensor: Create an open market where AI models compete and collaborate, rewarding valuable contributions through token incentives, and building a self-improving, censorship-resistant AI economic system.

These projects showcase the potential applications of decentralized AI in the real world, covering multiple important areas from privacy computing to knowledge management, from machine economy to AI model training. As technology continues to mature and the ecosystem gradually improves, we have reason to expect more innovative applications to emerge.

Decentralized AI represents a whole new way of building intelligent systems, challenging traditional centralized control models and providing us with more open and accountable choices. By decentralizing power, protecting privacy, and encouraging global participation, this new type of AI system is expected to shape a more equitable and transparent intelligent future. Despite facing numerous challenges, with continuous technological advancements and the gradual improvement of the ecosystem, decentralized AI will undoubtedly play an increasingly important role in shaping our digital world.