AI Revolution and Bittensor Network: Reshaping Collective Intelligence

The Historical Background of the AI Revolution

The rapid development of artificial intelligence technology is leading us into a data-driven new era. Breakthroughs in fields such as deep learning and natural language processing have made AI applications ubiquitous. The launch of ChatGPT in 2022 ignited the AI industry, followed by a surge of AI tools for text-to-video, automated office work, and more. The market value of the AI industry has also soared, expected to reach $185 billion by 2030.

However, the AI industry is currently dominated by a few technology giants, which not only brings challenges such as data centralization and uneven distribution of computing resources, but also restricts the innovation potential of the entire industry. Meanwhile, the decentralized concept of Web3 offers new possibilities for addressing these issues. In the distributed network of Web3, there is hope to reshape the current landscape of AI development.

In this context, a number of high-quality Web3+AI projects have emerged. Among them, the Bittensor project stands out by building an AI algorithm platform with self-selection and competition mechanisms through blockchain competition and incentive mechanisms, providing a new way to retain and develop the best AI projects.

Bittensor: Decentralized AI Ecosystem

Bittensor is a decentralized incentivized machine learning network and digital goods marketplace. Its core advantages include:

• Decentralized architecture: Operates on a distributed computer network controlled by different institutions, effectively addressing issues such as data centralization.

• Fair incentive mechanism: The token rewards provided by the network to the subnet are proportional to its contributions, and the reward distribution within the subnet follows the same principle.

• Open machine learning resources: providing services for individuals in need of machine learning computing resources.

• Diversified digital commodity trading: not only supports trading of machine learning models and data, but also allows for trading other forms of digital goods.

The development history of Bittensor reflects the qualities that a true geek project should have:

2021: Created by a group of technology experts dedicated to promoting decentralized AI networks, built on the Substrate framework.

2022: Released the Alpha version of the network, validating the feasibility of decentralized AI. Introduced Yuma consensus, emphasizing the principle of data unawareness.

2023: Beta version released, introducing the token economic model (TAO) to incentivize network maintenance.

2024: Utilize DHT technology to improve data storage and retrieval efficiency, with a focus on developing subnets and digital goods markets.

It is worth noting that there has been little participation from traditional venture capital in the development of Bittensor, which reduces the risk of centralized control. The project incentivizes nodes and miners through tokens, ensuring the vitality of the network. Essentially, Bittensor is an AI computing power and service project powered by GPU miners.

Subnet Architecture: Bittensor's Core Innovation

The core of the Bittensor network lies in its unique subnet architecture. Each subnet can be viewed as a piece of independently running code, establishing specific user incentives and functionalities, while maintaining the same consensus interface as the main network.

There are three main roles in the subnet:

1. Subnet owner: responsible for providing the basic miner and validator code, and can set additional incentive mechanisms.

2. Miners: Run servers and mining code, maintaining a competitive edge. Miners can run nodes across multiple subnets.

3. Validator: Evaluate the contributions of the subnet and ensure their correctness to receive corresponding rewards. You can stake TAO tokens for additional earnings.

Subnet emission is the core mechanism of reward distribution in the Bittensor network. Generally speaking, 18% is allocated to subnet owners, 41% to validators, and 41% to miners. A survival of the fittest mechanism is employed within the subnet, where underperforming validators and miners are replaced by newcomers.

After the subnet registration, there is a 7-day immunity period. When all subnet positions are filled, new subnet registrations will eliminate the ones with the lowest emissions that are not in the immunity period. This mechanism encourages subnets to continuously increase their staking amounts and miner efficiency to ensure survival.

Innovation in Consensus Mechanisms and Proof Mechanisms

The Bittensor network employs various innovative consensus and proof mechanisms:

1. Proof of Intelligence ( PoI ) mechanism: Participants' contributions are proven through the completion of intelligent computing tasks, ensuring network security, data quality, and efficient resource utilization.

2. Yuma Consensus: Validators score based on task completion, inputting the Yuma algorithm. Validators with a higher staking amount have a greater weight in scoring, while results that deviate from the majority are eliminated. Ultimately, rewards are allocated based on the comprehensive scoring.

3. MOE mechanism: Integrating multiple expert-level sub-models within a model architecture to work collaboratively for better results. Validators can score and rank the expert models, incentivizing model optimization.

These mechanisms together ensure the efficient operation and continuous innovation of the Bittensor network.

Current Status and Prospects of Subnet Projects

As of now, Bittensor has 45 registered subnets, 40 of which have been named. The top three subnets are:

1. Subnet 19 Vision: Focus on decentralized image generation and inference, providing access to high-quality models.

2. Subnet 18 Cortex.t: Committed to building a cutting-edge AI platform that provides reliable text and image responses.

3. Subnet 1: Bittensor's first subnet, focused on text generation.

These subnets demonstrate different AI application directions, ranging from image processing to text generation, and market sentiment analysis, among others. Although the returns are considerable, new entrants face intense competition and need high-performance equipment and optimized algorithms to establish themselves in the network.

Future Outlook

Bittensor, as an innovative project in the Web3+AI field, has a broad development prospect:

1. Market Enthusiasm: The combination of AI and Web3 will maintain long-term market attention.

2. Project Foundation: Bittensor has technical innovation and market recognition, laying the foundation for long-term development.

3. Ecological Expansion: The subnet architecture lowers the threshold for AI teams to enter the decentralized network, facilitating rapid ecological expansion.

4. Continuous Optimization: The competitive elimination mechanism encourages subnets to continuously improve models and increase staking amounts.

However, it is necessary to be vigilant about the challenges that may arise with the increase in the number of subnets, such as the rise in low-quality projects and the decline in individual subnet yields. Bittensor needs to seek a balance between expansion and quality control to achieve long-term sustainable development.