Quick Bites

• Artificial intelligence (AI) is a rapidly adopted technology expected to transform society, with centralized AI currently dominating the field. While offering accurate responses, centralized AI carries risks of misuse, biases, data privacy concerns, restricted innovation, monopolistic control, and inefficiencies in resource utilization.

• Bittensor enables the decentralization of machine learning by establishing a peer-to-peer market for AI models, promoting collective intelligence and collaboration.

• A Bittensor network subnet represents a distinct domain or topic area consisting of registered nodes and machine learning models. Subnets are smaller components within the overall network, each focused on a specific field of machine learning or data processing. They serve as foundational elements for developing various programs and applications within Bittensor.

• The TAO represents intelligence and knowledge in the Bittensor network. Like Bitcoin, it has a predetermined total supply and undergoes halving every 4 years. TAO rewards miners and validators who help bring valuable AI models into existence.

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The Dawn of New Era

The Age of AI

In the realm of technology, few innovations have sparked as much interest and excitement as Artificial Intelligence (AI). Its rapid adoption surpasses even that of the internet and smartphones, making it a cornerstone of modern technological advancement. From digital assistants to advanced predictive analytics, AI is seamlessly integrating into every facet of our lives.

The Double-Edged Sword of Centralized AI

• Currently, AI models are predominantly centralized, controlled by major tech ventures. This centralization, while efficient in delivering accurate and intuitive responses, is not without significant drawbacks:

  • Potential Misuse and Biases: Centralized control over AI systems can lead to potential misuse, and the models may inherently carry the biases of their creators

  • Data Privacy & Surveillance Risks: Such systems often accumulate vast amounts of data, raising concerns about privacy and enabling surveillance.

  • Siloed Model Development: Centralized AI tends to operate in silos, restricting the integration and cross-pollination of ideas and innovations, this will lead to higher probability of bias outcome produced.

  • Monopolistic Nature: Dominance by a few entities can lead to a monopolistic control over AI technologies, stifling competition and innovation.

  • Inefficiencies of Resources: Centralized systems can lead to redundant efforts and an inefficient use of computational resources, as multiple entities often work in parallel without collaboration.

• In response to these challenges, the concept of decentralizing AI has emerged as a beacon of hope. A decentralized AI ecosystem offers numerous advantages:

  • Promotes Collaboration: It encourages global cooperation among developers and researchers, fostering innovation and creativity.

  • Unified Resources: By pooling computational resources, decentralized AI can handle massive computational requirements more effectively than any single entity.

  • Democratic Ownership: It allows for collective decision-making in the trajectory of AI development, incorporating diverse perspectives.

  • Dynamic Learning Environment: Decentralized AI thrives on varied datasets and continuous learning, which drives its evolution and adaptation.

  • Efficient Resource Allocation: Funds and computational resources are distributed based on needs and contributions, maximizing efficiency and reducing wastage.

• The outcome of these efforts is an AI that is decentralized, collaborative, resistant to censorship, resilient, and not easily influenced by specific biases. The goal is to create an unbiased AI that is trained using the most effective collaborative model.

Bittensor: A Paradigm Shift in AI

• At the forefront of this revolution is Bittensor, a novel platform that decentralizes the process of machine learning. It creates a peer-to-peer market for machine intelligence, fostering a digital hive mind.

• To simplify things, let's imagine that Bittensor functions on three different layers.

  • The Client and Enterprise Layer is the part where end-users and businesses connect to the network's AI services. This is where applications are created and interact with the underlying AI and Consensus layer.

  • The AI Layer is the central part of Bittensor's machine learning capabilities. It consists of miners, which are nodes that host and serve the machine learning model. The AI Layer handles neural network operations, AI model training, and inferencing. It enables peer-to-peer interactions among these models. It is also where specialized AI domains, known as Subnets, are located.

  • The Consensus Layer is the foundation of the Bittensor’s blockchain network based on Polkadot Substrate SDK. It utilizes Yuma Consensus by Validators to maintain the integrity of the network and ensure secure and verifiable transactions. This layer also implements consensus mechanisms like Proof of Intelligence to validate and record contributions. It then rewards the contributors.

• The Yuma Consensus takes its name from Yuma Rao, the unidentified author of the Bittensor whitepaper. The Yuma Consensus is a peer-to-peer algorithm that enhances AI capabilities across a network. It includes a unique mechanism called 'Proof of Intelligence' which rewards valuable machine-learning contributions.

• The Yuma consensus is customizable to suit specific requirements, making it versatile for various applications. Due to its robustness, resilience, and resistance to censorship, AI apps can operate with greater independence and freedom.

• Bittensor uses game-theoretic scoring methods, such as the Shapley Value, to evaluate model performance. Their consensus mechanism, known as 'Proof of Intelligence,' rewards machine-learning contributions that have value.

• Bittensor uses the Decentralized Mixture of Experts (MoE) to train the model. This model combines the strengths of various neural networks to effectively tackle complex problems.

• The Bittensor Protocol create a marketplace where machine intelligence can be traded. TAO tokens symbolize the intelligence and knowledge within the Bittensor ecosystem.

Bittensor Subnets

The Subtensor Networks (Subnets)

• A subnet in the Bittensor network represents a distinct domain or topic area. Each subnet consists of registered nodes and their related machine learning models.

• These subnets are essentially smaller parts of the larger Bittensor network, each dedicated to a specific field of machine learning or data processing.

• Each subnet serves as a building block, providing the foundation for creating countless programs and applications on Bittensor. As AI continues to rapidly advance, we can expect the creation of a multitude of subnets across various domains in the future.

TAO: The Cognitive Economy

The Cognitive Economy

• Bittensor emphasizes decentralization and collaboration through "Knowledge Compounding." This approach expands AI knowledge in a decentralized manner, reducing retraining costs and facilitating adaptability to changing needs.

• Bittensor's decentralized network fosters innovation and diversity in AI development by encouraging collaboration and knowledge sharing among nodes. These services are a natural demand of Bittensor's ecosystem activities.

• TAO symbolizes the value of intelligence and knowledge in the Bittensor network. It represents the contributions made by contributors and the quality of their AI models. Its tokenomics draw inspiration from Bitcoin, incorporating halving and a comparable total supply.

  • 21.000.000 Total Supply

  • Distributed to 50% Validator and 50% Miner

  • Halving /4 Years

  • 256+ years to fully emitted

• In the Bittensor ecosystem, TAO tokens are utilized to incentivize individuals to participate in the Bittensor network. Users who contribute valuable AI models and perform effectively receive these tokens, along with validators.

• However, on the demand side, TAO is needed to:

  • Create a subnet and participation in governance

  • Enable enterprise and client access to machine learning models

  • Engage in ecosystem activities such as node creation, staking, becoming validators, etc.

  • Seek incentives and optimistic speculation on AI.

Conclusion

• Bittensor signifies more than just a significant advancement in technology; it marks a fundamental change in our approach to developing AI. By promoting decentralization, it strives to establish an impartial, cooperative, and effective AI ecosystem.

• As we enter this new period, the possibilities for groundbreaking ideas and changes in AI are limitless.

• The future of AI, now decentralized and accessible to all, has arrived hand in hand with Bittensor.

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