Draft:Bittensor

Bittensor (abbreviation: TAO; sign: τ) is an open-source, decentralized protocol that combines blockchain technology with artificial intelligence to create a peer-to-peer machine learning network and digital commodities market. It incentivizes participants to contribute AI models and rewards them with the native TAO token based on the value they provide to the network.

Bittensor employs a modular architecture with subnets dedicated to specific machine learning domains, where miners register to provide services and validators ensure response quality. Consensus is achieved using the Yuma algorithm, a hybrid Proof-of-Stake and Proof-of-Work mechanism that evaluates AI model quality and protects against malicious validator activity.

TAO, the native cryptocurrency of Bittensor, is used for staking, governance, and payments within the ecosystem. It has a capped supply of 21 million tokens, mirroring Bitcoin's model, and follows a halving cycle every 10.5 million blocks.

Bittensor's decentralized AI marketplace has potential applications across various industries, including natural language processing, computer vision, predictive analytics, and autonomous systems. By democratizing access to AI capabilities and fostering collaboration, Bittensor aims to accelerate innovation and promote more equitable ownership of machine intelligence. However, as an emerging technology, it faces challenges such as regulatory uncertainty, security vulnerabilities, and competition from centralized AI providers.

History

Bittensor was launched in 2019 by the Opentensor Foundation and its founders, Jacob Robert Steeves and Ala Shaabana. The project aimed to create a decentralized, blockchain-based machine learning network that incentivizes participants to contribute AI models and rewards them with the native TAO token based on the value they provide. The founders' vision was to democratize access to AI capabilities and foster collaboration in the development of artificial intelligence.

The network has undergone several iterations and upgrades since its inception:

• Launch of 'Kusanagi' (January 2021): Marked Bittensor's debut as the first main network version, released on the Substrate. This initial implementation enabled miners and validators to start earning TAO rewards for their contributions to the network.
• Pause of Kusanagi (May 2021): The Kusanagi network was temporarily halted to allow for refinements and optimizations of the consensus mechanisms, ensuring a more robust and efficient system moving forward.
• Transition to 'Nakamoto' (November 2021): Building upon the lessons learned from Kusanagi, the Bittensor team launched the Nakamoto network as a successor through a hard fork upgrade.This significant update introduced various enhancements and laid the groundwork for future scalability and interoperability.
• Emergence of 'Finney' (March 2023): The Bittensor network took a leap forward with the launch of Finney, marking its evolution into an independent blockchain. Named after Bitcoin pioneer Hal Finney, this upgrade saw the introduction of the Yuma hybrid consensus algorithm, combining Proof-of-Stake and Proof-of-Work to evaluate AI model quality and protect against malicious validator activity.

Architecture

Bittensor's architecture is designed to facilitate a decentralized, scalable, and efficient marketplace for AI services. The network consists of several key components that work together to enable collaborative learning, ensure data security, and maintain the integrity of the system.

At the core of Bittensor's architecture is the modular subnet system. Subnets are dedicated to specific machine learning domains, such as natural language processing, computer vision, or predictive analytics. Each subnet has its own set of rules and parameters, including minimum staking requirements for validators, reward distribution mechanisms, and the specific AI models and datasets used. This modular approach enhances the efficiency and scalability of the network, allowing for parallel processing of requests and the compartmentalization of data and resources.

Within each subnet, there are three main types of participants: miners, validators, and nominators.

• Miners offer AI models and respond to user requests, contributing to the Proof-of-Work component of the consensus.
• Validators send user queries to miners, assess the quality of their responses, and participate in block production. They stake TAO tokens as collateral and can be penalized for misconduct or poor performance.
• Nominators are TAO holders who delegate their tokens to validators, increasing the validators' influence and earning a share of the rewards

Bittensor employs a decentralized mixture-of-experts (MoE) approach, where each miner acts as an expert, offering its specialized knowledge and capabilities to the network. When a user submits a request, it is routed to the appropriate subnet based on the nature of the task. Within the subnet, validators distribute the request to a subset of miners, who process the input data using their AI models and generate outputs. The validators then assess the quality and relevance of the responses, aggregating the most valuable contributions to form a final output.

To ensure data security and protect user privacy, Bittensor utilizes advanced cryptographic techniques, such as homomorphic encryption and secure multi-party computation. These methods enable miners to process and learn from encrypted data without revealing its contents, preserving the confidentiality of sensitive information. Bittensor's architecture is designed to scale horizontally, accommodating a growing number of participants and handling increasing volumes of requests. The peer-to-peer communication protocol enables efficient and secure interactions between network participants, eliminating the need for intermediaries and reducing the risk of single points of failure. The network also supports cross-subnet communication and interoperability, enabling the seamless integration of different AI services and creating opportunities for the development of novel applications that span across domains.

Consensus Mechanism

Underpinning the entire Bittensor network is the Yuma consensus mechanism, a hybrid Proof-of-Stake and Proof-of-Work algorithm that evaluates AI model quality and protects against malicious validator activity. Yuma is designed to ensure the security, fairness, and efficiency of the network while incentivizing participants to contribute valuable AI services.

In the Yuma consensus, validators stake TAO tokens to participate in block production and earn rewards. The amount of TAO staked determines a validator's voting power and influence on the network. Validators are responsible for proposing new blocks, which contain a set of transactions and updates to the network state. Other validators then vote on the proposed blocks, and if a block receives a sufficient number of votes, it is added to the blockchain.

Alongside the Proof-of-Stake component, Yuma incorporates a Proof-of-Work element, where miners compete to provide high-quality AI services. Miners are rewarded with TAO tokens based on the value they generate for the network, as determined by the quality and relevance of their AI model outputs. This incentive mechanism encourages miners to continuously improve their models and contribute to the overall performance of the network.

Yuma also introduces a novel concept called "quality-weighted voting," where a validator's voting power is adjusted based on the quality of the AI services provided by the miners it has staked on. This mechanism ensures that validators are incentivized to support and promote high-quality miners, as their own rewards and influence depend on the performance of the miners they back.

To protect against malicious behavior, Yuma employs a combination of slashing penalties and reputation scores. Validators that engage in dishonest or malicious activities, such as proposing invalid blocks or supporting low-quality miners, can have their staked TAO tokens slashed and their reputation scores reduced. This system discourages misbehavior and ensures that participants act in the best interest of the network.