On November 20th, Bittensor (TAO) officially debuted on HotsCoin. Bittensor is an open-source protocol designed to support blockchain-based machine learning networks. Currently, TAO is available for trading on HotsCoin.
- Project Overview
Bittensor is an open-source protocol aiming to facilitate blockchain-based machine learning networks. HotsCoin recognizes that the protocol, by collaboratively training machine learning models and rewarding them based on their collective information value, establishes a decentralized and trustless peer-to-peer network. This network not only allows users to extract information from it but also adjusts its activities based on user demands, creating an artificial intelligence marketplace in an environment that is trustless, open, and transparent.
- Development History
Background: The development of machine intelligence is limited by the narrow problem of supervision. In response, Bittensor proposes a new mechanism through decentralized networks and mutual ranking of machine intelligence.
Key Events: Since 2012, the development of neural networks has accelerated, and Bittensor has introduced the concept of effectively generating machine intelligence through a ranking system in this trend.
- Network Architecture
AI Layer: Responsible for abstracting the Bittensor kernel to ensure compatibility of the node's neural network with the input/output of other nodes. Each node contains a neural network, also known as a neuron, measured and trained in a peer-to-peer manner.
Blockchain Layer: Based on Polkadot Substrate, responsible for executing consensus mechanisms, ensuring node identity, and incentivizing network nodes. Utilizes a staking-weighted trust mechanism for fair reward distribution.
- Mechanisms and Algorithms
Ranking and Incentives: Bittensor calculates rankings using Shapley values, and rewards are allocated based on peers' contributions to the collective information. Peers with higher rankings receive more incentives.
Consensus Mechanism: A trust-based incentive mechanism prevents collusion issues, ensuring network security and correctness through consensus equations and a trust matrix.
Sparse Monte Carlo Estimation: Used to calculate Shapley scores for thousands of peers, balancing between computational efficiency and accuracy.
- Current Status and Outlook
Current Status: The Bittensor network officially launched in November 2021, including 4096 nodes. The team has made multiple algorithm, consensus, and infrastructure changes to ensure the protocol's correctness and scalability.
Future Outlook: The team is actively working on training more complex models directly on the Bittensor protocol, aiming to achieve performance comparable to the current state-of-the-art models.
- Risks and Challenges
Market Competition: The field of machine learning is highly competitive, facing competition from other projects, necessitating continuous innovation to maintain competitiveness.
Technical Implementation: Successful implementation of the various technologies and algorithms mentioned in the protocol is crucial to ensure network security and performance.
- Investment Advice
Potential Growth: Bittensor's unique mechanism brings new possibilities to the field of machine learning, potentially gaining a larger market share in the future.
Risk Warning: Intense competition, technical implementation, and market acceptance are primary risks for investment.
- Conclusion
Bittensor, as a decentralized machine learning network, constructs a trustless intelligence marketplace through peer-to-peer collaborative training and information ranking mechanisms. In a fiercely competitive market, Bittensor is poised for success through its unique mechanisms. Investors should closely monitor its technical progress and market performance.
HotsCoin will continue to monitor the project ecosystem. (Report writing date: November 2023) The information in this report is for reference only and does not constitute investment advice. Investors should thoroughly understand relevant projects and risks and seek professional advice when necessary.
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