Glossary
Glossary This glossary provides definitions and explanations of key terms and concepts used within the Cortensor ecosystem. It serves as a reference to help users understand the various components and functionalities of the platform.
A AI Inference: The process of using trained AI models to make predictions or generate outputs based on new input data.
AI Marketplace: A platform within Cortensor where developers can share, sell, and purchase AI models and services.
B Blockchain: A decentralized digital ledger that records transactions across multiple computers, ensuring that registered transactions cannot be altered retroactively.
Block Time: The time interval required to create a new block in a blockchain network.
C Classification Services: AI services that categorize data into predefined classes, such as spam detection or image classification.
Consensus Mechanism: A protocol used to achieve agreement on a single data value among distributed processes or systems. Examples include Proof of Inference (PoI) and Proof of Useful Work (PoUW).
Cortensor Token: The utility and governance token used within the Cortensor ecosystem to reward contributions and participate in governance.
D Data Management: The practice of organizing and maintaining data processes to meet ongoing information lifecycle needs.
Decentralized: A system where control is distributed among various nodes rather than being centralized in a single entity.
E Ethereum: A decentralized, open-source blockchain system that features smart contract functionality.
Embedding Comparison: A method of comparing AI-generated outputs by transforming them into vector representations and measuring similarity.
F Fine-Tuning: The process of making small adjustments to a pre-trained AI model to adapt it for a specific task or dataset.
G Governance: The system by which decisions are made and implemented within the Cortensor ecosystem, typically involving token holder participation.
I Inference Services: Services that allow applications to use AI models to generate outputs or predictions from new input data.
IPFS (InterPlanetary File System): A protocol and network designed to create a peer-to-peer method of storing and sharing hypermedia in a distributed file system.
L Llama 3: An AI model supported by Cortensor, available in both quantized and regular versions, used for various inference tasks.
M Model Quantization: The process of reducing the precision of the numbers used in a model's calculations, enabling it to run on lower-end hardware.
Miner Nodes: Nodes within the Cortensor network responsible for executing AI inference tasks and participating in validation processes.
O Oracle Services: Services that provide reliable data feeds to smart contracts, enabling accurate execution of blockchain operations.
P Prediction Services: AI services that analyze historical data to forecast future trends and outcomes.
Proof of Inference (PoI): A consensus mechanism used to validate the completion and accuracy of AI inference tasks.
Proof of Useful Work (PoUW): A consensus mechanism that ensures the work performed by miner nodes is useful and meets required standards.
R Router Nodes: Nodes that manage the allocation of tasks to miner nodes and facilitate communication between clients and the network.
Reputation and Scoring: A system that evaluates the performance of nodes and participants within the Cortensor network, influencing task allocation and rewards.
S SDK (Software Development Kit): A collection of software development tools in one installable package, enabling the creation of applications for a specific platform.
Staking: The process of holding tokens in a cryptocurrency wallet to support the operations of a blockchain network, often earning rewards in return.
Synthetic Data Generation: The creation of artificial data that mimics real-world data, used to train AI models and improve performance.
T Tokenomics: The economic model and distribution mechanisms of tokens within a blockchain ecosystem, including supply, distribution, and incentives.
V Validation Nodes: Nodes responsible for verifying the accuracy and reliability of AI inference results within the Cortensor network.
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