Glossary
A
API integration: Interface provided by PrivateAI for seamless integration with external systems, applications, and services, enabling efficient data exchange and extending platform functionality.
AI solutions: PrivateAI integrates advanced AI solutions to enhance data management and utilization. The platform leverages semantic models for anomaly detection and utilizes Transformer architecture for scalability and compatibility across diverse applications.
Access control: Measures implemented to regulate and restrict user access to sensitive data and functionalities on PrivateAI.
B
Batch processing: The method of processing large volumes of data on PrivateAI in predetermined batches or groups, optimizing efficiency and resource utilization.
Blockchain integration: Incorporation of blockchain technology within PrivateAI's infrastructure to enhance data security, transparency, and immutability in transactions and data management.
C
Cryptocurrency: Digital currency used within PrivateAI's ecosystem for transactions, incentives, and value exchange, facilitating economic activities and incentivizing network participation.
Cloud computing: PrivateAI's use of remote servers hosted on the internet to store, manage, and process data, offering scalability, flexibility, and cost-efficiency.
D
Data integrity: Ensuring the accuracy, consistency, and reliability of data processed and stored within PrivateAI's ecosystem, critical for maintaining trust and confidence among users and stakeholders.
Data monetization: Process on PrivateAI enabling users to buy and sell datasets, knowledge graphs, and AI models using PGPT tokens, fostering transparent and efficient data transactions.
Data processing: Systematic handling of raw data within PrivateAI, encompassing cleaning, transformation, and normalization processes to prepare data for AI model training and analysis.
Data security: Measures implemented by PrivateAI to protect data privacy, confidentiality, and integrity throughout storage, processing, and transmission, ensuring compliance with regulatory standards and user expectations.
Decentralized omputing: Distributed computing architecture used by PrivateAI to harness computational resources across network nodes, enhancing scalability, efficiency, and fault tolerance in data processing.
Data contribution: Contributors on PrivateAI participate in a sophisticated data monetization system, earning PGPT tokens for providing datasets and computational resources.
Decentralized storage network: PrivateAI's distributed network of storage nodes leveraging advanced SSD technologies for efficient and secure data storage across multiple locations.
E
Entity recognition: Process within PrivateAI identifying and categorizing entities (e.g., names, locations) within unstructured data, essential for forming structured knowledge graphs.
F
Federated storage network: Decentralized network of nodes within PrivateAI responsible for secure data storage, ensuring high redundancy, availability, and data integrity.
G
Graph construction: Process in PrivateAI involving the creation of nodes (entities) and directed edges (relationships) to form a graphical representation of structured knowledge, aiding data visualization and analysis.
K
Knowledge graph: A structured representation of information within PrivateAI, derived from raw data to enhance data interpretability, facilitate detailed analysis, and support decision-making processes.
L
Large language model: Specialized AI model employed by PrivateAI for secure handling of sensitive intellectual property during data processing and analysis.
M
Model training: Process within PrivateAI where AI models, including Large Language Models, are trained using curated datasets to improve performance and accuracy in various applications.
N
Node participation: Incentivized engagement of network nodes within PrivateAI's ecosystem, ensuring scalability, resilience, and security in decentralized data storage and processing.
O
Open-source principles: Commitment by PrivateAI to transparency, collaboration, and community-driven development, promoting accessibility to code, tools, and resources for broader adoption and innovation.
P
PGPT tokens: Cryptocurrency tokens native to the PrivateAI ecosystem, used for transactions, data monetization, incentivizing data contributors, and accessing platform features.
Pre-processing: Initial data preparation stage in PrivateAI, involving data cleaning, feature extraction, and normalization to enhance data quality and usability for AI applications.
R
Reproducibility: Refers to the ability to achieve consistent results when using the same inputs in research and development. PrivateAI emphasizes reproducibility in constructing knowledge graphs by implementing rigorous criteria and processing steps.
S
Semantic models: AI models utilized by PrivateAI to detect anomalies in data, optimize datasets for Machine Learning applications, and ensure data accuracy and reliability.
Storage network: PrivateAI's decentralized storage network supports Gen3 and Gen4 SSDs, ensuring secure and accessible data storage.
T
Triplets: Basic units within PrivateAI's knowledge graphs, structured in the [subject, link, object] format to represent semantic relationships and enhance data understanding.