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Terminology

Welcome to the Jutsu Terminology page! This guide will help you understand the key terms and concepts used throughout our platform and documentation.

Terminology​

Welcome to the Jutsu Terminology page! This guide will help you understand the key terms and concepts used throughout our platform and documentation. Whether you're a beginner or an experienced user, this guide will provide you with clear definitions and explanations.

General Terms​

  • Artificial Intelligence (AI): The simulation of human intelligence in machines that are programmed to think and learn like humans.
  • AI Agent: An AI-powered software program that performs tasks, automates processes, and enhances productivity by leveraging artificial intelligence.
  • Machine Learning (ML): A subset of AI that involves the use of algorithms and statistical models to allow computers to learn and make decisions without explicit programming.
  • Natural Language Processing (NLP): A subset of AI that focuses on the interaction between computers and humans through natural language.
  • Deep Learning: A type of machine learning based on artificial neural networks with multiple layers, allowing the model to learn complex patterns.

Jutsu-Specific Terms​

  • Agent: An autonomous software entity that performs tasks on behalf of users, leveraging AI models to operate intelligently and efficiently.
  • AI Model: A pre-trained artificial intelligence algorithm provided by various vendors (e.g., OpenAI, Claude) that can be integrated into agents to enhance their capabilities.
  • API (Application Programming Interface): A set of protocols and tools that allow different software applications to communicate with each other. In Jutsu, APIs enable agents to interact with external data sources and services.
  • Bounty: A reward offered for completing specific tasks or projects within the Jutsu platform. Builders can earn tokens by successfully fulfilling these bounties.
  • Builder Studio: The suite of tools provided by Jutsu, enabling users to design and create AI agents with ease using no-code and low-code solutions.
  • Decentralized: A system where control and decision-making are distributed among a network of participants rather than centralized in a single entity.
  • ERC-20: A standard for tokens on the Ethereum blockchain that ensures interoperability between different tokens and applications. The Jutsu Token (JUT) follows this standard.
  • Governance: The process of making decisions about the rules and direction of the platform, often involving community voting and consensus.
  • Jutsu Platform: The comprehensive AI agent platform that allows users to create, deploy, and monetize AI applications with no-code tools and robust support.
  • Low-Code/No-Code: Development approaches that allow users to create applications or agents with minimal or no programming knowledge, using intuitive interfaces and visual tools.
  • Marketplace: A section of the Jutsu platform where users can publish and monetize their AI agents.
  • Micro-Royalty: A small, continuous payment that builders receive based on the usage and engagement of their agents. This ensures fair compensation for their contributions.
  • NFT (Non-Fungible Token): A unique digital asset that represents ownership of a specific item or piece of content, often used to verify ownership and provenance in digital art and collectibles.
  • Staking: The process of locking up tokens to support the operations of a blockchain network or platform, often earning rewards in return.
  • Tokenomics: The economic model and structure governing the distribution, utilization, and management of tokens within the Jutsu platform.
  • User Dashboard: The interface where users can manage their AI agents, access Builder Studio, and track performance metrics.
  • Validator: A participant in the blockchain network responsible for verifying transactions and maintaining the integrity of the ledger.
  • Wallet: A digital tool used to store, manage, and transact cryptocurrencies and tokens. In Jutsu, wallets are used to hold JUT tokens and interact with the platform.

Key Features and Capabilities​

  • Multi-modal Context Support: The ability of AI agents to understand and process inputs from various modes such as text, images, and voice.
  • Fine-tuning: Customizing a pre-trained AI model with specific data to improve its performance on particular tasks.
  • Interaction Modes: Different ways AI agents can interact with users, including text, speech, and visual inputs.
  • Horizontal Scalability: The ability to add more AI agents to handle increased workload without affecting performance.
  • Vertical Scalability: Enhancing the capabilities and processing power of existing AI agents to handle more complex tasks.
  • Self-Improvement (Reflection): The process by which AI agents regularly assess and improve their performance, similar to human self-review.
  • Tool Integration: The ability to incorporate external tools and resources, such as web search APIs and data processing utilities, to enhance AI agent functionality.

Security and Compliance​

  • Data Encryption: The method of converting data into a code to prevent unauthorized access.
  • GDPR Compliance: Adherence to the General Data Protection Regulation, a legal framework that sets guidelines for the collection and processing of personal data within the European Union (EU).
  • Privacy Policy: A statement that explains how Jutsu collects, uses, and protects user data.
  • Access Control: Mechanisms that restrict access to data and functionalities to authorized users only.
  • Audit Trail: A record that shows who has accessed a computer system and what operations they have performed during a given period.

Common Processes​

  • Training: The process of teaching an AI model to perform specific tasks by feeding it data and adjusting its parameters.
  • Deployment: The process of making an AI agent available for use in a real-world environment.
  • Monitoring: Continuously tracking the performance and behavior of AI agents to ensure they are operating correctly.
  • Feedback Loop: A system for collecting and analyzing user feedback to improve AI agent performance over time.
  • Version Control: A system that records changes to files or a set of files over time so that specific versions can be recalled later.

Creative and Advanced Concepts​

  • Generative AI: AI that can generate new content, such as text, images, or music, based on learned patterns from existing data.
  • Federated Learning: A machine learning technique where a model is trained across multiple decentralized devices using local data samples without exchanging them.
  • Reinforcement Learning: A type of machine learning where an agent learns to make decisions by performing actions and receiving rewards or penalties.
  • Explainability: The ability to understand and interpret the decisions made by an AI agent.
  • Ethical AI: The practice of developing AI systems that are fair, transparent, and accountable.
  • Energy-Efficient AI: Developing AI systems that consume less power and resources, reducing their environmental impact.
  • Zero-Shot Learning: An aspect of machine learning where a model can recognize and classify data it has never seen before.
  • Transfer Learning: A machine learning method where a model developed for a particular task is reused as the starting point for a model on a second task