AI Terminology

  𝗔𝗜: The overarching world of Artificial Intelligence, transforming industries.
- 𝗠𝗟: Machine Learning's role in shaping intelligent systems.
- 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Neural networks for human-like thinking.
- 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸: Mimicking human brain functions for learning.
- 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Teaching computers with labelled examples.
- 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Machines finding patterns without labels.
- 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Trial-and-error learning for machines.
- 𝗡𝗟𝗣: Tech enabling computers to understand human language.
- 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻: Machines interpreting visual information.
- 𝗖𝗵𝗮𝘁𝗯𝗼𝘁: Conversational AI for customer support and more.
- 𝗜𝗢𝗧: Devices connected, sharing data for smart applications.
- 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Remote storage, management, and data processing.
- 𝗕𝗶𝗮𝘀 𝗶𝗻 𝗔𝗜: Addressing unintentional biases in algorithms.
- 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺: Core of AI, the building block for intelligent systems.
- 𝗗𝗮𝘁𝗮 𝗠𝗶𝗻𝗶𝗻𝗴: Extracting patterns and insights from vast datasets.
- 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮:Navigating challenges with massive and diverse data.
- 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀: Merging AI with physical machines for automation.
- 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗙𝗮𝗶𝗿𝗻𝗲𝘀𝘀: Ensuring fairness and avoiding bias in AI.
- 𝗧𝗿𝗮𝗻𝘀𝗳𝗲𝗿 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Applying knowledge for enhanced AI efficiency.
- 𝗘𝗱𝗴𝗲 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: Localised AI implementation for efficiency.
- 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜: Making AI decisions transparent and understandable.
- 𝗚𝗔𝗡𝘀: AI creating realistic data through adversarial networks.
- 𝗘𝗱𝗴𝗲 𝗔𝗜: Localised AI for reduced reliance on centralised servers.
- 𝗔𝗜 𝗘𝘁𝗵𝗶𝗰𝘀: Guiding principles for responsible AI development.