AI Vocabulary Crossword

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Across
  1. 2. Intelligence The field of computer science where machines are designed to perform tasks that normally require human intelligence, such as problem-solving, learning, and decision-making.
  2. 3. Learning The AI learns from labeled examples (data that already has the correct answers). Example: teaching an AI to recognize cats by showing it many cat photos labeled “cat.”
  3. 6. Network A type of AI model inspired by the human brain. It uses layers of “neurons” (math functions) to recognize patterns and learn from data.
  4. 9. Learning The AI learns by trial and error, receiving rewards or penalties based on its actions. Example: training a robot to walk by rewarding steps taken in the right direction.
  5. 10. A step-by-step set of rules or instructions that a computer follows to solve a problem or perform a task.
  6. 11. Ethics The study of moral issues and responsibilities related to AI—like fairness, bias, privacy, job impact, and making sure AI benefits society safely.
  7. 13. Vision The field of AI that enables machines to “see” and interpret images or video (like facial recognition or self-driving car cameras).
  8. 14. AI AI that follows predefined “if-then” rules made by humans instead of learning from data.
Down
  1. 1. AI AI that is specialized in one specific task (like recommending songs or recognizing faces). It is the type of AI we mostly use today.
  2. 4. AI A hypothetical AI that is smarter than humans in every way—able to think, reason, and create beyond human capability.
  3. 5. AI A type of AI that could understand, learn, and apply intelligence to any problem, just like a human. (It does not exist yet.)
  4. 6. Language Processing The field of AI that helps machines understand, interpret, and generate human language (like chatbots or translation tools).
  5. 7. Using technology or machines to perform tasks with little or no human involvement (for example, self-checkouts or robots on factory lines).
  6. 8. Chatbots Programs that use AI, often NLP, to simulate conversations with humans (like customer service bots or virtual assistants).
  7. 12. Learning The AI looks for patterns in data without labels. Example: grouping customers by shopping habits without telling the AI what those groups mean.