ARTIFICIAL INTELLIGENCE

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Across
  1. 6. A type of machine learning that deals with data that does not have labeled responses, allowing the model to find patterns and relationships on its own.
  2. 7. The branch of technology that deals with the design, construction, operation, and use of robots, often incorporating AI to enhance functionality.
  3. 8. Facts and statistics collected for reference or analysis, which AI systems use to learn and make decisions.
  4. 11. The moral principles that govern a person's or group's behavior, important in AI to ensure technology is used responsibly and fairly.
  5. 12. A set of rules or instructions that a computer follows to complete a task or solve a problem.
  6. 13. The use of technology to perform tasks without human intervention, often used in AI to increase efficiency.
  7. 14. A field of AI that enables machines to interpret and make decisions based on visual information from the world.
  8. 15. A field of AI that focuses on the interaction between computers and humans through natural language, enabling machines to understand and respond to human speech.
Down
  1. 1. A type of machine learning where a model is trained on labeled data, meaning the input data is paired with the correct output.
  2. 2. A subset of AI that allows computers to learn from data and improve their performance over time without being explicitly programmed.
  3. 3. A computer system inspired by the human brain that is designed to recognize patterns and process data in a way similar to how humans learn.
  4. 4. The mental action or process of acquiring knowledge and understanding through thought, experience, and the senses, which AI systems attempt to mimic.
  5. 5. Techniques that use statistical algorithms and machine learning to identify the likelihood of future outcomes based on historical data.
  6. 9. A branch of computer science that aims to create machines that can perform tasks that typically require human intelligence, such as understanding language and recognizing patterns.
  7. 10. A tendency to favor one thing over another, which can affect AI systems if the data used to train them is not representative or fair.