1. Artificial Intelligence (AI): This is like giving computers the ability to think and learn so they can perform tasks that usually require human intelligence, such as understanding language or recognizing objects in pictures.
2. Machine Learning (ML): A way of teaching computers to learn from data and experiences and get better over time, much like you learn from studying or practicing a sport.
3. Deep Learning (DL): A more advanced type of machine learning that uses structures called neural networks to process information in complex layers, allowing the computer to recognize patterns and make decisions.
4. Neural Network: A design for computers that's inspired by the human brain, which helps machines recognize patterns and sort information in a way that mimics how our brains work.
5. Algorithm: A specific set of rules or steps that a computer follows to solve a problem or complete a task, similar to a recipe that you might follow to bake a cake.
6. Data Mining: The process where computers sift through large amounts of data to find patterns or insights, like looking for needles in a haystack, but the needles are valuable pieces of information.
7. Natural Language Processing (NLP): This is how computers are taught to understand and respond to human language, allowing them to read texts, listen to speech, and even write or speak in response.
8. Robotics: The technology that deals with the design and operation of robots — machines that can perform tasks automatically, often in environments that are unsafe or unpleasant for humans.
9. Cognitive Computing: Creating computers that can solve problems by reasoning and learning much like a human brain, not just by following pre-set rules.
10. Supervised Learning: A method where computers learn from examples that have known answers. It's like having a teacher who tells you if your answers are right or wrong while you're learning.
11. Unsupervised Learning: A method where the computer looks for patterns and relationships in data without any specific goal in mind, akin to exploring a new game where you have to figure out the rules as you go.
12. Reinforcement Learning: A strategy where computers learn to make decisions by trying different actions and seeing what results they get, much like learning to play a video game by trying different moves to see which one gets you the highest score.
13. Computer Vision: The ability of computers to interpret and understand visual information from the world, such as identifying objects in images or videos.
14. Chatbot: A computer program designed to simulate conversation with human users, especially over the internet, so you can ask questions or get help without needing a real person.
15. Bias in AI: This happens when an AI system shows unfair preferences or prejudices because of flawed data or poor design, which can lead to unjust or prejudiced outcomes.
16. Ethics in AI: The study of moral principles and questions about what is right and wrong in the development and use of AI, ensuring that AI technologies benefit and do not harm people.
17. AI Model: This is the specific 'brain' or program that an AI system uses after it's been trained to perform tasks like recognizing speech or images.
18. Training Data: The examples used to teach AI systems how to do their tasks, much like your study materials for a subject in school.
19. Testing Data: New examples used to check how well an AI system has learned after training, similar to taking a test after studying to see how much you've learned.
20. Feature: A characteristic or piece of information that a computer uses to understand data, like noticing the specific features of a face to recognize a person.