Combined Basics of AI Terminology

Artificial Intelligence (AI)

Definition: A branch of computer science that enables machines to perform tasks that typically require human intelligence, such as learning, decision-making, and problem-solving.
Example: AI is like a really smart assistant that can help you plan a trip, suggest recipes based on what’s in your fridge, or even recognize your voice when you ask your phone a question.

Algorithm

Definition: A set of rules or steps a computer follows to solve a problem or complete a task.
Example: An algorithm is like a recipe for baking a cake—it gives the computer step-by-step instructions on how to solve a problem.

Machine Learning (ML)

Definition: A type of AI where computers learn from data and improve their performance over time without being explicitly programmed.
Example: Imagine teaching a child to recognize dogs. You show them many pictures of different dogs, and over time, they get better at identifying them. That’s how machine learning works—by learning from lots of examples.

Data

Definition: Information that computers use to learn, such as text, images, or numbers.
Example: Data is like the ingredients in a recipe. If AI is a chef, it needs good ingredients (data) to make something useful.

Neural Network

Definition: A computer system designed to mimic how the human brain processes information, often used in advanced AI tasks like image recognition and natural language understanding.
Example: A neural network is like the way your brain learns to recognize faces. It connects bits of information to figure out who someone is, just like AI connects different pieces of data to make decisions.

Deep Learning

Definition: A type of machine learning that uses large neural networks with many layers to analyze complex patterns in data.
Example: Deep learning is like a detective who has learned to recognize tiny clues in a mystery. The more details they notice, the better they get at solving the case.

Natural Language Processing (NLP)

Definition: A field of AI focused on enabling computers to understand, interpret, and respond to human language.
Example: NLP is like teaching a dog to understand commands. If you say “sit” or “stay,” the dog learns what you mean. AI does the same with human language.

Chatbot

Definition: A computer program designed to simulate a conversation with a human, often used in customer service or virtual assistants.
Example: A chatbot is like a digital waiter. You ask for a menu, place an order, and it responds with what you need—without a human involved.

Training

Definition: The process of teaching an AI system by feeding it data so it can learn to make decisions or predictions.
Example: Training AI is like teaching a toddler new words. At first, they don’t understand, but with enough repetition and examples, they learn to use the words correctly.

Model

Definition: The result of the AI training process, which is used to make decisions or predictions based on input data.
Example: A model is like a finished jigsaw puzzle—it’s the result of AI learning from many pieces of data and putting them together to make predictions.

Prediction

Definition: A guess made by an AI system about what will happen or what something means, based on the data it has analyzed.
Example: AI making a prediction is like a weather forecast. It looks at past weather patterns to guess if it will rain tomorrow.

Bias

Definition: When an AI system produces unfair or inaccurate results due to biased data or flawed design.
Example: If you only ever show a child pictures of small dogs, they might think all dogs are tiny. AI bias happens when AI learns from limited or skewed data.

Automation

Definition: The use of AI and other technologies to perform tasks without human involvement.
Example: Automation is like using a coffee machine instead of making coffee by hand. The machine does the work for you.

Ethics in AI

Definition: The study of how to use AI responsibly, ensuring it benefits society while avoiding harm.
Example: Ethics in AI is like setting rules for fair play in a game—making sure the AI doesn’t cheat or treat some players unfairly.

Artificial General Intelligence (AGI)

Definition: A theoretical type of AI that could perform any intellectual task that a human can do. It does not exist yet.
Example: AGI would be like a robot that could do anything a human can—cook, learn, talk, and even feel emotions. But right now, it’s just science fiction.

Supervised Learning

Definition: A type of machine learning where the AI is trained on labeled data (e.g., pictures of cats labeled as “cat”).
Example: Supervised learning is like a teacher grading a student’s math homework and giving feedback so they learn the correct answers.

Unsupervised Learning

Definition: A type of machine learning where the AI tries to find patterns in unlabeled data (e.g., grouping similar images without knowing what they are).
Example: Unsupervised learning is like sorting a box of mixed-up socks by color, even if no one tells you what colors they are.

Reinforcement Learning

Definition: A type of machine learning where the AI learns by trial and error, receiving rewards or penalties based on its actions.
Example: Reinforcement learning is like training a dog with treats—if it does a trick correctly, it gets a reward and learns to repeat the behavior.

Turing Test

Definition: A test designed to determine if a machine’s behavior is indistinguishable from a human’s.
Example: The Turing Test is like a phone conversation where you can’t see the person. If you can’t tell whether it’s a human or a robot talking, the AI has passed the test.

Artificial Intelligence Assistant

Definition: A virtual helper, like Siri or Alexa, that uses AI to answer questions or perform tasks.
Example: AI assistants like Siri or Alexa are like personal secretaries. They help you set reminders, check the weather, and answer your questions.

Cloud Computing

Definition: Storing and accessing data and AI services over the internet instead of on your computer.
Example: Cloud computing is like streaming music from Spotify instead of downloading songs to your phone—it’s stored elsewhere, but you can access it anytime.

API (Application Programming Interface)

Definition: A tool that allows different software programs to communicate with each other, often used to integrate AI capabilities into applications.
Example: An API is like a waiter in a restaurant. You tell the waiter your order (request), they deliver it to the kitchen (the system), and then they bring you the food (response).

Big Data

Definition: Extremely large datasets that AI systems use to learn and make predictions.
Example: Big data is like the massive collection of photos in your phone’s gallery—it helps AI recognize patterns, like grouping pictures by location or people’s faces.

Robotics

Definition: The branch of technology that involves designing, building, and operating robots, often incorporating AI to make robots smarter.
Example: Robotics is like building a self-driving vacuum cleaner—it moves, makes decisions, and cleans without human help.

Computer Vision

Definition: A field of AI that allows computers to interpret and process visual information from the world, like recognizing faces in photos.
Example: Computer vision is like a toddler learning to recognize their parents’ faces—it sees shapes, colors, and patterns to understand images.

Generative AI

Definition: A type of AI that creates new content, such as text, images, or music, often based on patterns in the data it has learned from.
Example: Generative AI is like a creative artist—it can write stories, create paintings, or compose music based on patterns it has learned.

Token

Definition: A small piece of text or data that an AI model processes when analyzing or generating language.
Example: A token is like a single word in a book. AI reads words piece by piece to understand and generate text.

Prompt

Definition: A question or statement used to guide an AI system’s response.
Example: A prompt is like asking a magic genie for a wish—the way you phrase it affects the answer you get.

Feedback Loop

Definition: The process of improving an AI system by using its results to make adjustments and refine its performance.
Example: A feedback loop is like improving a recipe over time—each time you cook it, you tweak the ingredients to make it better.

Explainability

Definition: Making AI systems and their decisions understandable to humans.
Example: Explainability is like showing your math work on a test—so others can understand how you got your answer.