Machines that can think, learn, and solve problems on their own.
Imagine a robot that can do homework, tell jokes, or even solve puzzles just like you do. It's like a friendly assistant who gives you ideas or helps you with your to-do list.
Algorithm
/ˈalɡərɪð(ə)m/
A step-by-step set of instructions for solving a problem or completing a task.
It's like a recipe that tells you how to make a dish—each step leads to the final result
Edge AI
/ɛdʒ eɪˈʌɪ/
AI that processes data close to where it's being collected, rather than sending it to a distant server.
Imagine having a bakery in your neighborhood instead of ordering bread from another city.
Tokenization
/ˌtəʊ.kən.aɪˈzeɪ.ʃən/
The process of breaking text into smaller pieces (tokens), such as words or phrases, for easier processing by AI
It’s like chopping a big paragraph into smaller, manageable chunks for the AI to understand.
Turing Test
/ˈtjʊərɪŋ tɛst/
A test to see if a machine can mimic human behavior so well that a person can't tell if it's a machine.
It’s like having a conversation with a robot and not being able to tell if you're talking to a human or a machine.
Natural Language Processing (NLP)
/ˈnætʃ(ə)rəl ˈlæŋɡwɪdʒ ˈprɑːsɛsɪŋ/
A field of AI that helps computers understand and interact with human language.
It's like teaching a computer to read, understand, and respond to text or speech the way a person would.
Gen AI (Generative AI)
AI that creates new things like stories, pictures, or music by learning from existing data.
It’s like an artist who creates new works by learning from other artists.
Overfitting
When AI learns too much from the examples it’s given, and gets confused by new, unseen data.
It’s like memorizing a test but not understanding the subject—you can answer the questions but get stuck on new ones.
Reinforcement Learning
A type of learning where an AI learns by receiving rewards or punishments for its actions.
It’s like training a dog, where it gets a treat when it does something right, and learns not to do it when it’s wrong.
Swarm Intelligence
A type of AI that mimics the behavior of groups of creatures like ants or bees, solving problems together.
It’s like how a flock of birds can move in perfect sync without anyone leading them.
Explainability
The ability for an AI to explain how it made a decision or reached a conclusion.
It’s like having a teacher who can show you exactly how they solved a problem step by step.
Prompt Engineering
The practice of designing inputs (prompts) for AI systems to get the desired outputs.
It’s like crafting the perfect question to get the best answer from an AI.
Backpropagation
A method for training neural networks by adjusting weights based on errors made in predictions
It’s like going back through your work, finding mistakes, and fixing them to improve your results.
Hyperparameters
Settings that control the learning process of an AI model, such as learning rate or number of layers.
It’s like adjusting the settings on your phone to get the performance you want.
Black Box
A system where you can see the inputs and outputs, but not how the machine works inside
It's like a sealed box—you're not sure what’s happening inside, but you know what comes out.
Edge Computing
This is when computers do the thinking or work near where the action is happening, instead of sending everything far away to a big computer.
It’s like having a bakery in your neighborhood, instead of ordering bread from another city.
Mixture of Experts (MoE)
A smart way where different computer brains (experts) help with different tasks, and the smartest one decides who to ask for help.
Imagine a group of experts—one for cooking, one for painting, one for fixing cars—and you ask the right one for help every time.
AGI (Artificial General Intelligence)
A super-smart computer that can think and learn just like a human, doing many different things.
Imagine if a robot could cook, read, play games, and learn new things just like you!
ASI (Artificial Superintelligence)
This is an AI that is much smarter than humans and can solve problems we can’t even think of.
It’s like having a super-genius robot that can answer every question and fix every problem instantly.
AI Agent
An AI that can make decisions and take action by itself, like a helper robot.
It’s like having a robot that knows when to clean your room or make your bed without being asked.
Agentic AI
A smarter AI that not only makes decisions but also takes action by itself to do things.
Imagine a robot that doesn’t need you to tell it what to do—it just knows what to do, like cleaning your house or organizing your books.
Human-data
This is all the information about people’s habits, likes, and actions that AI can learn from.
It’s like a scrapbook that stores all the things people do and like, so the AI can understand what people might need next.
Synthetic Data
Fake data that’s made by computers, used when real data is too hard to get.
It’s like drawing a picture of a dog when you can’t see one, but you know what a dog looks like from books.
AI Hallucination
When an AI makes up something that isn’t true, even though it sounds convincing.
It’s like when a friend tells you a crazy story that sounds real, but you find out it’s all made up.
LLMs (Large Language Models)
These are computer brains trained to understand and talk like humans, based on tons of reading.
Imagine a super-smart robot that can read all the books in the world and then have a conversation with you.
Multi-Modal
AI that can use different types of data, like pictures, sounds, and words, all at once.
It’s like a superhero that can see, hear, and talk at the same time, making it extra powerful.
Ethical AI
AI that is made to be fair and kind, treating everyone equally.
It’s like having a fair teacher who treats all students the same and never plays favorites.
Responsible AI
AI that works in a way that’s safe, fair, and good for people.
Imagine a superhero who always uses their powers for good and never hurts anyone.
AI Governance
The rules that control how AI should work to make sure it’s safe and fair for everyone.
It’s like the rules for a game, making sure everyone plays fairly and no one cheats.
Enterprise AI
AI used by big companies to help them do their work more quickly and easily.
It’s like a helper robot in a big office, sorting papers and doing all the boring tasks so humans can do the fun stuff.
Neural Networks
Computer systems that try to work like the human brain to process information and learn.
Imagine a network of little helpers passing messages to each other to solve a big puzzle.
Deep Learning
A type of learning where AI uses many layers of information to understand complex things.
It’s like a cake with many layers, where each layer adds something extra to the final result.
Inference
The process of using learned information to make decisions or predictions.
It’s like a weather forecast based on what’s happened in the past.
Training Data
The information used to teach AI so it can learn how to do things.
It’s like studying from a textbook to learn how to solve math problems.
AI Bias
When AI makes unfair decisions because it’s been trained on biased or incomplete data.
It’s like a teacher who only teaches about one group of people and ignores everyone else.
Supervised Learning
A type of learning where AI is given correct answers during training to help it learn.
Imagine a teacher giving you the correct answers to practice problems so you can learn faster.
Unsupervised Learning
AI learns by looking for patterns in data without being told the right answers.
It’s like sorting a big pile of books into categories without knowing which category they belong to.
Data Augmentation
The process of making more training data by slightly changing what’s already available.
It’s like taking a picture of a dog and rotating it to make a new image for the AI to learn from
Transfer Learning
Teaching AI to use knowledge from one area and apply it to a new area.
It’s like learning to ride a bike and then using that knowledge to learn how to ride a scooter.
Zero-shot Learning
A type of learning where AI can make predictions about things it’s never seen before.
It’s like guessing the ending of a new movie without having seen it before, just based on what you know from other movies.
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