Imagine if a robot could do everything you can: from playing football to painting pictures, and even making jokes. That's AGI! It can understand, learn, and do lots of different things, just like humans.
Imagine you're playing with a big box of Lego. If you have to build something, it's easier if you group similar pieces together. You put all the blue pieces in one pile, the yellow ones in another, and so on. That's what 'chunking' in computers is like. It's about grouping similar things together to make them easier to understand.
Imagine reading a book and only remembering the last few words you've read. That's what a context window in AI does. It's like a spotlight in the dark that can only see a certain number of words at a time.
Remember when you learned to ride a bike? At first, you had to think about every little thing, like pedalling and balancing. After a while, you could do it automatically, almost like your brain had layers: one for pedalling, one for balancing, and one for steering. Deep Learning is like that. It's a way for computers to learn things in layers, each one learning something different.
Imagine every word is a different toy in a huge toy box. Each toy has its own special spot in the box that helps you know more about it, like its colour, size, or what it does. Embeddings do the same but with words or other things in the computer's brain.
Imagine if you could learn to play a game really well just by watching someone else play it a few times. That's few-shot learning! The AI can learn from just a few examples.
You know how you use LEGO bricks to build all sorts of different things? A foundation model is like the base or first few layers of your LEGO construction that other things can be built upon.
Imagine you and your friend having a drawing competition where you make a picture and your friend tries to tell if it's real or fake. You keep improving your drawings based on your friend's feedback. That's what GAN does, but with computer programs.
Imagine you have a giant toy robot that loves to read books. After reading lots of books, it starts to understand and use the language just like humans do. That's what a LLM does, but with computer codes and data instead of physical books.
Imagine you're teaching your puppy to sit. You say "sit" and when the puppy sits, you give a treat. The puppy is learning what "sit" means by your instructions and rewards. This is a lot like machine learning – computers learning to do something (like recognising a photo or understanding speech) by being trained with lots of examples.
NLP is like having a secret decoder ring for understanding and talking in computer language. It helps computers understand and respond to us in human language.
A neural network is like a team of brainy ants. Each ant doesn't know much on its own, but together they can solve big problems. Each ant, or "neuron", takes in some information, does a tiny bit of thinking, and then passes its results on to the next ants. By working together, they can do things like recognise pictures or understand speech.
Think about a teacher who gives you a question in a special way that helps you give a better answer. That's what prompt engineering is like. It's about asking the AI in the right way to get the best answer.
Imagine if you played a video game and every time you made a mistake, a friend corrected you. That's RLHF! The AI learns from human feedback, just like you learn from your friend.
Imagine your teacher helping you to understand your maths homework by showing you how to solve a lot of similar problems. That's supervised learning! The AI learns by being shown examples.
A token in AI is like a piece in a puzzle. Just like a big puzzle is made of many small pieces, a sentence or paragraph in AI is made of many tokens, usually words or parts of words.
Imagine a machine that chops up a big chocolate bar into little pieces so you can share it with friends. A tokeniser in AI is like that! It chops up text into smaller parts called tokens.
Imagine learning to play a game without anyone teaching you the rules, just by trying it out and understanding it yourself. That's unsupervised learning! The AI learns without being given any examples.
Think of a vector database as a huge digital library where each book (vector) has a specific place. This makes it easy to find exactly the book (vector) you want quickly.
Imagine you've trained a dog to sit, stay, and roll over. One day, you ask it to fetch, and it does it without ever being taught! That's zero-shot learning, where the AI can do tasks without seeing examples.