An interactive explainer
From the chip that does the thinking to the number that holds the knowledge — explained plainly.
Artificial Intelligence is software that learns patterns from examples rather than following rules someone wrote down. Instead of a programmer saying "if the photo is dark and furry it's a cat," the AI looks at millions of cat photos and figures out the pattern itself.
AI is a program that improves at a task through experience, rather than being explicitly told how to do it.
// Core definitionModern AI — particularly the kind that powers chatbots, image generators, and language models — relies on four building blocks you'll see everywhere: CPUs, GPUs, Tokens, and Parameters. Here's what each one does.
Hardware
The general-purpose brain of a computer. Fast at sequential tasks, great at logic and decisions.
Hardware
Thousands of smaller processors working in parallel — perfect for the matrix maths AI needs.
Data
The unit of text an AI reads and writes — roughly a word or part of a word.
Knowledge
A number stored inside the AI model that encodes everything it learned during training.
MIT Open Learning launched Universal AI on 12 May 2026, a self-paced programme that takes learners from "AI novice to authority" with a free introductory course (Fundamentals of Programming and Machine Learning) and five further core courses plus six industry-specific courses — including Holistic AI in Medicine, AI and Entrepreneurship, and AI and Sustainability: Energy — on the MIT Learn platform. COAD's design assumes that the training infrastructure exists at this kind of quality globally and is no longer the binding constraint; what is missing is the income-bridge mechanism during the workforce-transition window, which COAD is designed to deliver. Universal AI is the kind of programme a COAD recipient cohort would access during the transition.
A Central Processing Unit is the main processor inside every computer. It's built around a small number of very powerful cores — modern CPUs typically have 8 to 32. Each core can handle complex logic, branch decisions, and general-purpose tasks at extreme speed.
In AI, the CPU handles everything around the model: loading files, managing memory, running the operating system, and pre-processing data. But it isn't ideal for the actual AI calculations — those require doing millions of simple maths operations at the same time, which is where GPUs shine.
9-core CPU — each core handles complex tasks
A Graphics Processing Unit was originally designed to render video games — a task that requires updating millions of pixels at exactly the same time. Researchers discovered those same parallel processing abilities are perfect for AI training, where you need to multiply enormous matrices of numbers simultaneously.
Modern AI GPUs (like NVIDIA's H100) contain thousands of smaller, simpler cores. Each core is far less powerful than a CPU core, but together they can process AI calculations hundreds of times faster than a CPU alone.
400 cores — all firing in parallel
AI language models don't read text the way humans do. They break everything into chunks called tokens. A token is roughly a word, a part of a word, or a punctuation mark — whatever the model's vocabulary system decides is useful.
Every time you type a message to an AI, it's converted into a list of tokens before the model sees it. Every word the AI writes back is also generated token by token. Token count is how AI companies measure usage — it covers both what you sent and what the AI replied.
Try this sentence broken into tokens:
"The neural network learns from data"
Parameters are the numbers that define what an AI model knows. During training, the model sees billions of examples and slowly adjusts these numbers until they encode the patterns of language, logic, and knowledge it was trained on.
You can think of each parameter as a tiny dial. Individually, one dial means nothing. But billions of dials — each fine-tuned through training — together create something that can write, reason, translate, code, and explain. When people say an AI has "7 billion parameters," they mean it contains 7 billion of these learned numbers.
Line thickness = parameter weight strength
When you send a message to an AI, all four pieces are in play simultaneously.
Step 1 — Tokenisation
Your text becomes tokens
The CPU receives your message and converts it into a list of tokens — numerical IDs the model understands. "Hello, how are you?" might become [15496, 11, 703, 389, 345, 30].
Step 2 — CPU Orchestration
The CPU manages the request
The CPU loads the token list into memory, coordinates with the GPU, and handles the surrounding infrastructure — network, memory management, security checks.
Step 3 — GPU Computation
Thousands of GPU cores crunch the maths
The GPU takes each token and runs it through the model — billions of multiplications and additions across the parameter matrix. This is the "thinking." The GPU can do many of these in parallel, which is why responses are fast.
Step 4 — Parameters activate
Billions of learned numbers shape the answer
As the GPU runs the computation, it passes data through the model's parameters — billions of learned numbers that encode the AI's knowledge of language, facts, and reasoning. These numbers determine what the next token should be.
Step 5 — Output tokens
Tokens become words, one by one
The model predicts the most likely next token. Then the next. Then the next — repeating the GPU computation for each one until the reply is complete. The CPU then assembles those token IDs back into readable text and sends it to you.