AI, machine learning, deep learning
The three terms people use interchangeably, what a model actually is, and where the boundary of "intelligence" sits.
Three nested ideas
AI is the goal: machines doing things we would call intelligent. Machine learning is the dominant method: instead of writing rules by hand, you show examples and let the program infer the rules. Deep learning is one family of ML methods — neural networks with many layers — which happens to work extremely well on images, audio and text.
| Term | What it means | Example |
|---|---|---|
| AI | The broad field | Chess engines, recommenders, chatbots |
| Machine learning | Learning patterns from data | Spam filter trained on labelled email |
| Deep learning | ML with deep neural networks | Speech recognition, image segmentation |
| Generative AI | Models that produce content | Text, images, code, audio |
| LLM | A very large text model | GPT-style, Claude, Llama, Mistral |
What a model really is
A trained model is a large set of numbers (parameters) plus a fixed computation graph. Training adjusts those numbers to reduce a loss — a number measuring how wrong the predictions are. Nothing in a model "knows" anything; it encodes statistical regularities of its training data.
# the shape of every supervised learning problem
X, y = load_data() # features and known answers
model = Model()
for epoch in range(EPOCHS):
preds = model(X)
loss = criterion(preds, y) # how wrong are we?
loss.backward() # gradients
optimizer.step() # nudge the parameters
optimizer.zero_grad()Two consequences follow directly from that definition. First, a model can only be as good as the data it saw — biases in, biases out. Second, it interpolates rather than reasons: give it an input unlike anything in training and the output is unreliable.
What current systems cannot do
- No guaranteed correctness. Outputs are plausible, not verified. Anything consequential needs a check or a deterministic fallback.
- No reliable self-knowledge. A model will state a wrong fact with the same confidence as a right one.
- Bounded context. It only "sees" what is in its window plus what tools let it fetch.
- Training cut-off. Facts change; the weights do not.
FAQ
Do I need maths to work with AI?
Is an LLM a database?
Related
The model lifecycle Machine learning in one page
Last refreshed 2026-09-18.