Tech & Future

AI & Machine Learning

Understand the machines rewriting everything

Novice

Mastery0%

Novice

Note. AI tools change fast and confidently get things wrong. Verify anything high-stakes against primary sources, and never paste secrets/PII into untrusted endpoints.

Subdomain

Safety and Ethics

Where AI quietly causes harm: bias, hallucination, privacy leaks, and misuse. The questions to ask before you ship a model into someone's life.

Spot a dataset bias before it turns into a product bias.
Add a human-in-the-loop check on any decision that affects a person.
Walk away from a use case where the failure cost outweighs the benefit.

Concepts (2)

All AI & Machine Learning glossary

Back to overview

Parameter
A tunable weight inside a model adjusted during training.
RAG
Retrieval-Augmented Generation, giving a model external documents to ground its answers.
Overfitting
Memorising training data so well the model fails on new inputs.
Inference
Using a trained model to produce outputs.
Fine-tuning
Continuing to train a model on a smaller, more specific dataset to specialise it.
Embedding
A vector of numbers representing the meaning/features of a chunk of input.
RLHF
Reinforcement Learning from Human Feedback, alignment via human ratings.
Hallucination
A fluent, confident output that's factually wrong.
Token
The smallest input/output unit an LLM works with, typically 3 to 4 characters in English.
Context window
The maximum tokens a model can consider in a single forward pass.
Vector database
A store optimised for nearest-neighbour search over embeddings.
MoE
Mixture of Experts. Routes tokens to specialised sub-networks so active parameters per token stay small.