How does AI read a sentence?
Tokens, embeddings and attention, stacked in layers to score the next token.
Transcript
Text is chopped into tokens, and each token becomes a list of numbers, called an embedding.
Words with similar meanings get similar numbers, so cat lands near dog.
To read a word, the model looks at every other word, and decides which ones matter.
That is attention. It points to animal, but change the ending, and it points to street.
Dozens of stacked layers refine the meaning of every word.
At the top, it scores the next token, and the loop starts again.
Tokens in, attention across them, one next word out. Repeat that, and you get a conversation.
More in this series
1:14How does AI work?
Neurons and weights, learning from mistakes, and predicting the next word.
1:17What are billions of parameters?
What a parameter is, how big a billion is, and why big models need racks of GPUs.
1:26How do networks learn from errors?
Loss as a landscape, gradient descent and backpropagation.
1:24How is a chatbot trained?
Pretraining, fine-tuning and human feedback turn a text predictor into an assistant.
1:23What does temperature do?
Scores become probabilities, and temperature sharpens or flattens them.
1:19How does AI draw pictures?
Diffusion models add noise to learn, then remove it to create, steered by a prompt.
1:15How can AI use your own documents?
Retrieval-augmented generation: embed, retrieve, augment the prompt, generate.
1:14Why does AI make things up?
Likely is not the same as true: gaps, snowballing errors, and what helps.
1:16How much can an AI remember?
The context window, forgetting, the cost of long inputs, and workarounds.
1:09What is an AI agent?
A model in a loop with tools and guardrails.
1:12Can AI be biased?
Skewed data, where bias comes from, proxies, and how to audit and fix it.
1:10What is overfitting?
Underfit, good fit and overfit curves, train vs test error, and the fixes.