How does AI work?
Neurons and weights, learning from mistakes, and predicting the next word.
Transcript
An AI model is a huge network of simple artificial neurons, arranged in layers.
Each link has a weight, a number for how strongly one neuron influences the next.
To learn, it makes a guess, compares it with the right answer, and measures the error.
Then it nudges every weight to shrink that error, over billions of examples.
A chat AI is trained on one simple task: predict the next word.
Add a likely word, then repeat, and the patterns it learned become whole sentences.
Data in, patterns learned, predictions out. That is how AI works.
More in this series
1:13How does AI read a sentence?
Tokens, embeddings and attention, stacked in layers to score the next token.
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.