Start with the machine.

Artificial intelligence becomes easier to understand once you stop treating it like magic. At its core, it is a system that takes in information, processes it, and produces an output.

What is AI?

AI is software designed to perform tasks that normally require abilities we associate with human intelligence.

That can include understanding language, recognizing patterns, interpreting images, making predictions, generating content and taking actions.

Underneath everything is code.

Computers do not begin with words, pictures or ideas. At the physical level they work with electrical states, which we represent as binary: ones and zeros.

Those tiny states combine into instructions, memory, software and eventually the systems we experience on a screen.

Traditional software follows rules.

In ordinary software, people write explicit instructions telling the computer what to do: compare this, calculate that, save this value, move here if a condition is true.

Machine learning changes the method.

Instead of writing every rule by hand, we can give a system examples and allow it to adjust internal numerical relationships until it becomes better at recognizing patterns.

Example, guess, compare, adjust, repeat.

Neural networks.

Neural networks are one way of doing machine learning. They use layers of connected numerical relationships to detect increasingly complex patterns.

Deep learning simply means using neural networks with many layers.

Large language models.

Large language models are deep-learning systems trained on enormous amounts of language. They learn statistical relationships between pieces of text and use those relationships to generate responses.

They are not encyclopedias. A better mental model is a vast network of learned relationships.

What AI can do.

A useful way to understand modern AI is by asking four questions:

What can it create?

What can it analyze or predict?

What can it see, hear or interpret?

What can it actually do?

The human layer.

The technology is only part of the system.

Goals, judgment, process design, implementation and context still determine whether AI creates value or simply adds another layer of complexity.