01 / ARTIFICIAL INTELLIGENCE
Patterns.
Predictions.
Possibilities.
AI can recognize patterns and generate useful ideas. Understanding how it learns helps us ask better questions about when to trust it.
A simplified learning pipeline. What comes out depends on the examples, the model and the task.
How does a
machine learn?
Start with supervised learning: the model gets examples paired with checked answers.
Find a pattern
Imagine sorting messages into spam and not spam. During training, a model adjusts internal values to make its predictions fit labeled examples.
Try something new
A good score on practice examples is not enough. Test on held-out examples that resemble the situations where you want to use the model.
Keep checking
A prediction is an output, not a guarantee. An unfamiliar input, an unrepresentative dataset or an unreliable label can lead to a mistake.
THE IMPORTANT DISTINCTION
Confident isn’t
the same as correct.
Generative AI produces new text, images or other outputs from learned patterns. A fluent answer can still contain a false claim or an invented source.
For a claim that matters, find the original evidence, check that it supports the exact claim, and notice what is still uncertain. Asking another bot to agree is not an independent fact-check.
Background: IBM: machine learning · NIST: managing AI risks
A 2-MINUTE THOUGHT EXPERIMENT
What did the model really learn?
Every training photo of a wolf has snow in the background. Every dog photo has grass.
What shortcut might a model learn?
It could use the background as a clue rather than the animal’s features. This is an illustrative example, not a report of a particular system.
How could you test that possibility?
Try wolf photos without snow and dog photos in snow. Keep those cases separate from the training examples so they can reveal the shortcut.
A useful question: what examples are missing?
PUT CURIOSITY TO WORK
Read it. Try it. Question it.
Explore new AI research, or take a paper-first investigation into your classroom.