AI Mistakes and Limitations
Machines make mistakes; they only know what they've been shown; bad training data leads to bad results; AI is not magic — just maths on data; showing edge cases and failures
What a learner can do afterwards
- Give an example of AI making a mistake (voice assistant mishearing, auto-correct error, wrong recommendation)
- Explain that AI mistakes happen because of gaps or errors in training data
- Describe why AI is not magic — it follows mathematical rules applied to data
The lesson
AI is not magic. It is maths, run on lots of examples called training data. A computer looks for patterns in things it has seen before and makes its best guess. Sometimes that guess is wrong.
Some AI programs once mixed up photos of muffins and photos of chihuahua puppies. Both can look round, brown, and speckled in a photo. The AI had not seen enough different examples to tell them apart, so it guessed wrong.
AI only knows what it has been shown. If the training pictures or sounds were missing something, or had mistakes in them, the AI's guesses can be wrong too. Better examples usually mean better guesses.
AI is not magic, it is maths trained on examples, so it can make mistakes, especially on things it has not seen much before.
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Where it sits
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Where this leads
Jobs that lean on this skill. Follow one to see everything it is built on.
8 questions wait behind this lesson, each with its answer explained. Every answer feeds the sky: stars light as they are learned, and dim when it is time to come back.