How AI learns
Training, models, and why memorising isn't learning. · ~15 minutes
- Machine Learning (ML): Teaching a computer by showing it lots of examples instead of writing step-by-step instructions.
- Training Data: The examples an AI learns from.
- Training: The learning phase: the AI makes guesses, gets told how wrong it was, and adjusts itself a little. Repeated millions of times.
- Parameters: The adjustable knobs inside an AI. Training sets their values.
- Overfitting: When an AI memorises its practice examples instead of learning the general idea, so it fails on new ones.
- Inference: Using an already-trained AI to get an answer.