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AI · Theory

How AI Works

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Theory

12/215

How AI Works

Loop: collect data → pick a representation (features or pixels) → train or write rules → test on held-out examples → deploy → watch errors → update. Training means adjusting weights so predictions match labels (supervised) or find structure (unsupervised).

Board: 100 ham / spam emails. Split 80 train / 20 test. Learn. Score the 20. If test is weak, you overfit. That loop is “how AI works” for a fresher — not a 50-layer diagram on day one.

How AI Works — output — train has 4 labels, test has ['spam']. You score only on test.

Diagram
data → train / test split
           │
           ▼
         model
           │
           ▼
        predict → monitor
Exam tip

The loop + why a test set exists.

Example

# How AI works — split first
rows = ["spam", "ham", "spam", "ham", "spam"]
train, test = rows[:4], rows[4:]
print("train", train)
print("test ", test)
print("never tune on test")

How AI Works — output: train has 4 labels, test has ['spam']. You score only on test.

Short notes

  • Defdata → train/rules → test → deploy → monitor.
  • RuleAlways hold out a test set.
  • TrapTrain accuracy only.

Questions

1

Explain How AI Works as if you are teaching a junior — definition, then one example.

2

What does the example print, and what does that prove?

3

What mistake do freshers make with How AI Works?

Previous← Domains of AINextFrames in AI →
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