AI · Theory

Generative AI

Theory

183/215

Generative AI

Generative AI creates new content: text, images, audio, code. It learns a distribution, then samples. LLMs predict the next token. Image models denoise or decode latents. Useful copilots. Risks: hallucination, copyright, deepfakes, leaking training data.

Not the same as a classifier that only outputs spam/not-spam. Generative = produce new samples. Discriminative = label existing ones. Say that pair.

Generative AI — output — three lines. Generative creates samples; still can hallucinate.

Exam tip

Generative vs discriminative + one risk.

Example

# Generative vs discriminative
print("classifier : spam / not spam")
print("generative : write a new email draft")
print("fluent text can still be false")

Generative AI — output: three lines. Generative creates samples; still can hallucinate.

Short notes

Creates new samples.
LLM = next token.
Trapfluent ≠ true.

AI · Theory

Generative AI

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Theory

183/215

Generative AI

Not the same as a classifier that only outputs spam/not-spam. Generative = produce new samples. Discriminative = label existing ones. Say that pair.

Generative AI — output — three lines. Generative creates samples; still can hallucinate.

Exam tip

Generative vs discriminative + one risk.

Example

# Generative vs discriminative
print("classifier : spam / not spam")
print("generative : write a new email draft")
print("fluent text can still be false")

Generative AI — output: three lines. Generative creates samples; still can hallucinate.

Short notes

Creates new samples.
LLM = next token.
Trapfluent ≠ true.

Generative AI

Example

Short notes

Questions

Generative AI

Example

Short notes

Questions