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Generative AI
A class of AI that can create new content (text, code, images, video, music) rather than just predicting outcomes.
Powered by foundation models like GPT, Stable Diffusion, etc.
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Builds on Deep Learning + NLP + multimodal modeling.
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Represents the shift from discriminative models (predicting) to generative models (creating).
Pros
- Enables creativity and automation at scale.
- Reduces time to draft, design, or brainstorm.
Cons
- Can hallucinate false information.
- High computational cost and environmental footprint.
- Raises copyright, ethics, and bias concerns.
Use Cases
- Text: AI writing assistants, code copilots.
- Image/video: marketing content generation, design prototyping.
- Data: generating synthetic data for ML training.
- Education: personalized learning materials and quizzes.
Key differences
| Traditional ML | Generative AI |
|---|---|
| Predicts outcome from features | Produces new content |
| Needs task-specific data | Pretrained on massive corpora |
| Optimized for accuracy | Optimized for creativity, coherence |
| Example: Predict churn | Example: Generate flying pigs/elephant |