1. Text
- Articles, essays, stories
- Emails, product descriptions
- Code writing and debugging
2. Images
- Artwork, logos, illustrations
- Realistic faces, objects, and scenes
- Product visualizations and mockups
3. Videos
- Short animations and explainer videos
- AI-generated avatars and presenters
- Deepfake videos (both ethical and unethical uses)
4. Audio
- Voiceovers and synthetic speech
- Music composition
- Sound effects and background scores
5. Code
- Writing scripts in Python, JavaScript, etc.
- Debugging and optimizing code
- Explaining code functionality
6. Synthetic Data
- Generating fake but realistic datasets for training other AI models
- Simulating environments for testing autonomous systems
Key Differences Between Generative AI And Predictive AI
| Aspect | Generative AI | Predictive AI |
| Purpose | Creates new content such as text, images, videos, or music | Analyzes existing data to forecast future trends or outcomes |
| Core Function | Generation of data or creative outputs | Prediction or classification based on patterns in historical data |
| Key Techniques | GANs, VAEs, Transformers, Diffusion Models | Regression, Classification, Time Series Forecasting, Clustering |
| Output Type | New and original content (text, images, audio, code) | Predicted values, labels, or decisions |
| Examples | ChatGPT generating articles, DALL·E creating images, and music composition tools | Stock price prediction, weather forecasting, and recommendation systems |
| Industries Used In | Media, advertising, design, education, and entertainment | Finance, healthcare, retail, logistics, insurance |
| Creativity Involved | High – simulates human-like creativity | Low – focuses on logical predictions from data |
| Training Data Required | Needs large datasets for learning content structure and style | Needs historical data for accurate forecasting |
| Real-Time Use | Often used in real-time content creation (e.g., chatbots, design tools) | Used for real-time decision-making (e.g., fraud detection, demand prediction) |
| Popular Tools/Models | GPT (OpenAI), DALL·E, Midjourney, Stable Diffusion | Scikit-learn, XGBoost, TensorFlow for ML models |