🔮 1. Predictive AI
- What it does:
Predictive AI is a type of artificial intelligence that uses historical data, statistical models, and machine learning to predict future outcomes or behaviors.
- 🔑 Key Points:
- Data-Driven: It analyzes past patterns and trends to forecast what’s likely to happen next.
- Probabilistic: Predictions are based on probability, not certainty.
- Core Techniques: Regression analysis, time-series forecasting, classification, and neural networks.
- 📌 Examples in Real Life:
- Banking: Predicting fraudulent transactions.
- Healthcare: Forecasting disease risks or patient readmissions.
- Retail/E-commerce: Predicting what products customers might buy next.
- Weather apps: Forecasting temperature, storms, or rainfall.
- Manufacturing: Predictive maintenance (anticipating when a machine will fail).
🎨 2. Generative AI
- What it does:
Generative AI is a type of artificial intelligence that can create new content—such as text, images, music, video, or code—based on the data it has been trained on.
- 🔑 Key Points:
- Instead of just analyzing or classifying data (like traditional AI), Generative AI produces original outputs.
- It uses machine learning models (like GPT for text, DALL·E or Stable Diffusion for images) that learn patterns, styles, and structures from massive datasets.
- Outputs can look human-made, even though they’re generated by AI.
- 📌 Examples in Real Life:
- Text: ChatGPT writing essays, emails, or code.
- Images: AI art generators creating digital artwork.
- Music: AI composing songs in different styles.
- Video: Deepfake technology or AI-generated animations.
- Business: Generating product descriptions, ads, or marketing content.
👁️ 3. Computer Vision
- What it does:
Computer Vision AI is a branch of artificial intelligence that enables computers to see, interpret, and understand visual information from the world — similar to how humans use their eyes and brain to process images.
🔑 Key Functions of Computer Vision AI:
- Image Classification: Identifying what’s in an image (e.g., cat vs. dog).
- Object Detection: Locating and labeling multiple objects in a picture (e.g., detecting cars, pedestrians in traffic).
- Image Segmentation: Dividing an image into regions for detailed analysis (e.g., separating organs in a medical scan).
- Facial Recognition: Identifying or verifying people from facial features.
- Activity Recognition: Understanding actions in videos (e.g., recognizing someone waving).
📌 Examples in Real Life:
- Healthcare: Detecting tumors in X-rays or MRIs.
- Self-driving cars: Recognizing road signs, lanes, and obstacles.
- Smartphones: Face unlock features and AR filters.
- Retail: Automated checkout systems (e.g., Amazon Go stores).
🗣️ 4. Natural Language Processing (NLP)
What it does:
Natural Language Processing (NLP) is a field of artificial intelligence (AI) that focuses on enabling computers to understand, interpret, and respond to human language (spoken or written).
🔑 Key Points:
- Human–Computer Communication: It bridges the gap between how people talk/write and how machines process information.
- Core Tasks of NLP:
- Speech recognition (turning speech into text, e.g., Siri, Alexa).
- Text analysis (sentiment analysis, spam detection).
- Language translation (Google Translate).
- Chatbots/virtual assistants (understanding questions and giving relevant answers).
- Autocomplete & predictive text (suggesting the next word as you type).
