Core AI Concepts
Before writing AI code, you need to understand the fundamental concepts that power artificial intelligence.
Types of AI
- Narrow AI (ANI) — AI designed for specific tasks (e.g., image recognition, chatbots). This is what exists today.
- General AI (AGI) — AI with human-level intelligence across all domains. Still theoretical.
- Super AI (ASI) — AI that surpasses human intelligence. Speculative future concept.
Types of Machine Learning
| Type | Description | Example Algorithms | Use Case |
|---|---|---|---|
| Supervised Learning | Learn from labeled data (input-output pairs) | Linear Regression, Decision Trees, SVM, Neural Networks | Classification, Regression |
| Unsupervised Learning | Find patterns in unlabeled data | K-Means, PCA, Hierarchical Clustering | Customer Segmentation, Anomaly Detection |
| Reinforcement Learning | Learn from rewards and penalties through interaction | Q-Learning, Deep Q-Network, Policy Gradient | Game Playing, Robotics, Autonomous Systems |
AI Development Lifecycle
- Problem Definition — Define what you want to predict or classify.
- Data Collection — Gather relevant data.
- Data Preparation — Clean, normalize, and split data.
- Model Selection — Choose the right algorithm.
- Training — Train the model on training data.
- Evaluation — Test the model on unseen data.
- Deployment — Deploy the model to production.
- Monitoring — Track model performance over time.
Key AI Terms
| Term | Definition |
|---|---|
| Dataset | Collection of data used to train or test models |
| Feature | Individual measurable property of data |
| Label | The target value we want to predict |
| Model | Mathematical representation learned from data |
| Training | Process of fitting model to data |
| Overfitting | Model memorizes training data, fails on new data |
| Underfitting | Model too simple to capture patterns |
Summary
You now understand the core AI concepts: types of AI, machine learning categories, and the development lifecycle. Next, we'll dive into machine learning with Python.