AI Basics

Artificial Intelligence (AI) mimics human thinking using math and code. This article introduces the core concepts of AI, from neural networks to real-world applications, demystifying this transformative technology.

Neural Networks

Neural networks model the brain:

• Structure: Layers of nodes (neurons) process inputs.
• Math: \( y = wx + b \) (weight × input + bias) computes outputs.
• Activation: Functions like sigmoid adjust results.

They learn patterns from data.

Learning Algorithms

AI learns through optimization:

• Gradient Descent: Minimizes error by adjusting weights.
• Loss Function: Measures prediction accuracy.
• Backpropagation: Updates network based on errors.

Math drives AI’s adaptability.

Data Processing

Data fuels AI:

• Preprocessing: Normalizes inputs (e.g., scaling to 0-1).
• Features: Extracts key variables for learning.
• Training: Uses datasets to refine models.

Quality data ensures accurate AI.

Applications

AI impacts daily life:

• Chatbots: Natural language processing powers assistants.
• Image Recognition: Identifies objects in photos.
• Healthcare: Predicts diseases from scans.

AI’s potential is vast and growing.