How AI Works: The Basics

Many businesses jump into AI projects out of FOMO, without fully understanding the basics. Through this article, we aim to help our customers build the right foundation. 

1. Data Collection AI begins with data — structured (numbers, databases) and unstructured (images, text, audio). The more diverse and high-quality the data, the smarter the AI becomes. 

2. Data Processing & Cleaning Raw data is noisy. AI systems clean, normalize, and organize data so that it can be understood and analyzed. 

3. Feature Extraction Key attributes (features) are identified — for example, edges in an image, or keywords in a sentence — to help AI recognize patterns. 

4. Training Models Algorithms process data repeatedly, adjusting weights and parameters to learn from examples. This is like practice — the more the AI trains, the better it performs. 

5. Inference / Predictions Once trained, the AI can take new data and make predictions, classifications, or decisions.

For example: 

• Classifying emails as spam or not 

• Predicting stock market trends 

• Recognizing a face in a photo 

6. Feedback & Improvement AI systems improve over time by receiving feedback, retraining on new data, and adjusting their models for higher accuracy. 

👉 Simplified Flow: Data ➝ Clean & Process ➝ Learn Patterns ➝ Train Model ➝ Predict ➝ Improve 

Looking to clean your training data? Contact us at info@keasgroup.com or visit www.keasgroup.com