What exactly is AI? First, understand it as a "broad term"
AI (Artificial Intelligence / 人工知能) is, in short, a broad umbrella term for technologies that enable computers to perform human intellectual tasks. This is important: AI is not a single technology name, but a umbrella that encompasses various methods and ideas.
For example, AI excels at tasks such as the following.
- Find cats in images (image recognition)
- Classify spam
- Forecast demand to optimize inventory (prediction)
- Summarize text, answer in chat (natural language processing)
And the representative approaches to realize that AI are machine learning, within which deep learning exists, and as the advanced form of deep learning, generative AI is drawing attention... that's the relationship.
First, a map: the relationships among AI, ML, DL, and Generative AI
Because it's easy to get confused, let's organize it in a rough hierarchy.
AI (Artificial Intelligence): A broad umbrella of technologies that realize intellectual tasks
└ Machine Learning (ML): A method that learns rules from data to perform predictions and classifications
└ Deep Learning (DL): A kind of ML that automatically learns features via multi-layer neural networks
└ Generative AI: AI that generates new content such as text, images, or audio (primarily DL-based)
Key point: Generative AI is a part of AI, and it is not a concept that conflicts with machine learning or deep learning.
What is Machine Learning (ML): A technique to "predict" from data
Machine Learning is a technique that, instead of a human writing all the rules, learns patterns from past data to estimate and make decisions. The idea is like "showing many examples to become strong at the test."
Representative tasks
- Classification: spam/ham, normal/anomalous, etc.
- Regression: predicting numeric values such as sales or temperature
- Clustering: grouping similar customers (unsupervised learning)
Common examples (useful in daily life)
- E-commerce recommendations (inferring preferences from purchase history)
- Credit card fraud detection (detecting unusual behavior)
- Predicting signs of equipment failure in factories (predicting anomalies from sensor data)
