What Is AI in the End? First, Understand It as a "Broad Word"
AI (Artificial Intelligence) is, in a word, "an umbrella term for technologies that make computers do humans' intellectual tasks." This is important: AI is not the name of one specific technology but an "umbrella"-like word that bundles various methods and ways of thinking.
For example, things AI is good at include the following.
- Finding cats in images (image recognition)
- Classifying spam mail (classification)
- Forecasting demand to optimize inventory (prediction)
- Summarizing text, answering in chat (natural language processing)
And the representative approach that realizes that AI is machine learning; within machine learning there is deep learning; and as a development of deep learning, generative AI is drawing attention—this is the relationship.
First, a Map: The Relationship of AI, Machine Learning, Deep Learning, and Generative AI
It is easy to get confused, so let's organize it roughly as a hierarchy.
AI (Artificial Intelligence): an umbrella term for technologies that realize intellectual tasks
└ Machine Learning (ML): a method that learns rules from data to predict or classify
└ Deep Learning (DL): a kind of machine learning that automatically learns features with (multi-layer) neural nets
└ Generative AI: AI that generates "new content" such as text, images, and audio (mainly based on DL)
The point is that generative AI is "a part of AI" and is not a concept opposed to machine learning or deep learning.
What Is Machine Learning (ML)? The Technology of "Hitting the Mark" From Data
Machine learning is technology that, instead of a person writing all the rules, learns patterns from past data to estimate and judge. The image is like "showing many example problems to make it strong on the test."
Representative tasks
- Classification: spam/not-spam, normal/abnormal, etc.
- Regression: predicting "numbers" such as sales or temperature
- Clustering: grouping similar customers (unsupervised learning)
Common examples (the handy, useful ones)
- E-commerce recommendations (estimating preferences from purchase history)
- Credit card fraud detection (detecting behavior different from usual)
- Predictive signs of factory equipment failure (predicting signs of anomalies from sensor data)
