Many people hear “AI” and instantly think of ChatGPT.
But the truth is: AI existed long before ChatGPT, and most AI we used for years wasn’t conversational at all.
- AI: The Big Umbrella
Let’s start from the top.
Artificial Intelligence is the broadest term.
If a system tries to mimic human intelligence — learning, decision-making, perception — it falls under AI.
AI includes many subfields:
• Machine Learning
• Deep Learning
• Computer Vision
• Natural Language Processing
• Robotics
• Expert systems
- ML: The Engine Behind Most “Old AI”
Before ChatGPT, the AI powering our world was mostly Machine Learning.
ML is simply this:
Models learn patterns from data and make predictions — without being explicitly programmed.
For almost 20 years, ML quietly powered:
• Fraud detection
• Credit scoring
• Traffic prediction in maps
• Recommendation engines
• Face recognition
• Spam filters
• Search ranking
And here’s the interesting part:
These models were not “smart” in a conversational way.
They didn’t talk, write, reason, or code.
They were mathematical prediction systems:
• Logistic regression
• Decision trees
• Random forest
• Gradient boosting (XGBoost, LightGBM)
• Early neural networks
This was the AI that shaped the modern internet long before LLMs arrived.
- LLMs: The New Evolution of AI This changed everything.
Suddenly, AI could:
• Understand text
• Generate text
• Reason
• Summarize
• Translate
• Write code
• Assist with workflows
This is where LLMs (Large Language Models) came in.
LLMs like ChatGPT, Claude, Gemini, and Llama aren’t just tools, they’re a new category of AI entirely.
They work on language, not just numbers.
They’re trained on massive datasets.
And they’re capable of general-purpose intelligence that older ML models were nowhere near.
In short:
ML predicted.
LLMs communicated.
The Whole Relationship:
AI is the field.
ML is a major branch inside AI.
LLMs are one specific type of ML model focused on language
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