Know it All Better What is AI – Part 2: The Family Tree (AI vs ML vs DL vs Generative AI)

The Family Tree (AI vs ML vs DL vs Generative AI)

People throw around “AI” like it means everything from Siri to Skynet. Let’s draw the family tree so we stop confusing the cousins.

Artificial Intelligence (the big umbrella) The dream of making machines that can do things that would require intelligence if a human did them. This term was born in 1956. Most of what we called “Artificial Intelligence” from the 1960s to 2010s was rules written by humans — expert systems, chess programs, etc. Powerful but brittle.

Machine Learning (the first big branch that actually learns) Instead of hand-writing every rule, you feed the machine data and let it figure out the rules itself. Born in the 1990s, became practical in the 2010s when we finally had enough data + compute.

Three main flavors inside ML:

Supervised learning → you give labeled data (“this is a cat, this is not a cat”)
Unsupervised learning → you just dump data and say “find patterns”
Reinforcement learning → trial and error with rewards (how AlphaGo learned)

Deep Learning (the crazy successful grandchild) A type of machine learning that uses neural networks with many layers (“deep”). Exploded around 2012 when a deep learning model crushed the ImageNet competition. Suddenly computers could recognize images, translate languages, and drive cars better than humans in specific tasks.

Generative AI (the branch that creates stuff) The part that took over the internet in 2023–2025. Takes all the above and instead of just classifying or predicting, it generates new things — text (ChatGPT, Claude, Grok), images (Midjourney, DALL-E, Flux), music, video, code.

Think of it like this:

AI = electricity
Machine Learning = appliances that use electricity
Deep Learning = the super-efficient modern appliances
Generative AI = the appliances that now cook the meal for you instead of you cooking on an electric stove

Or my favorite analogy I tell non-tech friends: Imagine teaching a child to recognize animals.

Traditional programming = writing a 10,000-page rulebook
Machine Learning = showing the child 10,000 pictures and saying “which ones are cats?”
Deep Learning = the child suddenly gets really, really good after seeing a million pictures
Generative AI = now the child can draw new cats that never existed

That’s it. That’s the entire family tree.

Next post 3 we’ll open the hood and look at how these things actually work — neurons, training, tokens — explained like I’m having coffee with my very confused but curious uncle.

Know It All Better What is AI Artificial Intelligence 5 Part series link below