The AI family tree: ML, deep learning, generative AI
See how AI, machine learning, deep learning and generative AI nest inside each other
A taste of a lesson
So is my bank's fraud alert the same kind of AI as ChatGPT?
Same family, different branch. Both use machine learning, so they sit under the same big limb of the tree. But your bank's fraud alert is mostly a predictor: it looks at a transaction and outputs a judgment, likely fraud or not. It was trained on labelled examples of past transactions. A chatbot is generative: it produces new text. The fraud system may not even use deep learning; simpler methods often work well there. Try placing your phone's face recognition on the tree. Where would it go?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Draw the AI family tree from the broad field down to large language models
- Explain supervised, unsupervised and reinforcement learning with an example each
- Tell apart models that predict or classify from models that generate
- Place a new AI product on the tree and describe what kind of system it likely is
Lesson plan
- 1 The trunk: what counts as AI Understand AI as a broad field that includes more than learning systems. Start
- 2 Machine learning and its three ways of learning Learn how systems learn from data, with or without labels or rewards. Start
- 3 Deep learning and neural networks See what makes deep learning different and why it took over certain tasks. Start
- 4 Predicting versus generating Distinguish models that make a judgment from models that produce new content. Start
- 5 The newest branches: LLMs and image generators Place large language models and image generators on the tree and see how they differ. Start
Try asking
About this tutor
For beginners who keep meeting overlapping terms and want one clear picture. You build a family tree: AI as the broad field, machine learning as one branch, deep learning as a branch of that, and generative AI and large language models as recent offshoots. Along the way you meet supervised and unsupervised learning, neural networks, and the difference between models that classify or predict and models that generate. Each branch comes with a concrete example from daily life. You finish able to place any AI product you hear about on the tree and explain what kind of system it probably is.
Reviews
4.7
3 ratingsSample
- Fatima Z.Sample
Having one picture to come back to each lesson really helped. I can now explain to my team why our forecasting tool and a chatbot are not the same thing.
- Ngozi E.Sample
The 'predict versus generate' lesson was the click moment. Simple drawings, no maths, exactly right for me.
- Liam R.Sample
Clear and well paced. Reinforcement learning was a bit quick, but the game example helped.
About the teacher
I help complete beginners and older learners feel at home with AI, one plain word at a time
9 tutors 394 lessons taught Sample
I teach people who were told AI is not for them. Most of my learners have never typed a question into a chatbot, and some are a little afraid of it. I start from what they already know: autocorrect, spam filters, the map on their phone. Then we build up the words and habits slowly, with lots of trying things...
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