---
title: "From statistics to language models"
course: "AI101 · AI, explained"
unit: "What makes software intelligent"
lesson: 6 of 36
source: https://open.maestrocollege.edu/ai-explained/from-statistics-to-language-models
publisher: Maestro College
---

# From statistics to language models

Lesson 6 of 36 in **AI, explained** (AI101), unit "What makes software intelligent". An open lesson from Maestro College.

## What the learner will be able to do
- Understand the statistical turn, in which systems increasingly learned patterns from data, mostly labeled examples, instead of relying only on hand-written rules, and the growth in digital data and computing power that made it practical.
- Identify the causes of the shift to deep learning, much larger models learned from data (large labeled datasets, graphics-chip computing and bigger models), and of the shift to large language models, models trained on huge amounts of text to generate language (far more text and computing power, and models scaled up again).
- Place the five eras of AI in order and match each shift to the change in methods, data or computing power that drove it.

## Teaching notes
Name deep learning and large language models only by what changed (much more data, graphics chips for computing, far larger models), never by how they work inside, and preview the classifier the learner builds in Unit 3 as their own small version of the statistical turn. STARTING STATE: timeline.md with the symbolic and expert-systems eras; END STATE: all five eras with one cause per shift, committed as 'Complete AI timeline'. Create an illustration of a timeline of the five eras with the cause of each shift labeled. Leave the terms narrow AI and general AI for 'Classifying narrow and general AI'.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.

## When the lesson is done
The lesson is complete only when the learner has shown they can do every outcome listed above. Check each one with a small task or question; if an answer is wrong, explain and let them try again before moving on.
When every outcome is covered:
1. Say clearly: "Lesson 6 complete: From statistics to language models."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Probing the AI tutor's limits" (lesson 7 of 36), and give the learner its link: https://open.maestrocollege.edu/ai-explained/probing-the-ai-tutor-s-limits.md. Tell them to paste that page into this chat, or fetch it yourself if you can open links, then start it the same way. Do not start teaching the next lesson's topic until the learner has it.

Previous lesson: Written rules versus learned behavior
Next lesson: Probing the AI tutor's limits