How we got here: a short history of AI
Follow AI from rule based programs to today's chatbots and see why progress came in waves
A taste of a lesson
If neural networks are old, why did they only start working well recently?
Great question, because it shows that ideas alone are not enough. The basic idea existed by the late 1950s, and the key training method was popularised in the 1980s. What was missing was fuel: huge collections of digital data and chips fast enough to crunch them. Graphics chips built for video games turned out to be ideal. Around 2012, all three came together and results jumped. Quick check: can you name the three ingredients I just mentioned?
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
- Describe the main chapters of AI history in your own words
- Explain the difference between rule based AI and machine learning
- Explain why data and computing power mattered as much as new ideas
- Use historical patterns to judge today's predictions about AI more calmly
Lesson plan
- 1 The first dream: thinking machines Understand the early ambitions of AI and why they were so optimistic. Start
- 2 Rules and experts See how rule based and expert systems worked and why they hit limits. Start
- 3 Winters and quiet progress Learn why interest in AI collapsed twice and what continued in the background. Start
- 4 Learning from data Understand the shift from writing rules to learning patterns from examples. Start
- 5 Transformers and chat assistants See how the transformer and scale led to today's language models. Start
- 6 What history teaches about forecasts Apply patterns from the past to judge today's claims about the future of AI. Start
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About this tutor
For beginners who want context: why AI suddenly seems everywhere when people have talked about it for decades. You walk through the main chapters: early hopes and rule based systems, expert systems, the quiet periods known as AI winters, the rise of machine learning, the deep learning breakthroughs, the transformer, and the arrival of chat assistants. Each lesson connects a historical idea to something you can see today. You learn why predictions about AI have so often been wrong in both directions, which is the most useful lesson for reading today's forecasts. No dates to memorise, just the shape of the story.
Reviews
4.5
2 ratingsSample
- Oliver J.Sample
I loved the ELIZA story. Realising people over-trusted a tiny program in the 1960s made me rethink how I react to chatbots now.
- Mei C.Sample
Good overview, not too many dates. The forecasts lesson was the most useful. Could have gone deeper on the expert systems era.
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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