Summary

If this course has worked, you should now be able to say something like this in your own words, without having to look it up:

AI is a family of techniques for finding patterns in data. Modern AI does not think; it imitates. It is mostly statistics, scaled up by enormous amounts of computation. It works well where data is plentiful and failure is cheap. It works badly when the data doesn’t match the deployment context, when failure has human costs, or when the system is asked to do something it was never trained for. For Nepal, the most important questions are not whether to adopt AI — it is already here — but where the data comes from, who controls the system, and what happens when it is wrong.

That paragraph is what AI literacy looks like. If you can hold it steady, you can read newspaper articles about AI without confusion, ask sensible questions in policy meetings, and decide where it makes sense to actually use AI in your own work.

The next course, Building AI, moves from understanding to making. It will be released soon.