There are many, many different variations, but they more or less all work on the same basic premise.

1. Begin with an initially random model.

2. Test the model. Give it a problem and ask for its response.

3. Modify. If the system didn't behave as intended, change something.

4. Repeat steps 2 and 3 until you run out of training data.

5. Pray that the model works.

The most obvious differences between AIs will be in the structure of the model (how big is it, how connected, how many layers, what kind of internal memory etc) but the real fun stuff is in how we do the modifying.

We can show that, for some problems, just tweaking the system randomly is enough to get okay solutions. But it's very far from ideal. Better is to be able to nudge the system "towards" the expected behavior. We've put a lot of focus into how to design these systems so that our modifications are more fruitful.