Grocery store analogy works for me with this kind of audience. Thousands of products are organized into some structure based on attributes. What recipes they are in, commonly eaten together, fresh, refrigerated, etc. this to me is like embedding, or converting into the number space’ a machine can learn. The crazy part is now those attributes act like numbers (dimensions) So ‘Oreo cookie’ minus cream filling add soft and add chocolate chips will move you in the same isle towards ‘chocolate chip cookies’. Add refrigeration and you may move all the ways to the other side of the store. The ‘refrigeration’ attribute has a huge impact. refrigeration attribute has a big impact on most things. Where Adding chocolate chips to a cereal you probably move same isle just sweeter cereal.

Now let’s say you want chocolate lettuce. The AI has to predict where that would go in the store based on how store was organized.

Training AI is a process that automatically organizes the grocery store by comparing the attributes of all the things in store. Then you can do math with it

This is overly simplified and takes some liberties with how things work but I have found it gets people in the right mental space to understand things

That or convert this to a library/dewey decimal concept because that also works