“Explain like I’m five” or ELI5, is an idea that started on Twitter (#ELI5) and spawned the Explain Like I’m Five subreddit. ELI5 takes a complex idea or concept and goes through the mental exercise of simplifying it in a way that a young child can understand.
In this BENlabs take on ELI5, we’ll hit on some common — and not so common — terms relating to marketing, influence, and AI and do our best to distill them into simple terms without losing too much of the nuance.
Define Machine Learning in ELI5
Inspired by this answer from Lux_Obscura on Reddit.
Machine learning is getting computers to “think” similar to the way human beings do.
When you were born, you didn’t know what an apple or an orange was. Now, you can look at a fruit bowl and immediately pick out the apples or oranges. Even if there were different types of apples and different types of oranges in the bowl, you’d be able to sort them. You know enough about apples and oranges now to make the distinction, because you’ve seen a lot of them.
Machine learning is like that. At first, we might have to tell the computer “this is an orange. It’s got bumpy skin, it’s orange in color, and it’s this big” “This is an apple. It’s got a stem, it has smooth skin, it’s green and red in color and it’s this big.” The computer can now start to sort out which fruits match what it knows about apples and which match what it knows about oranges.
But then, as the computer sorts more and more apples and oranges, it sees different types. Some oranges are smaller and some are larger. Some apples are more green and some are more red. If it gets confused, it moves on to the next apple or orange in the bowl until it sorts more apples and oranges and learns more about them. It learns that some apples are only green and some are only red. Some have stems and some don’t. Some oranges are big and some are small.
The more apples and oranges the computer sorts, the better it gets at knowing what makes an apple and what makes an orange until it can pick them out as easily as you can.
