A “deepity” (a term first used by Daniel Dennett) is an ambiguous statement with two meanings:
1. one is profound (but probably false or nonsense)
2. the other meaning is trivial or obvious (but true)
These paired meanings can trick our brains into thinking that the idea is both profound and true: we experience the profoundness of one meaning while experiencing the truth of the other meaning. These are phrases that exploit the odd nature of our minds.
Some examples are (arguably):
• “Love is just a word.”
• “Everything happens for a reason.”
• “There is no ‘I’ in team.”
• “Beauty is only skin deep.”
I’d like to propose a new term: “deepiful.”
A deepiful is a statement that seems trivial or dumb but which has genuinely profound consequences!
A deepiful is the opposite of a deepity (since a deepity is dumb but sounds profound, whereas a deepiful is profound but sounds dumb!).
Here are some examples.
Deepiful 1: if each of a system’s states is equally likely, and we group them into categories (e.g., “mixed up” states vs. “organized” states), then the things that actually happen will (obviously) tend to be from categories containing more states.
This is totally trivial in a sense, yet it’s the basis of entropy!
Deepiful 2: if some organisms are more successful at passing down their genetic material than others, then organisms similar to them will grow in relative numbers.
This is also trivial, yet it’s the basis of natural selection!
Deepiful 3: if you define new words for things and use the shortest words for the most commonly said things, you’ll be able to communicate using fewer letters. Keep going, and you’ll have the shortest way of saying things.
Another trivially true statement, yet it’s the core of information theory!
Deepiful 4: if you have a bunch of dots on a piece of paper, you can draw with a pencil from left to right to connect them together in an endless variety of ways!
This is obvious, and a young child can easily see that this is true, and yet it’s the basis of the concept of overfitting in machine learning!
This piece was first written on September 18, 2020, and first appeared on this site on April 22, 2022.
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