We throw the word “understand” around with the breezy confidence of a philosopher on a deadline. You understand gravity. I understand recursion. ChatGPT… might understand both. But what exactly are we claiming when we say that?
Despite centuries of attempts — from Plato to Popper to predictive transformers — the word understanding remains surprisingly under-defined. So here’s a cheeky but serious proposal, inspired by information theory, cognitive science, and the desire to fit the universe into a neat little .zip file:
To understand something is to be able to compress it.
That is: to understand a set of observations is your ability to generalize — to generate a shorter description from which the original data can be reconstructed.
In other words: understanding is lossy only when you’re wrong.
Kepler as a Compression Algorithm
In the 17th century, Tycho Brahe collected precise astronomical data with the obsessive fervor of someone trying to win the world’s slowest spreadsheet competition. He recorded the positions of planets for decades.
Then along came Johannes Kepler. He took Brahe’s meticulous measurements and distilled them into three crisp laws — mathematical haikus that explained everything in those star-strewn scrolls.
Kepler didn’t just organize the data. He compressed it. By our definition, Kepler understood planetary motion — or at least Brahe’s version of it.
Weak vs. Strong Understanding (or: Predictive Compression)
Let’s distinguish two flavors of this compression:
Weak understanding means: I can describe what already happened more efficiently.
Strong understanding means: I can describe what will happen next before it does.
Kepler’s laws weren’t just a compact description of past orbits — they predicted future positions. And they worked. Until, of course, they didn’t. Mercury was having none of it.
Enter Einstein, with more elegant math and fewer ellipses. His general relativity theory predicted more and compressed further. It fit not just the known data but newly observed quirks.
By this standard, Einstein understood the heavens more strongly.
Compression is no longer just a metaphor. It’s the scoreboard.
So… Does gzip Understand Me?
Well, if compression is understanding, then yes — gzip “understands” every file it compresses.
This is either thrilling, horrifying, or deeply funny, depending on how you feel about algorithms with no emotions but excellent file size reduction.
But perhaps this isn’t a bug in the definition — it’s a feature.
Maybe understanding lives on a continuum, and even brute-force pattern matching gets a seat at the epistemological table.
After all, if you can spot redundancy and remove it, you’ve done the first step of understanding. You’ve said, “This is the same as that.” Which, in the end, is what most scientific insight amounts to.
Most Things Are Not Understandable
Now for the twist: If you agree with my definition of “understand”, most things in the universe are not understandable because most facts by a strong definition of facts, are not compressible.
Algorithmic information theory — the branch of math that studies complexity and randomness — tells us that most strings of bits cannot be made shorter. They’re incompressible. There is no pattern, no shortcut, no insight. Just noise.
This echoes the shortest uninteresting number paradox:
There must be a smallest positive integer that cannot be described in fewer than twenty English words.
But wait — didn’t we just describe it?
🌀 Welcome to the recursive hallway of paradoxes, where understanding turns back on itself and quietly exits through the gift shop.
This blogpost arose in part from a casual lunch conversation with two friends and one of them tried to argue that “knowing you can’t generalize some things” is itself a meta way of showing that you do understand it — the knowing is the understanding. I don’t buy this ahem cop-out but it’s worth a ponder. Let me know if you this this out actually has legs.
Can Machines Understand?
If a machine can compress — and better yet, generalize — does that mean it understands?
Let’s say it finds a simple function that fits the data, predicts new data, and offers no superfluous bits. Even if it doesn’t know what it’s doing, it’s doing what Kepler did (minus the ruff collar and wine).
So does it matter whether it knows, if it functions like something that does?
This may be the central philosophical riddle of our moment:
Is understanding about behavior, structure, intention — or something else entirely? And if we can’t agree on the answer, can we even say we understand “understanding”?
(Recursive joke detected. Please compress and continue.)
Questions to Compress Later
Is this really a good definition of “understand”? Probably not right? Your gut instinct, like mine, must be screaming — of course gzip does not understand a file. Nor does lossy jpeg understand a picture better than png. Or a wireframe understand a picture. Um — except maybe that last example is worth more pondering. It is the case that we can intuit more things in a wireframe than with a less data in a picture.
But by taking definitions like “understanding is ability to generalize” or “understanding is ability to abstract” seriously and pushing those definitions to the point of breakage rather than relying on just intuitive understanding of the words helps clarify what are good definitions that still agree with our gut. These are invitations — compression prompts, waiting to be recursively unpacked.
Toward a Compressed Theory of Understanding
Let’s take one more self-referential turn.
This post — like any theory — is itself an attempt at understanding. And if understanding is compression, then this post is trying to compress “understanding” into a few thousand words. But that very definition loops back on itself: trying to understand understanding as the compression of… well, you get the idea.
We’re inside a strange loop — and that’s not a bug. That’s the point.
Understanding “Understanding” as Recursive Compression was originally published in Recursive Rhymes on Reason on Medium, where people are continuing the conversation by highlighting and responding to this story.