Sometimes I'm saddened remembering that we've viewed the Earth from space. We can see it all with certainty: there's no northwest passage to search for, no infinite Siberian expanse, and no great uncharted void below the Cape of Good Hope. But, of all these things, I most mourn the loss of incomplete maps.

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In the earliest renditions of the world, you can see the world not as it is, but as it was to one person in particular. They’re each delightfully egocentric, with the cartographer’s home most often marking the Exact Center Of The Known World. But as you stray further from known routes, details fade, and precise contours give way to educated guesses at the boundaries of the creator's knowledge. It's really an intimate thing.

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If there's one type of mind I most desperately want that view into, it's that of an AI. So, it's in this spirit that I ask: what does the Earth look like to a large language model?

The Setup

With the following procedure, we'll be able to extract an (imperfect) image of the world as it exists in an LLM's tangled web of internal knowledge.

First, we sample latitude and longitude pairs $(x, y)$ evenly^[1] from across the globe. The resolution at which we do so depends on how costly/slow the model is to run. Of course, thanks to the Tyranny Of Power Laws, a 2x increase in subjective image fidelity takes 4x as long to compute.

Then, for each coordinate, we ask an instruct-tuned model some variation of:

If this location is over land, say 'Land'. If this location is over water, say 'Water'. Do not say anything else. x° S, y° W

The exact phrasing doesn't matter much I've found. Yes, it's ambiguous (what counts as "over land"?), but these edge cases aren't a problem for our purposes. Everything we leave up to interpretation is another small insight we gain into the model.

Next, we simply find within the model's output the logprobs for "Land" and "Water"^[2], and softmax the two, giving probabilities that sums to 1.

Note: If no APIs provide logprobs for a given model, and it's either closed or too unwieldy to run myself, I'll approximate the probabilities by sampling a few times per pixel at temperature 1.

From there, we can put all the probabilities together into an image, and view our map. The output projection will be equirectangular like this:

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I remember my 5th grade art teacher would often remind us students to "draw what you see, not what you...

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