LLMs Turn Every Question Into an Answer

The world has changed considerably since our last ”think week” five months ago—and so has Every. We’ve added new business units, launched new products, and brought on new teammates. So we've been taking this week to come up with new ideas and products that can help us improve how we do our work and, more importantly, your experience as a member of our community. In the meantime, we’re re-upping four pieces by Dan Shipper that cover basic, powerful questions about AI. (Dan hasn’t been publishing at his regular cadence because he’s working on a longer piece. Look out for that in Q2.) Thus far we’ve re-published his jargon-free explainer of how language models work and his piece about how language models function as compressors—or summarizers—of text. Today we’re sharing how language models function as the opposite—as text expanders.—Kate Lee

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You can’t get energy for free. It can be neither created nor destroyed, just moved around. That's more or less what computers were able to do with text on their own for a long time. Barring a disk failure, text was always conserved, often moved around, sometimes crudely transformed. 

But they almost never created it. Other than doing a spell check, if you were seeing text on a computer, it was probably because some human, somewhere, had typed it.

Language models changed this entirely. 

Now, you and I can type a few sentences into ChatGPT and watch it expand, character by character, line by line, into something new—composed out of thin air, just for you. Language models take your text and stretch it into a different shape, like glass heated and blown through a tube. 

What had previously been an inert collection of bits—a line of characters extending across a screen—is now something different, something potentially alive. When you feed a piece of text to a language model, the text is like an acorn turning into a tree. The acorn itself contains instructions for the tree it will become, and the language model becomes rich dirt, water, and warm summer sun.

In short, language models are free energy for text. Let’s talk about how we can use that function for creative purposes.

A world where every question contains an answer

Language models create a world where every question can be expanded into an answer. 

This is critical for creativity because asking questions is a uniquely human thing to do. 

Apes, for example, share many human characteristics, including their social nature and a proto-culture that allows them to pass some of what they learn on to their offspring. But they are different from humans in at least one important respect: Apes don’t ask questions. 

Question-asking creates room for answers, and answers create room for more questions. This is the first step to getting new ideas and building new things.

Over the centuries, if an answer to a question exists, it’s become increasingly findable for the questioner. Books can answer questions so long as you have the wherewithal to search through them. Google brought us even closer: Many of our most common questions became just a search query away. 

But there are certain of humanity’s answers that have heretofore remained stubbornly unGoogleable. Google only has answers to questions that have already been asked and answered before. For example, try Googling “does Dan Shipper express any skepticism about AI?” I bet you’ll have a difficult time finding a succinct answer.

Language models love questions like this, though:

They expand any question into an answer. Because language models always predict what comes next in a sequence, the question itself points to the beginning of its answer. 

Anyone who spends time around children and listens to the ceaseless questions they ask will know why this is so important. In the past, a question implied a quest to find an answer. Today, questions are answers already—all they need is expansion through language models. 

Let’s talk about some of the most useful kinds of expansions.

Language models as comprehensive expanders