Knowledge and memory
The other day, I asked Claude how to do something using a particular Ruby library, and it hallucinated three nonexistent methods in a row. We can ask “why do language models do this?” but/and we can also ask, “why doesn’t Robin do this?”
I think it’s because I don’t only know things: I remember learning them. My knowledge is sedimentary, and I can “feel” the position and solidity of different facts and ideas in that mass. I can feel, too, the airy disconnect of a guess.
If you’d challenged me to simply guess the methods I was looking for, I would have typed exactly what Claude hallucinated. Same goes for most Ruby programmers. So, why didn’t I guess, and then find myself sincerely surprised (as Claude surely was) when the methods didn’t exist? Well, checking my memory, I found no record of ever learning them in the first place.
Not that I can connect every Ruby method I know to the precise time and place of its memorization —
I’ll remind you that biologists do not, in the year 2025, know memory’s physical substrate in the brain! Plenty of hypotheses —
Language models don’t have memory at all, because they don’t have experiences that compound and inform each other. Don’t the model weights encode a vast storehouse of memory? No —
Many engineers have pinned their hopes on the context window as a kind of memory, a place where “experiences” might accrue, leave traces. There’s certainly some utility there … but the analogy is waking up in a hotel room and finding a scratchpad full of notes that you don’t remember making. (Language models might, after all, be in hell.) You probably go ahead and trust the notes … but the disorientation of that scenario should be clear. The movie Memento is not the chronicle of a very stable guy’s very normal day.
The solid, structured memory that we use to understand what we know and don’t know —