Language, LLMs & Machine Understanding
It explained my own problem back to me — but does it mean a word of it?
The question
I asked it to explain a thing I'd been stuck on for a week, and it explained it — cleanly, in order, with the one example I'd needed and hadn't found. I sat back. And then, the way you do, I told myself the thing everyone tells themselves: it doesn't really understand this. It's autocomplete. It's just guessing the next word from a trillion words it swallowed. There's nobody in there who knows what any of it means.
Fine. So I tried to say what it was missing. The machine doesn't understand "gravity," I thought, because — because what? Because it's never dropped a glass? But I've never been to the centre of a star or watched a galaxy bend light, and I understand "gravity." Because the word isn't connected to anything for it? But I learned half my words the same way it did — from other words, from being told, never having touched the thing. I went looking for the line between what I do with a word and what the machine does, the line that makes mine meaning and its noise. And every place I drew it, I found myself standing on the wrong side.
The episodes
Each episode builds on the one before, and the finale hands the question back to you sharper, not settled.
9 of 9 transcripts are readable now — audio is coming.
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1
It explained it back to me
A language model explains your own work back to you, with the one example you needed — and the immediate reaction, that it does not actually understand any of this, turns out hard to state. Every test reached for to keep the machine out disqualifies some of your own words too, and a question comes into view that has never actually been asked — what is it for a word to mean something?
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2
The most elaborate parrot ever built
The strongest case that the machine is a parrot — John Searle's Chinese room, where flawless answers issue from a sealed room with no understanding inside, and Emily Bender's octopus and "stochastic parrots" arguments, on which the fluency is projected onto pure form. The position is built at full strength by the people who hold it most seriously, and left standing.
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3
Know a word by the company it keeps
Firth's dictum — you shall know a word by the company it keeps — becomes a rival theory of meaning. If a word's meaning is the pattern of the other words it runs with, the machine has exactly that, and there was never a second thing hidden behind the pattern. The two accounts of meaning are left pulling directly against each other, with no way yet to choose.
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4
What's the missing extra?
Ludwig Wittgenstein's question, put to the listener's own case — point to the grasp, the understanding beneath your use of a word that is something more than everything you can already do with it. The search comes back empty-handed, and the test of continuing correctly into a case never shown turns out to be one the machine does not fail.
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5
What it was never built to do
The tests that do hold. Stevan Harnad on symbol grounding, Ruth Millikan on words shaped by a history of tracking the world, and Grice on the intention to be understood — real differences between speaker and machine — set beside the view that the yes-or-no question was the wrong shape from the start. Neither side is allowed to win.
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6
Say what understanding is
No new theory this time. The episode asks the listener to state, in plain words, the rule they have been applying all along — the one thing the machine lacks that makes its words noise and yours meaning — and shows that stating it means adopting one contested account of meaning, resting on two assumptions never checked.
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7
It runs through you — or does it?
The rule stated last week is now applied — turned around and run over the listener's own vocabulary. Some rules place half of one's own words on the machine's side of the line; others hold and name something real. The episode does not decide which — the rule you chose decides, and nothing available says which rule is right.
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8
Whose words is it using, anyway?
Two histories. The idea that meaning is a word's connection to a thing in the world turns out to be one side of a hundred-year-old dispute, with Frege and Russell in its ancestry, rather than a plain fact; and the machine's fluency turns out to be fluency in a particular corpus — someone's language, not the world's.
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9
Are you sure you do more?
The final episode sets every position side by side — the parrot, the company of words, the grounding, history and intention tests, and the view that the question was malformed — and tests only one thing, the quiet certainty that you do more than the machine does. No verdict on the machine is offered; the question is handed back sharper than it arrived.
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Thinkers
The thinkers and traditions this series draws on: