On the Dangers of Overused AI Metaphors
Calling language models “bullshit generators” and people who want AI regulation “modern Luddites” is bad for everyone
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In our attempts to understand the new from the old and the unknown from the known, we risk either stripping away too much truth or adding too much falsehood so that our inquiries inevitably become futile.
Metaphors and historical comparisons are always imperfect. They often entail a trade-off between fidelity and simplicity with a touch of motivation to drive an argument forward. Overusing them inadvertently damages the quality of the conversation in a way that affects our understanding and that of others.
This discoursive illness pains AI today.
This article is a selection from The Algorithmic Bridge, an educational newsletter whose purpose is to bridge the gap between AI, algorithms, and people. It will help you understand the impact AI has in your life and develop the tools to better navigate the future.
Successful metaphors as signals of partisanship
AI metaphors fall victim to a singular phenomenon: death by success.
When I first heard GPT-3 referred to as a “stochastic parrot,” term coined by linguist Emily M. Bender, something clicked for me. It nicely captured one of the most problematic — and idiosyncratic — features of language models (i.e., that they, in contrast to humans, output intention-less pseudorandom utterances). The idea went viral and resonated with a lot of people: anyone could point out the limitations of language models with just a pair of words. A succinct, winning argument on AI debates.
But it’s been two years of seeing it everywhere. The term has been tampered with to the point of emptying it of meaning: the metaphor has eaten the substance within. When I read it now, I realize it doesn’t play the role it was conceived for anymore because its ideological charge…
