Coattails
When I talk about large language models, I make sure to call them large language models, not “AI”. I know it’s a lost battle, but the terminology matters to me.
The term “AI” can encompass everything from a series of if/else statements right up to Skynet and HAL 9000. I’ve written about this naming collision before.
It’s not just that the term “AI” isn’t useful, it’s so broad as to be actively duplicitous. While talking about one thing—like, say, large language models—you can point to a completely different thing—like, say, machine learning or computer vision—and claim that they’re basically the same because they’re both labelled “AI”.
If a news outlet runs a story about machine learning in the context of disease prevention or archeology, the headline will inevitably contain the phrase “AI”. That story will then gleefully be used by slopagandists looking to inflate the usefulness of large language models.
Conflating these different technologies is the fallacy at the heart of Robin Sloan’s faulty logic:
If these machines churn through all media, and then, in their deployment, discover several superconductors and cure all cancers, I’d say, okay … we’re good.
“AI” is mostly a marketing phrase for a bunch of different processes and tools which in a different era would have been called “machine learning” or “neural networks” or something else now horribly unsexy.
But I’ve noticed something recently. More than once I’ve seen genuinely-useful services refer to their technology as “traditional machine learning”.
First off, I find that endearing. Like machine learning is akin to organic farming or hand-crafted furniture.
Secondly, perhaps it points to a severing of the ways between machine learning and large language models.
Up until now it may have been mutually benificial for them to share the same marketing term, but with the bubble about to burst, anything to do with large language models might become toxic by association, including the term “AI”. Hence the desire to shake the large-language model grifters from the coattails of machine learning and computer vision.