Journal tags: buzzwords

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.

John Scalzi recently wrote:

“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.

There are different kinds of buzzwords.

Some buzzwords are useful. They take a concept that would otherwise require a sentence of explanation and package it up into a single word or phrase. Back in the day, “ajax” was a pretty good buzzword.

Some buzzwords are worse than useless. This is when a word or phrase lacks definition. You could say this buzzword in a meeting with five people, and they’d all understand five different meanings. Back in the day, “web 2.0” was a classic example of a bad buzzword—for some people it meant a business model; for others it meant rounded corners and gradients.

The worst kind of buzzwords are the ones that actively set out to obfuscate any actual meaning. “The cloud” is a classic example. It sounds cooler than saying “a server in Virginia”, but it also sounds like the exact opposite of what it actually is. Great for marketing. Terrible for understanding.

“AI” is definitely not a good buzzword. But I can’t quite decide if it’s merely a bad buzzword like “web 2.0” or a truly terrible buzzword like “the cloud”.

The biggest problem with the phrase “AI” is that there’s a name collision.

For years, the term “AI” has been used in science-fiction. HAL 9000. Skynet. Examples of artificial general intelligence.

Now the term “AI” is also used to describe large language models. But there is no connection between this use of the term “AI” and the science fictional usage.

This leads to the ludicrous situation of otherwise-rational people wanted to discuss the dangers of “AI”, but instead of talking about the rampant exploitation and energy usage endemic to current large language models, they want to spend the time talking about the sci-fi scenarios of runaway “AI”.

To understand how ridiculous this is, I’d like you to imagine if we had started using a different buzzword in another setting…

Suppose that when ride-sharing companies like Uber and Lyft were starting out, they had decided to label their services as Time Travel. From a marketing point of view, it even makes sense—they get you from point A to point B lickety-split.

Now imagine if otherwise-sensible people began to sound the alarm about the potential harms of Time Travel. Given the explosive growth we’ve seen in this sector, sooner or later they’ll be able to get you to point B before you’ve even left point A. There could be terrible consequences from that—we’ve all seen the sci-fi scenarios where this happens.

Meanwhile the actual present-day harms of ride-sharing services around worker exploitation would be relegated to the sidelines. Clearly that isn’t as important as the existential threat posed by Time Travel.

It sounds ludicrous, right? It defies common sense. Just because a vehicle can get you somewhere fast today doesn’t mean it’s inevitably going to be able to break the laws of physics any day now, simply because it’s called Time Travel.

And yet that is exactly the nonsense we’re being fed about large language models. We call them “AI”, we look at how much they can do today, and we draw a straight line to what we know of “AI” in our science fiction.

This ridiculous situation could’ve been avoided if we had settled on a more accurate buzzword like “applied statistics” instead of “AI”.

It’s almost as if the labelling of the current technologies was more about marketing than accuracy.