AI isn’t any one thing. It’s an broad term used in computer science to refer to any system designed to perform a cognitive task that would normally require human intelligence. The chess opponent on an old Atari console is an AI. It’s an intelligent system - but only narrowly so. That’s called “narrow” or “weak” AI.

It can still have superhuman abilities, but only within the specific task it was built for - like playing chess or generating language.

A large language model like ChatGPT is also narrow AI. It’s exceptionally good at what it was designed to do: generate natural-sounding language. What people expect from it, though, isn’t narrow intelligence - it’s general intelligence. The ability to apply cognitive skills across a wide range of domains the way a human can. That’s something LLMs simply can’t do - at least not yet. Artificial General Intelligence is the end goal for many AI companies, but LLMs are not generally intelligent. However they still fall under the umbrella of AI as a broad category of systems.

If you're going to change some part of the name, I think "intelligence" is the part you should be focused on

I think its clearer to say language models

Considering Meta just got caught using contractors to make phone calls for their "AI" that can make phone calls and make reservations. It's people making the phone calls. It's people fixing the bad answers.

It's Actually (underpaid) Indians

Well, the words "artificial" and "counterfeit" both mean "fake," so the accuracy would be the same.

Several answers touch on this already. "Intelligence" is a very flattering "magic" feeling that doesn't really explain what is happening with LLMs.

Probablistic/Statistical weights are heavily involved here. But it feels "intelligent" because of a combination with ever-expanding processing/computational speed and power that can be thrown at the prompts. Things that were previously limited to on-site super computers can now be accessed via the internets/online services.

It's worth noting also that dynamic "seeding", such as via on-demand searches can contribute to better/worse context resolution.

AI is the common speak for large language model querying (with all the above, plus other details), in mind. So, Probablistic Inferencing (at) Super Speed (i.e. "PISS") seems about right to me!

You think the problem with the term is...the first word?

Poorly simulated intelligence?

Just say LLM

No since the problematic word is "intelligence."

I feel like we should better differentiate between LLMs (the ones that literally tell you to eat rocks), generative models (the ones that are stealing the work of creatives without permission), and the ones that are actually used for neat science stuff, where they are trained on a specific set of data and are doing a specific set of tasks repeatedly in a controlled environment, rather than training on the entire internet and then some and trying to do everything!

I think we should label them as large language models, generative models, and predictive models (maybe? Or maybe "targeted predictive models"?). It's important to emphasise that all of these, even the useful ones, are not intelligent, they do not understand what they are doing. They are just models of data that do thing go in, thing go out.

How about an echo of intelligence, as it rederived from intelligent signals.

🤷

I'm going to start doing it anyway.

I think the entire field of AI (not just the generative AI subset) should've been called "simulated intelligence" or something like that. It points to the goal of the field (i.e. to achieve something similar to intelligence) without the implicit claim that any of the contributions achieve true intelligence.

It's accurate to call AI in it's current state "a toddler who knows how to google and read pretty well"

You can even say that to AI and they will agree with it