I tend to call it “Glorified predictive text”.
Is it more accurate to call current 'Artificial intelligence' (AI/LLM) as Counterfeit Intelligence?
Submitted 7 hours ago by Bluegrass_Addict@lemmy.ca to [deleted]
Comments
DigDoug@lemmy.world 7 hours ago
Fallibilist@feddit.uk 2 hours ago
Which is a so-called thought terminating clishé: a slogan intended to end discussion without even engaging with it.
radix@lemmy.world 7 hours ago
Autocomplete 2.0
Not_mikey@lemmy.dbzer0.com 4 hours ago
Calling llms “glorified predictive text” is like calling humans “glorified bacteria”
Both have the same goal: guess the next word; survive and reproduce, But the way they accomplish that goal requires orders of magnitude more complexity in the case of a human or LLM.
edgemaster72@lemmy.world 5 hours ago
If you’re going to change some part of the name, I think the “intelligence” is the part you should be changing
Fizz@lemmy.nz 4 hours ago
I think its clearer to say language models
ChaosCatWitch@feddit.online 2 hours ago
Well, the words “artificial” and “counterfeit” both mean “fake,” so the accuracy would be the same.
disregardable@lemmy.zip 7 hours ago
personally I think it’s more accurate to call it either a “pattern identifier” or “random word generator”
Fallibilist@feddit.uk 2 hours ago
Guess what else heavily relies on pattern recognition and probabilities? Humans.
flueterflam@lemmy.world 4 hours ago
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!
scallisto@retrofed.com 6 hours ago
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, 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.
KRAW@linux.community 4 hours ago
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.
TheReanuKeeves@lemmy.world 7 hours ago
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
spankysalmon@fedinsfw.app 7 hours ago
No, no it is not accurate. At all. A toddler knows meaning behind words. Not too many words, but most toddlers understand there is connection outside of, “this other word likely follows this word”. LLMs do not. They have no concept of meaning. They have no concepts at all.
BlameThePeacock@lemmy.ca 6 hours ago
If you dig into what we know about human brains, you might find out we aren’t that different. We come up with words based on the previous words just like processing tokens.
We have have a better model, and more advanced hard(wet)ware but it’s still very much a predictive process based on context.
Fallibilist@feddit.uk 2 hours ago
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.