Poorly simulated intelligence?
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
No since the problematic word is “intelligence.”
I don’t know. SI does actually have a certain ring to it. Stolen Intelligence.
Just say LLM
nah, abbreviations are honestly kinda stupid…
everyone abbreviates as much as they can with as much stuff as they can, and you end up in dumb scenarios where you’re constantly asking people to define their abbreviations rendering them frustrating and useless
Ok just say “large language model”
It’s more descriptive of what it actually is? It’s not intelligence, so “counterfeit intelligence” is just as bad.
And yeah when it’s random ass abbreviations that’s valid, but when it’s “LLM” which is in every single news cycle, not as much of a problem… especially if you are on a site like lemmy. No one is going to ask you.
Abominable intelligence.
You think the problem with the term is…the first word?
no, but it drives a point home rather then just calling it shit
🤷
I’m going to start doing it anyway.
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.
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Autocomplete 2.0
Which is a so-called thought terminating clishé: a slogan intended to end discussion without even engaging with it.
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.
I think its clearer to say language models
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Well, the words “artificial” and “counterfeit” both mean “fake,” so the accuracy would be the same.
If you’re going to change some part of the name, I think “intelligence” is the part you should be focused on
personally I think it’s more accurate to call it either a “pattern identifier” or “random word generator”
Random? No. Probabilistic.
it’s inherently random, though, because it is working with things that have meanings and it cannot understand what the meanings are.
You keep using that word. I do not think it means what you think it means.
I agree that LLMs do not understand what it is they are predicting. At least no more than any other computer program “understands” its output. Which is to say: they are not conscious beings experiencing things.
This doesn’t mean LLM output is random. It means they aren’t thinking persons. You may have confused random with non-deterministic. LLMs are complex guessing machines that require a fudge-factor to produce meaningfully useful output. If the guesses were fully random then they’d have a significantly narrower field of practical applications.
I just don’t understand why you think it needs to be “fully random” to be called random, when the output is in fact random. To me, when you can ask it the same question multiple times and receive different answers, it’s random and unreliable. You may argue it only changes its answer randomly 10% of the time or whatever- it’s still random. It doesn’t matter if it only answers incorrectly 10% of the time or 5% of the time. Every time the answer is unreliable, because sometimes it changes its answer for no reason.
You don’t understand because you’re either not paying attention or because you’re trolling.
Random would be output like this:
Ostrich maypole dandy ironic. Great cucumber zebra!
Or this:
fhsjarbskzkdbsjzb kOdbg zkakfiebwkl
Random would be a password generator. Random would be utter nonsense. Incoherent. That’s not what LLMs produce.
Still not random. Also not “intelligence” at all.
That depends on your definition of “random”. They’re non-deterministic; the same inputs will produce multiple outputs, and I think that’s the point.
So, it depends whether you’re referring to most people’s definition of “random” or a mathematician’s, because they often differ. One characteristic of “true randomness” (as defined in mathematical terms) is that eventually, by accident, patterns emerge. When people inevitably notice these patterns, the system appears less random.
Systems that check ahead for such patterns and adjust outcomes to avoid them are called “pseudo-random”. Even though the outcomes are carefully calculated, they’re perceived as being more random because they lack patterns.
Notable examples I can think of off the top of my head are Diablo 3 and the official Risk mobile app.
In Diablo 3, the drop tables started off using a truly random number generator. Some players noticed long stretches of no legendary drops, other players saw multiple legendaries in a row. People complained the drops were “rigged”, so Blizzard altered the algorithm to be pseudo-random and players stopped ccomplaining. Now, if a certain amount of time goes by and you haven’t had a legendary drop, you’re guaranteed one. Once a legendary drops, you’re guaranteed NOT to have one for a certain amount of time.
With Risk, same story with the dice rolling algorithm; players abjectly refused to accept the outcomes were random, despite the devs adding functionality to count your dice rolls and every players’ showed a perfectly uniform distribution.
A mathematician would agree that an LLM isn’t random because the next node in the chain is determined by the current nodes. But, since the algorithm won’t generate the same output every time it’s provided identical input (like a calculator), that satisfies most people’s definition of the word “random”.
Bold claim considering there is no agreed upon definition on what we even mean by the term intelligence.
- The ability to acquire, understand, and use knowledge.
- the ability to learn or understand or to deal with new or trying situations
- the ability to apply knowledge to manipulate one’s environment or to think abstractly as measured by objective criteria (such as tests)
- the act of understanding
- the ability to learn, understand, and make judgments or have opinions that are based on reason
- It can be described as the ability to perceive or infer information; and to retain it as knowledge to be applied to adaptive behaviors within an environment or context.
Guess what else heavily relies on pattern recognition and probabilities? Humans.
Except most of us suck at it. Very few of us are really good at it.
But it’s true, AI models make the same stupid mistakes humans do, are are convinced those mistakes are truth and reality… and are unable to error correct when corrected or given new information
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!
