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.
Well, the words “artificial” and “counterfeit” both mean “fake,” so the accuracy would be the same.
I think its clearer to say language models
If you’re going to change some part of the name, I think “intelligence” is the part you should be focused on
I tend to call it “Glorified predictive text”.
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.
Autocomplete 2.0
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!
personally I think it’s more accurate to call it either a “pattern identifier” or “random word generator”
Guess what else heavily relies on pattern recognition and probabilities? Humans.
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.
Still not random. Also not “intelligence” at all.
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.
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”.
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.
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
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.
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 may have a better model, and more advanced hard(wet)ware but it’s still very much a predictive process based on context.
lol no, no there is no equivalence between associating words with actual things and deriving further words, and calculating the likelihood of words appearing after other words. At all.
ah, Clearly you have the superior brain. Smarter than all the doctors who study this shit.
Good for you, I hope you get the Nobel Prize for Medicine for enlightening the world on how the human brain actually works.
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