- cross-posted to:
- fuck_ai@lemmy.world
- cross-posted to:
- fuck_ai@lemmy.world
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Well, yeah. People are acting like language models are full fledged AI instead of just a parrot repeating stuff said online.
Spicy auto complete is a useful tool.
But these things are nothing more
The paper actually argues otherwise, though it’s not fully settled on that conclusion, either.
Whenever any advance is made in AI, AI critics redefine AI so its not achieved yet according to their definition. Deep Blue Chess was an AI, an artificial intelligence. If you mean human or beyond level general intelligence, you’re probably talking about AGI or ASI (general or super intelligence, respectively).
And the second comment about LLMs being parrots arises from a misunderstanding of how LLMs work. The early chatbots were actual parrots, saying prewritten sentences that they had either been preprogrammed with or got from their users. LLMs work differently, statistically predicting the next token (roughly equivalent to a word) based on all those that came before it, and parameters finetuned during training. Their temperature can be changed to give more or less predictable output, and as such, they have the potential for actually original output, unlike their parrot predecessors.
You completely missed the point. The point is people have been lead to believe LLM can do jobs that humans do because the output of LLMs sounds like the jobs people do, when in reality, speech is just one small part of these jobs. It turns, reasoning is a big part of these jobs, and LLMs simply don’t reason.
Whenever any advance is made in AI, AI critics redefine AI so its not achieved yet according to their definition.
That stems from the fact that AI is an ill-defined term that has no actual meaning. Before Google maps became popular, any route finding algorithm utilizing A* was considered “AI”.
And the second comment about LLMs being parrots arises from a misunderstanding of how LLMs work.
Bullshit. These people know exactly how LLMs work.
LLMs reproduce the form of language without any meaning being transmitted. That’s called parroting.
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AI is a marketing buzzword. When someone claims that so-called “AGI” is close, they’re either doing marketing or falling for marketing.
Since you didn°t address the “parroting” part, I’m assuming that you retract your point.
LLMs reproduce the form of language without any meaning being transmitted. That’s called parroting.
Even if (and that’s a big if) an AGI is going to be achieved at some point, there will be people calling it parroting by that definition. That’s the Chinese room argument.
You’re moving the goalposts.
Me? How can I move goalposts in a single sentence? We’ve had no previous conversation… And I’m not agreeing with the previous poster either…
By entering the discussion, you also engaged in the previops context. The discussion uas about LLMs being parrots.
And the argument was if there’s meaning behind what they generate. That argument applies to AGIs too. It’s a deeply debated philosophical question. What is meaning? Is our own thought pattern deterministic, and if it is, how do we know there’s any meaning behind our own actions?
LLMs work differently, statistically predicting the next token (roughly equivalent to a word) based on all those that came before it, and parameters finetuned during training.
Which is what a parrot does.
Yeah this is the exact criticism. They recombine language pieces without really doing language. The end result looks like language, but it lacks any of the important characteristics of language such as meaning and intention.
If I say “Two plus two is four” I am communicating my belief about mathematics.
If an llm emits “two plus two is four” it is outputting a stochastically selected series of tokens linked by probabilities derived from training data. If the statement is true or false then that is accidental.
Hence, stochastic parrot.
If i train an LLM to do math, for the training data i generate
a+b=cstatements, never showing it the same one twice.It would be pointless for it to “memorize” every single question and answer it gets since it would never see that question again. The only way it would be able to generate correct answers would be if it gained a concept of what numbers are, and how the add operation operates on them to create a new number.
Rather than memorizing and parroting it would have to actually understand it in order to generate responses.It’s called generalization, it’s why large amounts of data is required (if you show the same data again and again then memorizing becomes a viable strategy)
If I say “Two plus two is four” I am communicating my belief about mathematics.
Seems like a pointless distinction, you were told it so you believe it to be the case? Why can’t we say the LLM outputs what it believes is the correct answer? You’re both just making some statement based on your prior experiences which may or may not be true
You’re arguing against a position I didn’t put forward. Also
Seems like a pointless distinction, you were told it so you believe it to be the case? Why can’t we say the LLM outputs what it believes is the correct answer? You’re both just making some statement based on your prior experiences which may or may not be true
This is what excessive reduction does to a mfer. That is just such a hysterically absurd take.
but, the LLM has faith!
I’m a curmudgeonly physics nerd, it’s very strange being on the side of a debate going “No now come on, that’s way too reductive”
The AI builds some kind of a model of the world in order to better understand the input and improve the output prediction,
You have some mental model of how maths work which you have built up through school and other experiences in your life,
You both are given a maths problem, you both give an answer based on your understanding of mathematics
The algorithm assigns weights to nodes in a neural network. These weights are derived by statistical association of tokens in the training data after they have been cleaned.
That is so enormously far from how we think humans learn (you don’t teach a kid to understand theory of mind by plopping them in front of the Gutenberg project and saying good luck, and yet they learn to explain theory of mind problems all the same) that it is just comically farcial to assume something similar is happening underneath.
It is very interesting that llms are able to appear to be conversational but claiming they have some sort of mind with an understanding of maths is as ridiculous as suggesting a chess bot understands the Pauli exclusion principle because it never moves two pieces into the same physical space.
You’ve been speaking with your chest this whole time and now that we’re into the nitty gritty you really just said “The ai does… something!” It’s so general a description that by your measure automated thermostats are engaging in human reasoning when they make it a little bit cooler on a hot day.
You might’ve been oversimplifying on purpose. I just can’t help but think you have no idea how LLMs work outside of this inherently flawed comparison to human thought.
This is parrot libel
You take in some information, combine that with some precious experiences, and then output words
Which is what an LLM does.
Flat epistemological statements like this are why I feel like more STEM people need to take Philosophy.
Big fan of philosophy, so please do tell me how my joke is wrong? Does knowledge and beliefs not come from life experiences?
AI hasn’t been redefined. For people familiar with the field it has always been a broad term meaning code that learns (and subdivided in many types of AI), and for people unfamiliar with the field it has always been a term synonymous with AGI. So when people in the former category put out a product and label it as AI, people in the latter category then run with it using their own definition.
For a long time ML had been the popular buzzword in tech and people outside the field didn’t care about it. But then Google and OpenAI started calling ML and LLMs simply “AI” and that became the popular buzzword. And when everyone is talking about AI, and most people conflate that with AGI, the results are funny and scary at the same time.
and for people unfamiliar with the field it has always been a term synonymous with AGI.
Gamers screaming about the AI of bots/NPCs making them mad beg to differ
I was going to add a note about the exception of video games but decided I’m digressing
LLMs have more in common with chatbots than AI.
You are very skilled in the art of missing the point. LLMs can absolutely be used as chatbots, amongst other things. They are more advanced than their predecessors in this, and work in a different way. That does not stop them from being a form of artificial intelligence. Chatbots and AI are not mutually exclusive terms, the first is a subset of the second. And you may indeed be referring to AGI or ASI as AI, a misconception I addressed in my earlier comment.
I work on ML projects. I’m telling you, as a matter of fact, you do not understand what you are talking about.
Try being less smug and pedantic.
Oh, wow! You ‘work in ML projects’, do you?
Then maybe you could point out specific examples of me not knowing what I’m talking about, instead of general dismissiveness?
I’m not here to teach you and I don’t care if you ever learn.
If you’re interested check out your community college.
You have no obligation to teach me, correct. But if you choose not to, you have no right to criticise me without backing up your claims. Pick one.
I appreciate you taking the time to clarify thank you!
https://link.springer.com/article/10.1007/s10676-024-09775-5
Link to the article if anyone wants it
Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005)
Now I kinda want to read On Bullshit
Don’t waste your time. It’s honestly fucking awful. Reading it was like experiencing someone mentally masturbating in real time.
Yep. You’re smarter than everyone who found it insightful.
That’s actually a fun read
fucking love that article. sums up everything wrong with AI. Unfortunately, it doesn’t touch on what AI does right: help idiots like me achieve a slight amount of competence on subjects that such people can’t be bothered with dedicating their entire lives to.
Suddenly it dawned on me that I can plaster my CV with AI and win over actual competent people easy peasy
What were you doing between 2020 and 23? I was working on my AI skillset. Nobody will even question me because they fucking have no idea what it is themselves but only that they want it.
As an engineering manager, I’ve already seen cover letters and intro emails that are so obviously AI generated that it’s laughable. These should be used like you use them for writing essays, as a framework with general prompts, but filled in by yourself.
Fake friendliness that was outsourced to an ai is worse than no friendliness at all.
I didn’t mean AI generated anything though 🙄. I meant put lots of ‘AI’ keyword in the resume in whatever way looks professional but in reality is pure bullshit
Watch their neuron being activated as they see magic word. Gotta play the marketing game.
You want to be AI ready? Hire me. I have spent three years working with AI and posses invaluable experience that will elevate your company into a new era of rapid development.
It feels like you didn’t quite understand… If you’ve ever read an AI essay, you can see some of the way they currently write. When you see facts and figures thrown in from the internet in terms of what the company does and they sound… Artificial… It’s rather obvious that it was AI written. I’m currently getting AI spam and it’s also quite easy to see and detect. It’s the same thing.
Someone used an AI tool to write a cover letter and sent it to me. I’ve seen this a few times. It seems very obvious when you come across it.
I’m sure it’ll get better in the future, but right now it needs massaging in order to sound real. There’s a very obvious uncanny valley that exists with some AI writing. That’s what I’m talking about.
Okay but we are talking about two different things which is fine by me of course but it is a little tricky. I agree though on that second topic
It’s extremely easy to detect this. Recruiters actively filter out resumes like this for important roles.
Plot-twist: The paper was authored by a competing LLM.
Just reading the intro pulls you in
We draw a distinction between two sorts of bullshit, which we call ‘hard’ and ‘soft’ bullshit
This paper should cite On Bullshit.
It does. It’s even cited in the abstract, and it’s the origin of bullshit as referenced in their title.
It talks extensively about On Bullshit, lol.
Yup. The paper is worth actually reading
There are things that chatgpt does well, especially if you temper your expectations to the level of someone who has no valuable skills and is mostly an idiot.
Hi, I’m an idiot with no valuable skills, and I’ve found chatgpt to be very useful.
I’ve recently started learning game development in godot, and the process of figuring out why the code that chatgpt gives me doesn’t work has taught me more about programming than any teacher ever accomplished back in high school.
Chatgpt is also an excellent therapist, and has helped me deal with mental breakdowns on multiple occasions, while it was happening. I can’t find a real therapist’s phone number, much less schedule an appointment.
I’m a real shitty writer, and I’m making a wiki of lore for a setting and ruleset for a tabletop RPG that I’ll probably never get to actually play. ChatGPT is able to turn my inane ramblings into coherent wiki pages, most of the time.
If you set your expectations to what was advertised, then yeah, chatgpt is bullshit. Of course it was bullshit, and everyone who knew half of anything about anything called it. If you set realistic expectations, you’ll get realistic results. Why is this so hard for people to get?
Because few people know what’s realistic for LLMs
Intelligence is a very loaded word and not very precise in general usage. And i mean that amongst humans and animals as well as robots.
I’m sure the real AI and compsci researchers have precise terms and taxonomies for it and ways to measure it, but the word itself, in the hands of marketing people and the general population as an audience . . . not useful.
Hah I had that exact same experience with Godot
This is something I already mentioned previously. LLMs have no way of fact checking, no measure of truth or falsity built into. In the training process, it probably accepts every piece of text as true. This is very different from how our minds work. When faced with a piece of text we have many ways to deal with it, which range from accepting it as it is to going on the internet to verify it to actually designing and conducting experiments to prove or disprove the claim. So, yeah what ChatGPT outputs is probably bullshit.
Of course, the solution is that ChatGPT be trained by labelling text with some measure of truth. Of course, LLMs need so much data that labelling it all would be extremely slow and expensive and suddenly, the fast moving world of AI to screech to almost a halt, which would be unacceptable to the investors.
It’s even more than just “accepting everything as true” the machines have no concept of true. The machine doesn’t think. It’s a combination of three processes: prediction algorithm for the next word, algorithm that compares grammar and sentence structure parity, and at least one algorithm to help police the other two for problematic statements.
Clearly the problem is with that last step, but the solution would be a human or a general intelligience, meaning the current models in use will never progress beyond this point.
Your statement on no way of fact checking is not a 100% correct as developers found ways to ground LLMs, e.g., by prepending context pulled from „real time“ sources of truth (e.g., search engines). This data is then incorporated into the prompt as context data. Well obviously this is kind of cheating and not baked into the LLM itself, however it can be pretty accurate for a lot of use cases.
Does using authoritative sources is fool proof? For example, is everything written in Wikipedia factually correct? I don’t believe so unless I actually check it. Also, what about reddit or stack overflow? Can they be considered factually correct? To some extent, yes. But not completely. That is why most of these LLMs give such arbitrary answers. They extrapolate on information they have no way knowing or understanding.
I don’t quite understand what you mean by extrapolate on information. LLMs have no model of what an information or the truth is. However, factual information can be passed into the context, the way Bing does it.
This is very different from how our minds work.
Childrens’ minds work similarly.
Why do you even think that? Children don’t ask questions? Don’t try to find answers?
Sure they do. But they also trust adults a lot. Children try to find answers only because they have stimulus other than humans telling them things, but if that stimulus is missing, they will believe the adult. The environments that AI “grow up” in are different, but they are very similar from a mental perspective.
How many times have you heard the story of something hearing something false from a family member and holding it close to their heart for years?
Now that I think about children develop critical thinking at around the age of 10. Perhaps you are right. But, the question remains, will LLMs develop such critical thinking on it’s own or are we still missing something?
Wouldn’t it be funny if the article was written by chat GPT.
Because these programs cannot themselves be concerned with truth, and because they are designed to produce text that looks truth-apt without any actual concern for truth, it seems appropriate to call their outputs bullshit.
This is actually a really nice insight on the quality of the output of current LLMs. And it teaches about how they work and what the goals given by their creators are.
They are but trained to produce factual information, but to talk about topics while sounding like a competent expert.
For LLM researchers this means that they need to figure out how to train LLMs for factuality as opposed to just sounding competent. But that is probably a lot easier said than done.
Unlike OpenAI, this article is actually open.
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clearly they have never heard of harry g frankfurts (excellent) „on bullshit“
The paper explicitly states that they are calling ChatGPT “bullshit” in the Frankfurtian sense and they cite “On Bullshit” as the source for that definition. It’s right there in the introduction.
You’d know this if you had read the paper or even checked whether your statement were true. So either you read it and then lied deliberately, or you didn’t read the paper nor actually care about the truth value of your own statement, rendering your comment itself bullshit in the Frankfurtian sense.
By grabthar’s hammer, what a put down!
Best movie ever.
Sheesh, leave some for the rest of us to pick on, you savage!
jesus christ ofc i didn‘t read the paper, i was just making a joke ffs
It was a bad joke
Worse than a bad joke: an ill-informed joke
Actually, they reference him.
