Well, yeah. People are acting like language models are full fledged AI instead of just a parrot repeating stuff said online.
this post was submitted on 28 Jun 2024
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Spicy auto complete is a useful tool.
But these things are nothing more
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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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.
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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.
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I appreciate you taking the time to clarify thank you!
The paper actually argues otherwise, though it's not fully settled on that conclusion, either.
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
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.
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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.
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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
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?
Yeah it is as if someone invented the microwave oven and everyone over hypes it as being able to cook Michelin star meals. People then dismiss it entirely since it cannot produce said Michelin star meals.
They fail to see that is a great reheating machine and a good machine for quick meals.
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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.
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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.