When someone is trying to hold this untenable position, a few things tend to come up (not everytime, not everyone):

1- Defensiveness. People read criticism of the systems and proposed uses of the systems as accusations that users are "bad people". Thus a criticism of the tech lands as criticism of the user, and tensions flare.
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4- Tone policing. People who are trying to occupy that uncomfortable, untenable space will claim that clear statements of harms/strong principles against use of these systems will "turn others away" as if the centrists are the ones actually pushing for more ethical practice.

But this "other people won't listen" remark I think is really a way of saying "This makes me uncomfortable" while trying to claim to be on the right side of history at the same time.
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OCR can be a useful tool for many research projects! OCR is also the kind of technology that gets better with better language models, i.e. more fine-grained models of which word(parts) go where. That has been true since before "genAI" and will be true after.

Just because you can use the synthetic media extruding machines to approximate the task of OCR, however, doesn't mean that that task can or should be used to justify the use of "genAI" in research.
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I think another important step is a values examination. What is important to you? How are those values supported or not by entering the discourse in a way that holds space for OpenAI/Anthropic/Google/Meta and all the other actors in this massive push to shove "AI" into every part of our lives as "not all bad"?

What are your research goals, what do you value about participating in scholarship, how can you meet those goals/act in accordance with those values and what obstacles are in your way?
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Part of what makes that middle ground untenable and uncomfortable, I think, is that it requires carrying water for these clearly bad actors. You can set that bucket down and step out onto firmer ground.

This does require going against the mainstream, but that gets easier when a) you find you're not alone and b) you see how much of mainstream opinion on this is actually the result of marketing.

/fin for now

@xvf17 @emilymbender THIS IS SO UPSETTING. I am one of those folks advocating for using certain types of machine learning responsibly, like "identify the patients with cancer" which ML tools can do better than humans, and which were trained using ethical data sources.

But the LLM field is just a cesspool. I can *see* use cases for it, but holy shit fruit of the corrupt root system with that training data.

@SomeVeganCheeseIsOk
Oh no dude. This prof thinks you are an asshole then.

Read what she wrote again. She is full of fallacies herself.
@xvf17 @emilymbender

@xvf17
As far as I know AI was coined as simulated intelligence. I think LLMs are exactly that.
Ai is a term that we used to describe a bunch of systems. Chess engines, opponents in video games,... Now LLMs. Perhaps someday when we have even better systems we will call them ai, but for now ai seems to fit LLMs completely.

@SomeVeganCheeseIsOk @emilymbender

@Noisecolor @xvf17 @SomeVeganCheeseIsOk @emilymbender AI is literally the abbreviation of *artificial* intelligence. Not simulated intelligence. That feels like a real "moving the goalposts" moment.

AI as it has historically/originally been used is referencing a legitimate, non-organic intelligence. LLMs are smoke and mirrors, when it comes to intelligence. There is no cognition, no understand.

Personally, I prefer GenAI over scare quotes, but both are more accurate than LLM = AI

@theadhocracy
Not at all. You can freely browse the web or ask a chat who coined the term and why. It will be an interesting read I can assure you and you will understand that ai is not meant to refer to a specific human level intelligence like you mean. It wouldn't make sense at that time anyway.

@xvf17 @SomeVeganCheeseIsOk @emilymbender

@Noisecolor @xvf17 @SomeVeganCheeseIsOk @emilymbender Okay, literally my first reult:

"The term "artificial intelligence" was coined in 1956 at Dartmouth College by John McCarthy and colleagues to describe the new field of creating machines that could think and learn like humans."

So, it *means* "artificial", not "simulated". It covers inorganic "machines". And it references *thinking* and *learning*.

We can argue semantics on learning, but an LLM ticks 0 of those boxes.

@Noisecolor @xvf17 @SomeVeganCheeseIsOk @emilymbender And just to head off "learning", yes genAI has roots on "machine learning". But the output, the LLM, does not learn "like a human". Some may have reinforcement algorithms, but all that's fundamentally doing is a loop: take the new data, run your previous "training" algorithm, update your output.

Humans learn through understanding and cognition. An LLM does not. Otherwise they'd be able to do simple calculus before they could write essays.

@theadhocracy
"Doesn't learn like a human": True. Doesn't matter. Learning is adapting based on data. LLMs do that. That's the definition.
"Just a loop": Human brains are also "just" electrical impulses. Mechanism doesn't negate capability.
"Calculus vs. Essays": Outdated. Modern models handle both. Training order is not intelligence limit.

@xvf17 @SomeVeganCheeseIsOk @emilymbender

@Noisecolor @xvf17 @SomeVeganCheeseIsOk @emilymbender Yeah if you're just going to redefine words, then I guess there go possible way to help you understand why people use specific terms to mean things...

Like, I get that language evolves, but this is absurd 😂 "Learning is adapting based on data", sure if you want to argue that a model "learns", but that's not what anyone means or how any dictionary defines it.

Learning implies knowledge and understanding. And LLM cannot understand.