A phenomenon I've noticed recently is people trying to occupy some untenable middleground wrt to the use of systems sold as "AI" -- this is a position where people try to recognize the harms of this tech but also hold space for "responsible" or "ethical" use.
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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
Human beings have a genius for mean-spirited subtle insults.
In a recent meeting, a coworker was profusely praised for using CoPilot to summarize some very dry & voluminous documentation. It was kind of over the top.
Soon my puzzled coworker clued that they were obliquely putting her down. They were implying she was lazy and insufficiently bright enough to accomplish the task without a crutch like AI.
What's concerning was Copilot praising her in a similar fashion
@xvf17 @emilymbender Eh, don't get worked up. There's lots of bad stuff out there, no use wasting time on hate.
Denounce it, work against it, but hate is way too much effort only to the detriment of your well being.
I'd see hate being useful to stir to action at a rally but the podcast is pretty much ether.
@cohentheblue @xvf17 @emilymbender
The result of not using AI is protecting yourself, your family, and your community. The result of not using AI is harm reduction, battling against the devastating social, political, and environmental costs of AI. The result of not using AI, is making it easier for others to refuse to use it as well.
So please, stop using AI.
@pip @xvf17 @emilymbender Where did you get that any of us uses AI?
Have you even glanced at our profiles for that matter?
You come across as an LLM yourself.
Nothing more frustrating than being misunderstood deliberately and repeatedly.
@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.
@Noisecolor @xvf17 @emilymbender "if they're so bad no one will use them"
Ohhhhh, dear. How do you want to approach this topic? Because persistant human use of incredibly bad tools and ideas has a wonderful, fascinating history. We can go on for ages on the influence of marketing and habit on human behavior! It's sad, and funny, and fascinating.
@SomeVeganCheeseIsOk
I was being checky to a guy who was clearly out of his depth.
It's ok , you don't have to explain marketing to me, sadly I worked in marketing for quite a while.
More sadly still, we failed to gain a few hundred millions of users and a few trillion capital.
@xvf17 @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.
@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.
@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 š "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.
@SomeVeganCheeseIsOk @Noisecolor @xvf17 @emilymbender I can agree with that, but the term in this case was being used in a definition, specifically "machines that can learn like a human". That's why I was referencing it.
I did explicitly add a comment about how "learning" can be applied to algorithmic behaviour (ML etc.), but 1) that isn't the way the term is used in the definition of AI (scope is narrower), and 2) I don't agree that's enough to make the leap for LLMs to *intelligence*
@SomeVeganCheeseIsOk @Noisecolor @xvf17 @emilymbender But yeah, in this instance, I still think its a real stretch to claim that GenAI models **learn**. They may be born from reinforcement algorithms, and we may have adopted "learning" to describe aspects of that behaviour, but imo that is a marketing term.
Biological algorithms actually *learn* and modify their behaviour without oversight. So I'm not saying that ML is never learning. Just that I disagree that such a broad redefinition is okay
@Noisecolor @theadhocracy @xvf17 @emilymbender You would REALLY benefit from reading "How to Win Friends and Influence People", because you can be right and still set people deeply against you, and it isn't their bias- it's very much your approach and attitude. A marketing person should already know this though.
People today can get good information from folks who aren't treating them with contempt. Why on earth would they talk to someone rude?
@Noisecolor @xvf17 @SomeVeganCheeseIsOk @emilymbender No it's goalposts shifting, you're just changing the meaning of words š
And I'm not even sure why? Why do you **want** LLMs to be "AI"? If we actually get true AI, it'll be so much more than a statistical averaging model. Shouldn't we reserve the term for when it's applicable, and use terms relevant to the tools we have (like LLM)?
@theadhocracy
I'm making a point how the anti ai mob is silly. Nothing else. It's a pointless debate.
@SomeVeganCheeseIsOk
It's not a marketing term. We have always used this term. There was no marketing team involved.
Humans need words to describe certain stuff.
When we do choose those words those words are assigned that meaning. It's linguistics, it has nothing to do with who you think is a good or bad guy or what a certain group might like or not.
In certain times words can change or the meaning of words can change. But in this case it's pretty clear I think.
@Noisecolor @xvf17 @emilymbender In early usage, artificial intelligence was specifically used as a term for computers which had human level or better intelligence, whether it had human-like body or not. You could read Asimov, for one of the most famous examples exploring the idea, but lots of others were also shaping the terminology.
https://www.baen.com/artificial-intelligence
That usage and definition persisted unchanged until recently.
@SomeVeganCheeseIsOk
Intelligence is the ability to compress information, recognize patterns, and generalize solutions to new problems with high efficiency. It is a measure of functional competence, not subjective experience.
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