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.
@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 @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.
@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
Let me clarify: "AI" is an unbounded marketing term with wildly ambitious, ambiguous definitions. No machine performs contextual reasoning like a human at this time, but that's the implication of "AI" to most folks. Machine Learning is a bounded, defined, useful term which talks about compute systems with specific capabilities.
@SomeVeganCheeseIsOk
But that's not precise nor clear at all. You are mixing tech itself with I don't know what that is, some kind of preaching? It sure sounds like you found your external enemy you can pin everything on and hate together with a group. Like a mob or a cult
@SomeVeganCheeseIsOk
It's not a marketing term. It's an old term that people have been using for a while. And I don't think it's ever been used to describe a machine person that's alive. It was always used as simulated intelligence. Which LLMs are. It's a very good description.
@emilymbender great thread, I think at different points I've run into each of the points you laid out (especially when I was trying to be more amenable to LLM users). since, I've grown to taking a much more hard-line anti-big-tech-LLM, neutral-to-meh academic/open-model LLM, and recommending searching out all alternatives before turning to the tech as a solution.
thank you for your work in this area, as you and @alex have helped shaped my opinions/knowledge on the subject.
i despise AI because of its energy waste, its abuse of source material it rips off without attribution, and how it concentrates power with these amoral techbros who can use it for all sorts of malicious goals
but i always wondered if AI could be a standalone thing, something you ran yourself, at home, so there is no exposure to manipulative outside agendas, if i would have the same objections. assuming sane power usage and respect for creators. and assuming complete privacy
It's a start. Once Nvidia no longer has the techbros sucking on its tit, they'll be begging for folks to buy their GPUs.
@ryanprior @benroyce @hackersquirrel @emilymbender
Yes, and they share most of the harms of cloud models anyway.
@benroyce @hackersquirrel @emilymbender IME you need substantial computer muscle for it to run models large enough to be useful. Not that it can't be done, not at all, but it's more accessible if you already have a powerful rig for other reasons.
And then, it doesn't necessarily improve some of the issues. Since models need lots of ram, it's actually less useful to fill, say 32 gigs of ram to serve one user, times 10000 users, than it is to have 320 terrabytes of ram serving 10000 users.
@renardboy @hackersquirrel @emilymbender
well distant future
like we walk around with computers in our pockets that are 10,000x what we used to send astronauts to the moon. in 50 years, we'll walk around with petabytes and petaflops in our pockets (or stuck in our heads... cyborgs)
@benroyce @hackersquirrel @emilymbender Personally, my hope for better generative "AI" involves distributed models running on home servers that channel their heat usefully.
You'd have a bunch of servers with water cooling that feeds into appliances like hot water heaters, and each would hold some focused fragment of a large language model, and when you'd prompt you'd have some kind of router language model that'd send it to whoever is likely to have an answer.
@benroyce @hackersquirrel @emilymbender I don't dare to expect this but I allow myself to hope.
It's not that far-fetched as I understand, there are existing LLMs that are internally structured as components, they are called "Mixture-of-Experts" (MoE) LLMs.
@renardboy @hackersquirrel @emilymbender
AI as water heaters is very Matrixy. I like it
@renardboy @benroyce @hackersquirrel CPUs and GPUs slow down or even shut down and consume far less energy when they’re not actively crunching numbers.
E.g. a phone or tablet may have peak performance numbers surprisingly competitive with a desktop, yet the battery could last multiple days if left idle.
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