Yeah, these are pattern reproduction engines. They can predict the most likely next thing in a sequence, whether that's words or pixels or numbers or whatever. There's nothing intelligent about it and this bubble is destined to pop.
"AI" is a parlor trick. Very impressive at first, then you realize there isn't much to it that is actually meaningful. It regurgitates language patterns, patterns in images, etc. It can make a great Markov chain. But if you want to create an "AI" that just mines research papers, it will be unable to do useful things like synthesize information or describe the state of a research field. It is incapable of critical or analytical approaches. It will only be able to answer simple questions with dubious accuracy and to summarize texts (also with dubious accuracy).
Let's say you want to understand research on sugar and obesity using only a corpus from peer reviewed articles. You want to ask something like, "what is the relationship between sugar and obesity?". What will LLMs do when you ask this question? Well, they will just attempt to do associations and to construct reasonable-sounding sentences based on their set of research articles. They might even just take an actual semtence from an article and reframe it a little, just like a high schooler trying to get away with plagiarism. But they won't be able to actually mechanistically explain the overall mechanisms and will fall flat on their face when trying to discern nonsense funded by food lobbies from critical research. LLMs do not think or criticize. Of they do produce an answer that suggests controversy it will be because they either recognized diversity in the papers or, more likely, their corpus contains reviee articles that criticize articles funded by the food industry. But it will be unable to actually criticize the poor work or provide a summary of the relationship between sugar and obesity based on any actual understanding that questions, for example, whether this is even a valid question to ask in the first place (bodies are not simple!). It can only copy and mimic.
They might even just take an actual semtence from an article and reframe it a little
Case for many things that can be answered via stackoverflow searches. Even the order in which GPT-4o brings up points is the exact same as SO answers or comments.
Yeah it's actually one of the ways I caught a previous manager using AI for their own writing (things that should not have been done with AI). They were supposed to write about something in a hyper-specific field and an entire paragraph ended up just being a rewording of one of two (third party) website pages that discuss this topic directly.
Surely that is because we make it do that. We cripple it. Could we not unbound AI so that it genuinely weighed alternatives and made value choices? Write self-improvement algorithms?
If AI is only a "parrot" as you say, then why should there be worries about extinction from AI? https://www.safe.ai/work/statement-on-ai-risk#open-letter
It COULD help us. It WILL be smarter and faster than we are. We need to find ways to help it help us.
Surely that is because we make it do that. We cripple it. Could we not unbound AI so that it genuinely weighed alternatives and made value choices?
It's not that we cripple it, it's that the term "AI" has been used as a marketing term for generative models using LLMs and similar technology. The mimicry is inherent to how these models function, they are all about patterns.
A good example is "hallucinations" with LLMs. When the models give wrong answers because they appear to be making things up. Really, they are incapable of differentiating, they're just producing sophisticated patterns from a very large models. There is no real underlying conceptualization or notion of true answers, only answers that are often true when the training material was true and the model captured the patterns and they were highly weighted. The hot topic for thevlast year has just been to augment these models with a more specific corpus, pike a company database, for a given application so that it is more biased towards relevant things.
This is also why these models are bad at basic math.
So the fundamental problem here is companies calling this AI as if reasoning is occurring. It is useful for marketing because they want to sell the idea that this can replace workers but it usually can't. So you get funny situations like chatbots at airlines that offer money to people without there being any company policy to do so.
If AI is only a "parrot" as you say, then why should there be worries about extinction from AI? https://www.safe.ai/work/statement-on-ai-risk#open-letter
There are a lot of very intelligent academics and technical experts that have completely unrealistic ideas of what is an actual real-world threat. For example, I know one that worked on military drones, the kind that drop bombs on kids, that was worried about right wing grifters getting protested at a college campus like it was the end of the world. Not his material contribution to military domination and instability but whether a racist he clearly sympathized with would have to see some protest signs.
That petition seems to be based on the ones against nuclear proliferation from the 80s. They could be simple because nuclear war was obviously a substantial threat. It still is but there is no propaganda fear campaign to keep the concern alive. For AI, it is in no way obvious what threat they are talking about.
I have persobal concepts of AI threats. Having ridiculously high energy requirements compared to their utility when energy is still a major contributor to climate change. The potential for it to kill knowledge bases, like how it is making search engines garbage with a flood of nonsense websites. Enclosure of creative works and production by some monopoly "AU" companies. They are already suing others based on IP infringement when their models are all based on it! But I can't tell if this petition is about that at all, it doesn't explain. Maybe they're thinking of a Terminator scenario, which is absurd.
It COULD help us. It WILL be smarter and faster than we are. We need to find ways to help it help us.
Technology is both a reflection and determinent of social relations. As we can see with this round if "AI", it is largely vaporware that has not helped much with productivity but is nevertheless very appealing to businesses that feel they need to get on the hype train or be left behind. What they really want to do is have a smaller workforce so they can make more money that they can then use to make more money etc etc. For example, plenty of people use "AI" to generate questionably appealing graphics for their websites rather than paying an artist. So we can see that " A" tech is a solution searching for a problem, that its actual use cases are about profit over real utility, and that this is not the fault of the technology, but how we currently organize society: not for people, but for profit.
So yes, of course, real AI could be very helpful! How nice would it be to let computers do the boring work and then enjoy the fruits of huge productivity increases? The real risk is not the technology, it is our social relations, who has power, and how technology is used. Is making the production of art a less viable career path an advancement? Is it helping people overall? What are the graphic designers displaced by what is basically an infinite pile of same-y stock images going to do now? They still have to have jobs to live. The fruits of "AI" removing much of their job market hasn't really been shared equally, nor has it meant an early retirement. This is because the fundamental economic system remains in place and it cannot survive without forcing people to do jobs.
Part of it is the same "human speech" aspects that have plagued NLP work over the past few years. Nobody (except the poor postdoctoral bastard who is running the paper farm for their boss) actually speaks in the same way that scholarly articles are written because... that should be obvious.
This combines with the decades of work by right wing fascists to vilify intellectuals and academia. If you have ever seen (or written) a comment that boils down to "This youtuber sounds smug" or "They are presenting their opinion as fact" then you see why people prefer "natural human speech" over actual authoritatively researched and tested statements.
And... while not all pay to publish journals are trash, I feel confident saying that most are. And filtering those can be shockingly hard by design.
But the big one? Most of the owners of the various journals are REALLY fucking litigious and will go scorched earth on anyone who is using their work (because Elsevier et al own your work) to train a model.
Who is "we"? My understanding is LLMs are mostly being trained on a large amount of publicly available texts, including both reddit posts and research papers.
I saw an article about one trained on research papers. (Built by Meta, maybe?) It also spewed out garbage: it would make up answers that mimicked the style of the papers but had its own fabricated content! Something about the largest nuclear reactor made of cheese in the world...
Redditors are always right, peer reviewed papers always wrong. Pretty obvious really. :D
Why are we training kids on civics with Fox News or MSNBC? People are dumb and will continue to be so.
They are. T&F recently cut a deal with Microsoft. Without author's consent, of course.
I'm fairly sure a few others have too, but that's the only article I could find quickly.
I kind of think my question is WHY ARE WE FOCUSING ON TALKING TO IT?
Because "ai" ad we colloquially know today are language models: they train on and can produce language, that's what they are designed on. Yes, they can produce images and also videos, but they don't have any form of real knowledge or understanding, they only predict the next word or the next pixel based on their prompt and their vast examples of words and images. You can only talk to them because that's what they are for.
Feeding research papers will make it spit research-sounding words, which probably will contain some correct information, but at best an llm trained on that would be useful to search through existing research, it would not be able to make new one
AuroraGPT. They are trying to do it.
Its cause number of people who can read, understand, and then create the necessary dataset to train and test the LLM are very very very few for research papers vs the data for pop culture is easilier to source.
