Machine learning has some pretty cool potential in certain areas, especially in the medical field. Unfortunately the predominant use of it now is slop produced by copyright laundering shoved down our throats by every techbro hoping they'll be the next big thing.
The term AI was coined in 1956 at a computer science conference and was used to refer to a broad range of topics that certainly would include machine learning and neural networks as used in large language models.
I don't get the "it's not really AI" point that keeps being brought up in discussions like this. Are you thinking of AGI, perhaps? That's the sci-fi "artificial person" variety, which LLMs aren't able to manage. But that's just a subset of AI.
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.
Hopefully not too pedantic, but no one is “teaching” AI anything. They’re just feeding it data in the hopes that it can learn probabilities for certain types of output. It “understands” neither the Reddit post nor the scientific paper.
Because AI needs a lot of training data to reliably generate something appropriate. It's easier to get millions of reddit posts than millions of research papers.
Even then, LLMs simply generate text but have no idea what the text means. It just knows those words have a high probability of matching the expected response. It doesn't check that what was generated is factual.
I find it amusing that everyone is answering the question with the assumption that the premise of OP's question is correct. You're all hallucinating the same way that an LLM would.
LLMs are rarely trained on a single source of data exclusively. All the big ones you find will have been trained on a huge dataset including Reddit, research papers, books, letters, government documents, Wikipedia, GitHub, and much more.
Ignore facts, don’t do research to see if the comment/post is correct, don’t look at other comments to see if anyone else has corrected the post/comment already, there is only one right side (and that is the side of the loudest group)
"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.
Why does everyone keep calling them Markov chains? They're missing all the required properties, including the eponymous Markovian property. Wouldn't it be more correct to call them stochastic processes?
Edit: Correction, turns out the only difference between a stochastic process and a Markov process is the Markovian property. It's literally defined as "stochastic process but Markovian".
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?
You should look closer who is making those claims that "AI" is an extinction threat to humanity. It isn't researchers that look into ethics and safety (not to be confused with "AI safety" as part of "Alignment"). It is the people building the models and investors. Why are they building and investing in things that would kill us?
AI doomers try to 1. Make "AI"/LLMs appear way more powerful than they actually are. 2. Distract from actual threats and issues with LLMs/"AI". Because they are societal, ethical, about copyright and how it is not a trustworthy system at all. Cause admitting to those makes it a really hard sell.
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.
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.
You could feed all the research papers in the world to an LLM and it will still have zero understanding of what you trained it on. It will still make shit up, it can't save the world.
Slightly longer answer: GPT models like ChatGPT are part of an experiment in "if we train the AI model on shedloads of data does it make a more powerful AI model?" and after OpenAI made such big waves every company is copying them including trying to train models similar to ChatGPT rather than trying to innovate and do more
Even longer answer: There's tons of different AI models out there for doing tons of different things. Just look at the over 1 million models on Hugging Face (a company which operates as a repository for AI models among other services) and look at all of the different types of models you can filter for on the left.
Training an image generation model on research papers probably would make it a lot worse at generating pictures of cats, but training a model that you want to either generate or process research papers on existing research papers would probably make a very high quality model for either goal.
More to your point, there's some neat very targeted models with smaller training sets out there like Microsoft's PHI-3 model which is primarily trained on textbooks
As for saving the world, I'm curious what you mean by that exactly? These generative text models are great at generating text similar to their training data, and summarization models are great at summarizing text. But ultimately AI isn't going to save the world. Once the current hype cycle dies down AI will be a better known and more widely used technology, but ultimately its just a tool in the toolbox.
also the answer to that question, shitloads of data for a better ai, is yes… with logarithmic returns. massively underpriced (by cost to generate) returns that have questionable value statement at best.
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.
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noun
(especially in prehistoric times) a person who lived in a cave.
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Papers are most importantly a documentation of exactly what and how a procedure was performed, adding a vagueness filter over that is only going to decrease its value infinitely.
Real question is why are we using generative ai at all (gets money out of idiot rich people)
Anyone running a webserver and looking at their logs will know AI is being trained on EVERYTHING. There are so many crawlers for AI that are literally ripping the internet wholesale. Reddit just got in on charging the AI companies for access to freely contributed content. For everyone else, they're just outright stealing it.
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...
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
Because that's what it's designed for? I'm curious what else it could be good for. A machine capable of independent, intelligent research sounds like a totally different invention entirely.
We are. I just read an article yesterday about how Microsoft paid research publishers so they could use the papers to train AI, with or without the consent of the papers' authors. The publishers also reduced the peer review window so they could publish papers faster and get more money from Microsoft. So... expect AI to be trained on a lot of sloppy, poorly-reviewed research papers because of corporate greed.
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.
The few I've seen weren't shining examples of the language, and could have used some editing.
As well, the rumours abound that a lot of papers are available before review, and that's likely to cause some harm if we trust a model predicting on bad data.
(Yes, I know: reddit isn't going to be better; but it has its own warning because, well, Reddit)
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.
I was thinking the sentence "We could be saving the world!" meant 'we' as in humans only.
No need to be training AI. No need to do anything with AI at all. Humans simply start saving the world. Our Research Papers can train on Reddit. We cannot be training, we are saving the world. Let the Research Papers run a train on Reddit AI. Humanity Saves World.