I recently got into a debate with a friend who swears is “Against AI Slop”, but uses AI for 90% of his researching stuff. When i point it out, i got a 20 minute rant about how AI helps him learn and makes him learn faster, but i see massive issues with this. For once, using AI even as a starting point is a gamble, if not a straight damage to the learning process as a whole, mainly because sloppified information spit out by an LLM is often simplified, imprecise and oversimplified. For example, he told me to be interested in Arch Linux, but said the Arch Wiki was too “difficult to understand and took knowledge for granted” and told me that AI helped him understanding it. However i think that the wiki being verbose and designed to not be understandable by everybody is a filter to make sure that you don’t go and use Arch if you can’t understand the wiki, not for gatekeeping, but because a sloppified and short version of processes explained in the wiki will result in you knowing WHAT to do, but often not HOW and WHY. Also, it keeps you locked to AI since if you get used to the simplification (and often misinformation) given by the slop generator, it’ll be hard to understand the real sources, and it’ll be hard to learn the jargon and will lead to an incomplete result. I could go on for days and feel free to debate me, but I am tired of pretending that AI is a great learning tool. It can be, but in its current status it’s just not, and when it can be, IMO the other cons that come with AI usage outweigh the possible value of the info.
I feel AI definitely has a place in more specific fields of learning.
For instance, I’ve played with a local model and used it as a more intelligent search for an hours-long D&D audio transcript, I could get useful shortcuts to things like “make a list of items the group used today, and include an exact quote when it was used”, or “Here’s this name, find every word in the log that could be that name spelled or transcribed incorrectly”. The second is very handy when there’s names like Vlaxor and the transcript includes things like “flax ore” and “blaster”.
It really is a decent unpaid intern. It’s just unpaid interns need specific instruction and need to be checked, but people treat it like they’re experts. Your friend could also get a similar understanding in beginner forums, or, if it’s a common issue, even YouTube videos going over it, but posting on a forum isn’t as convenient as asking the unpaid intern who cannot think less of your skills as a Linux user.
It doesn’t do you as the user any favours so it’s definitely wrong in that sense. It’s bad for the environment too. People will still do it because it was pushed on them hard by universities and employers; it’ll take more time to unlearn
Not sure why is that a “hot” take. It’s obvious.
A learning foundation of a human is crucial. It must be supervised by another human or originate from a human… A human who realizes the effort, empathy, pain, value, experience, love for discoveries, academical or not, and from one who realized the information passed for another human, improving it together in alive, respectful, careful, manner for the authors, artists, people, with names who ask, refer to, quote for, to rely on, to trust and discuss with together realizing it all ineffably magnificent…
ML/LLM/AI “knowledge” is limited, indeterministic, unverified, unstable, and no one is irresponsible for it, and never will be.
This is a reckless, lonely, sorrowful, self-damaging experience… that no one alive must experience.
It is a path of degradation, atrophy, and disrespect towards anyone involved.Apparently it’s not so obvious, way too many people were against me when i originally had that debate
AI and troll farms and bots, Ooh MY!
Unfortunately it was real life :(
All systems based on LLM, ML, or anything that is algorithmically based on feedback system, learning, training, is indeterministic, and hence, always inaccurate, unreliable, and irresponsible systems, by design.
That is, it may be fun to play with as a toy, as any ML/LLM/AI based system, where no trust exist but only probability, entropy, chance, luck… but nothing serious, time-rewarding, or humane enough to appreciate and love it in the long run.
These are impossible to rely on, and absolutely not for education, for learning, for human mind foundational processes, for any kind of mind and actual knowledge development you care about and want to actually realize, remember, experience, to learn and carry with you for the infinite future…
These people you describe don’t have children, I believe.
They come from infants, immature, children, too, sorry… from those who could not care less about the history, responsibility, human being a miracle of unique and incredible…
They don’t care about the overall process of the life of us, people, to learn, realize, create, to find solutions, to make mistakes and learn from it, to reach the goals, to treasure achievements, to write our history with actual names of people on papers signed by hand or not but still referred to people who experienced, and passed it further with hope that is a miracle…
They do not care about what will leave after us, the effort, human realized knowledge, the art, the statues, the shapes/sayings/quotes/ideas… in the infinite history of us, and the ineffably magnificent world…
The older I get the more I realize most people don’t actually care about how or why anything happens
This is my SO, they are so against learning anything in STEM or Philosophy that their eyes glaze over with even the slightest mention of anything related. They don’t want to know how anything works, they just care it does.
I agree LLMs are not easy to learn from, though it can happen sometimes. I actually find though that the problem is usually the incredibly verbose but hard to absorb writing style that comes back. My (software dev) workplace has been insisting we use Claude so I’ve been trying it. Everything it does, it writes 10 paragraphs explaining. I try to read it and my eyes glaze over. Then of course it makes many mistakes and I think that’s also part of my issue. I become demotivated in playing the game of “sift through all this bullshit to find how it might be wrong”. It’s exhausting.
From first person experience, both with software developers and business users: it’s fucking up their brains.
It’s making them less competent software designers and basic programmers, it’s launching a thousand careers where their only skills are copying and pasting LLM output, and it’s making them hyperconfident while actually rotting their fucking brains.
I think users of LLMs often suffer from a sort of Gell-Mann amnesia phenomenon. If you are well versed on a topic, you can often find the mistakes they make; but, when researching a topic you are unfamiliar with, the responses feel like the “AI” knows what it’s talking about.
I agree and disagree.
I agree in part because yes, learning requires effort. Subjecting yourself to the instant gratification machine won’t do you any favors when it comes to learning. The only thing you’ll learn is that there’s an instant gratification machine ready to help you anytime.
I disagree in part because I think anything that helps Linux adoption is good in my eyes. Linux doesn’t need any ”filters”. Not everyone has English as their first language for example. Should they be closed off access to the wealth of information in Arch Wiki?
To be fair, i think the language is a non-issue. sure, not everyone speaks english as a first language, but many people speak it as a second and the Arch Wiki specifically but also others are indeed translated in other languages.
You and I both want people to learn. You and I both see AI as an obstacle to learning. We just see things slightly differently.
LLMs are bad for education because they oversimplify.
I actually think this is not the main reason AI is awful for learning. The gravest problem, in my opinion, is the illusion of competence or the illusion of learning that it brings. This phenomenon is very well documented in the learning literature, well before LLMs were a thing. But LLMs amplify it.
Just take a look at PISA results. Students do poorly because they use LLMs and they never properly understood the content.
However, the education literature also paints a clear picture: after someone learns the basics, they’re ready for other kinds of activities. In fact, if someone already understood thing and yet they still go over the same thing over and over, they stop learning. This is called the expertise reversal effect. So the alternative is to move on to other stuff.
I’m not arguing for having AI in the classroom. I’m just thinking of the implications of a well-researched theory: cognitive load theory. From that point of view, students that already learned the basics can then look at LLM slop and see its deficiencies. They can now plan an activity with an LLM and see how it nails some stuff and falls apart in other stuff. In that case, and only in that case, LLMs can improve learning in the same way that looking at your friend’s essays can improve yours.
But, again, I’m not arguing for AI in the classroom. I think the world would be better off with a blanket AI ban in education. I think this is similar to how nicotine actually has some positive effects, but the bad outweighs the good by so much that it’s much better to ban it.
Having a verbose wiki that is incomprehensible to most people is good because it filters out people who don’t understand
Maybe.
I guess here I ask: what’s the goal? What do we want more of? What do we want less of?
If our only policy is “out wiki should be incomprehensible to most*”, I’m not sure we’ll have more people in the world who understand the wiki. If, instead, we think “our wiki should be as accessible as possible”, then we may find ways of teaching people.
- Edit: better characterize JettuhGlowie’s stance
incomprehensible wiki = good
i never said that, maybe i worded it poorly. What I’m saying is that simplifying a wiki article is detrimental to learning because it paints a wrong image. If i get a slop answer to a question about a wiki article I’m effectively avoiding the learning process that’d lead to me being more capable to maintain my system more effectively, not to mention that it teaches me to seek answers instead of taking the quick, risky and immoral shortcut. Sure, i guess AI can make it easier to understand, but these hobbyist pieces of software are meant to be used carefully by people who are willing to spend time to use them. If you don’t want to read the wiki, It’s okay, use something else, but using AI will eventually lead to issues.
As for the rest of what you said (my bad if i misunderstood), i think that if the only useful use of AI in education comes AFTER learning the basics, it is essentially just useless, since if you manage to know the basics you are clearly able to go further
Relying purely on the knowledge from an LLMs dataset is never a good idea. Frontier models at least append their data by popular demand. Google used to do this back in the day. If a search used to show no results they would note that and start searching their indexed sites for the unresolved search. Maybe a week or so later you could try again and go Google would finally come up with something. Same thing happens with LMMs now. If a question or prompt is popular enough they scrub their indexed sites with more focus and refine the dataset to give more accurate responses. But more accurate is still not 100% reliable. Open weight models usually have Datasets up until 2020, since filtering out AI slop from current day data is a science in itself and smaller labs and institutes don’t have the rescources for that.
But where LLMs shine is in the fact that they can understand natural language and use tools. For example if I where to research a topic I’d go through hours of web searches, clicking all the links, reading all the abstracts etc. Why not just have an LLM with a websearch access do some agentic work. You give it relevant search terms, and it will go through several pages of the Google results, create summaries and provide the sources directly.
Add some MCP servers to that so LLMs can directly interact and navigate websites and it will massively speed up research. Instead of reading through several pages of a paper to figure out it is only vaguely related to your topic turns from 10-20 minutes to 1-2 minutes.
For example Qwen Modells can easily handle 300 page PDF documents without hallucinations.
And once you’ve got enough material, then you can go back on the sources and properly read through it yourself.LLMs can very much be helpful for research, if you use them properly for what they where designed to do, namely data analysis, instead of some know-it-all Guru.
But AI is good at suggesting the direction: correct names of the relevant scientific field, names of algorithms, names of theorems, or names of certain classes. Then you can later find and open the right book and the right section. Some times it happens that you don’t really understand the topic yourself, and it’s hard for you even to formulate the topic correctly and properly to find the right section or topic. This still speeds up the search for the needed infdormation. But I myself try to minimize the use of AI, but sometimes I use local AI exactly in the above-mentioned cases and for the purpose of learning|studying something.
