I’d argue that making students give generic regurgitated info as an assignment is the actual issue. Make a good assignment…
In my experiments with LLMs for writing code, I find that the code is objectively garbage if my prompt is garbage. If I don't know what I want, if I don't have any ideas, and I don't have a structure or plan, that's the sort of code I get out.
I'd love to hear any counterpoints from folks who have used LLMs lately to get academic or creative writing done, as I haven't tried using any models lately for anything beyond helping me punch through boilerplate/scaffolding on personal programming projects.
https://old.reddit.com/r/singularity/comments/1andqk8/gemini...
As a side note, I find the way that you interact with a LLM when doing creative writing is generally more important than the model. I have been having great results with LLMs for creative writing since ChatGPT 3.5, in part because I approach the model with a nucleus of a chapter and a concise summary of relevant details, then have it ask me a long list of questions to flesh out details, then when the questions stop being relevant I have have it create a narrative outline or rough draft which I can finish.
I pointed this out a few weeks ago with respect to why the current state of LLMs will never make great campaign creators in Dungeons and Dragons.
We as humans don't need to be "constrained" - ask any competent writer to sit quietly and come up with a novel story plot and they can just do it.
https://news.ycombinator.com/item?id=43677863
That being said - they can still make AMAZING soundboards.
And if you still need some proof, crank the temperature up to 1.0 and pose the following prompt to ANY LLM:
Come up with a self-contained single room of a dungeon that involves an
unusual puzzle for use with a DND campaign. Be specific in terms of the
puzzle, the solution, layout of the dungeon room, etc. It should be totally
different from anything that already exists. Be imaginative.
The output is pretty non-sensical: https://pastebin.com/raw/hetAvjSG
Haha, I was suspicious, so I tried this, and I indeed got an hourglass themed puzzle! Though it wasn't physics-based - characters were supposed to share memories to evoke emotions, and different emotions would ring different bells, and then you were supposed to evoke a certain type of story. Honestly, I don't know what the hourglass had to do with it.
I commented in another thread. We're using image and video diffusion models for creative:
https://www.youtube.com/watch?v=H4NFXGMuwpY
Still not a fan of LLMs.
I personally tend not to use AI this way. When it comes to writing, that's actually the exact inverse of how I most often use AI, which is to throw a ton of information at it in a large prompt, and/or use a preexisting chat with substantial relevant context, possibly have it perform some relevant searches and/or calculations, and then iterate on that over successive prompts before landing on a version that's close enough to what I want for me to touch up by hand. Of course the end result is clearly shaped by my original thoughts, with the writing being a mix of my own words and a reasonable approximation of what I might have written by hand anyway given more time allocated to the task, and not clearly identifiable as AI-assisted. When working with AI this way, asking to "read the prompt" instead of my final output is obviously a little ridiculous; you might as well also ask to read my browser history, some sort of transcript of my mental stream of consciousness, and whatever notes I might have scribbled down at any point.
Fairly or unfairly, people (including you) will inexorably come to see anything done with AI as ONLY done with AI, and automatically assume that anyone could have done it.
In such a world, someone could write the next Harry Potter and it will be lost in a sea of one million mediocre works that roughly similar. Hidden in plain sight forever. There would no point in reading it, because it is probably the same slop I could get by writing a one paragraph prompt. It would be too expensive to discover otherwise.
I'm expanding on the author's point that the hard part is the input, not the output. Sure someone else could produce the same output as an LLM given the same input and sufficient time, but they don't have the same input. The author is saying "well then just show me the input"; my counterpoint is that the input can often be vastly longer and less organized or cohesive than the output, and thus less useful to share.
To be fair, the first Harry Potter is a kinda average British boarding school story. Rowling is barely an adequate writer (and it shows badly in some of the later books). There was a reason she got rejected by so many publishers.
However, Netscape was going nuts and the Internet was taking off. Anime was going nuts and produced some of the all time best anime. MTV animation went from Beavis and Butthead to Daria in this time frame. Authors were engaging with audiences on Usenet (see: Wheel of Time and Babylon 5). Fantasy had moved from counterculture for hardcore nerd boys to something that the bookish female nerds would engage with.
Harry Potter dropped onto that tinder and absolutely caught fire.
The world-building is meh at best. The magic system is perfunctory. But the characters are strong and the plot is interesting from beginning to end.
It sounds to me that you don't make the effort to absorb the information. You cherry-pick stuff that pops in your head or that you find online, throw that into an LLM and let it convince you that it created something sound.
To me it confirms what the article says: it's not worth reading what you produce this way. I am not interested in that eloquent text that your LLM produced (and that you modify just enough to feel good saying it's your work); it won't bring me anything I couldn't get by quickly thinking about it or quickly making a web search. I don't need to talk to you, you are not interesting.
But if you spend the time to actually absorb that information, realise that you need to read even more, actually make your own opinion and get to a point where we could have an actual discussion about that topic, then I'm interested. An LLM will not get you there, and getting there is not done in 2 minutes. That's precisely why it is interesting.
Synthesizing large amounts of information into smaller more focused outputs is something LLMs happen to excel at. Doing the exact same work more slowly by hand just to prove a point to someone on HN isn't a productive way to deliver business value.
You prove my point again: it's not "just to prove a point". It's about internalising the information, improving your ability to synthesise and be critical.
Sure, if your only objective is to "deliver business value", maybe you make more money by being uninteresting with an LLM. My point is that if you get good at doing all that without an LLM, then you become a more interesting person. You will be able to have an actual discussion with a real human and be interesting.
We were talking about writing, not about vibe coding. We don't use calculators for writing. We don't use API requests for writing (except when we make an LLM write for us).
> Using the right tool for the job is just doing my job well.
I don't know what your job is. But if your job is to produce text that is meant to be read by humans, then it feels like not being able to synthesise your ideas yourself doesn't make you excellent at doing your job.
Again maybe it makes you productive. Many developers, for instance, get paid for writing bad code (either because those who pay don't care about quality or can't make a difference, or something else). Vibe coding obviously makes those developers more productive. But I don't believe it will make them learn how to produce good code. Good for them if they make money like this, of course.
No one said anything about vibe coding. Using tools appropriately to accomplish tasks more quickly is just common sense. Deliberately choosing to pay 10x the cost for the same or equivalent output isn't a rational business decision, regardless of whether the task happens to be writing, long division, or anything else.
Just to be clear, I'm not arguing against doing things manually as a learning exercise or creative outlet. Sometimes the journey is the point; sometimes the destination is the point. Both are valid.
I don't know what your job is.
Here's one: prepping first drafts of legal docs with AI assistance before handing them off to lawyers for revision has objectively saved significant amounts of time and money. Without AI this would have been too time-consuming to be worthwhile, but with AI I've saved not only my own time but the costs of billable hours on phone calls to discuss requirements, lawyers writing first drafts on their own, and additional Q&A and revisions over email. Using AI makes it practical to skip the first two parts and cut down on the third significantly.
Here's another one: doing security audits of customer code bases for a company that currently advertises its use of AI as a cost-saving/productivity-enhancing mechanism. Before they'd integrated AI into their platform, I would frequently get rave reviews for the quality and professionalism of my issue reports. After they added AI writing assistance, nothing changed other than my ability to generate a greater number of reports in the same number of billable hours. What you're suggesting effectively amounts to choosing to deliver less value out of ego. I still have to understand my own work product, or I wouldn't be able to produce it even with AI assistance. If someone thinks that somehow makes the product less "interesting", well then I guess it's a good thing my job isn't entertainment.
For the structure, they are barely useful: Writing is about having such a clear understanding, that the meaning remains when reduced to words, so that others may grasp it. The LLM won't help much with that, as you say yourself.
LLMs may seem like magic buy they aren't. They operate within the confines of the context they're given. The more abstract the context, the more abstract the results.
I expect to need to give a model at least as much context as a decent intern would require.
Often asking the model "what information could I provide to help you produce better code" and then providing said information leads to vastly improved responses. Claude 3.7 sonnet in Cline is fairly decent at asking for this itself in plan mode.
More and more I find that context engineering is the most important aspect of prompt engineering.
They’re great at proofreading. They’re also good at writing conclusions and abstracts for articles, which is basically synthesising the results of the article and making it sexy (a task most scientists are hopelessly terrible at). With caveats:
- all the information needs to be in the prompt, or they will hallucinate;
- the result is not good enough to submit without some re-writing, but more than enough to get started and iterate instead of staring at a blank screen.
I want to use them to write methods sections, because that is basically the exact same information repeated in every article, but the actual sentences need to be different each time. But so far I don’t trust them to be accurate with technical details. They’re language models, they have no knowledge or understanding.
Really? The example used was for a school test. Is there really much original thought in the answer? Do you really want to read the students original thought?
I think the answer is no in this case. The point of the test is to assess whether the student has learned the topic or not. It isn’t meant to share actual creative thoughts.
Of course, using AI to write the answer is contrary to the actual purpose, too, but it isn’t because you want to hear the students creativity, but because it is failing to serve its purpose as a demonstration of knowledge.
Why else would you become a teacher, if you didn't care about what your students think?
Perhaps the problem is that they are "graded", but this is to motivate the student, and runs against the age-old problem of gamification.
Arguably, that's not what teachers mainly do (to an ever increasing proportion).
Most knowledge is easily available. A teacher is teaching students to think in productive ways, communicate their thoughts and understand what others are trying to tell them. For this task, it's essential that the teacher has some idea what the students are thinking, especially when it's something original.
As long as LLM output is what it is, there is little threat of it actually being competitive on assignments. If students are attentive enough to paraphrase it into their own voice I'd call it a win; if they just submit the crap that some data labeling outsourcer has RLHF'd into a LLM, I'd just mark it zero.
I would have thought that giving 0s to correct solutions would lead to successful complaints/appeals.
If you’re not willing to cross out an entire assignment and return it to the student who handed it in with “ChatGPT nonsense, 0” written in big red letters at the top of it, you should ask yourself what is the point of your assignments in the first place.
But I get it, university has become a pay-to-win-a-degree scheme for students, and professors have become powerless to enforce any standards or discipline in the face of administrators.
So all they can do is give the ChatGPT BS the minimum passing grade and then philosophize about it on their blog (which the students will never read).
The world will be consumed by AI.
Once upon a time only the brightest (and / or richest) went to college. So a college degree becomes a proxy for clever.
Now since college graduates get the good jobs, the way to give everyone a good job is to give everyone a degree.
And since most people are only interested in the job, not the learning that underpins the degree, well, you get a bunch of students that care only for the pass mark and the certificate at the end.
When people are only there to play the game, then you can't expect them to learn.
However, while 90% will miss the opportunity right there in front of them, 10% will grab it and suck the marrow. If you are in college I recommend you take advantage of the chance to interact with the knowledge on offer. College may be offered to all, but only a lucky few see the gold on offer, and really learn.
That's the thing about the game. It's not just about the final score. There's so much more on offer.
This is because that is what companies care about. It's not a proxy for cleverness or intelligence - it's a box to check.
Learning is not just a function of aptitude and/or effort. Interest is a huge factor as well, and even for a single person, what they find interesting changes over time.
I don't think it's really possible to have a large cohort of people pass thru a liberal arts education, with everyone learning the same stuff at the same time, and have a majority of them "suck the marrow" out of the opportunity.
Then fail to actually learn anything and apply for jobs and try to cheat the interviewers using the same AI that helped them graduate. I fear that LLMs have already fostered the first batch of developers who cannot function without it. I don't even mind that you use an LLM for parts of your job, but you need to be able to function without it. Not all data is allowed to go into an AI prompt, some problems aren't solvable with the LLMs and you're not building your own skills if you rely on generated code/configuration for the simpler issues.
Playing the contrarian here, but I'm from a batch of developers that can't function without a compiler, and I'm at 10% of what I can do without an IDE and static analysis.
Sure, there's a huge jump from a line editor like `ed` to a screen editor like `vi` or `emacs`, but from there on, it was diminishing returns really (a good debugger was usually the biggest benefit next) — I've also had the "pleasure" of having to use `echo`, `cat` and `sed` to edit complex code in a restricted, embedded environment, and while it made iterations slower, not that much more slower than if I had a full IDE at my disposal.
In general, if I am in a good mood (and thus not annoyed at having to do so many things "manually"), I am probably only 20% slower than with my fully configured IDE at coding things up, which translates to less than 5% of slow down on actually delivering the thing I am working on.
Same with more advanced editors and IDEs. They help with tediousness, which can hinders insight, but does not help it if you do not have the foundation.
A compiler translates _what you have already implemented_ into another computer runnable language. There is an actual grammar that defines the rules. It does not generate new business logic or assumptions. You have already done the work and taken all the decisions that needed critical thought, it's just being translated _instruction by instruction_. (btw you should check how compilers work, it's fun)
Using an LLM is more akin to copying from Stackoverflow than using a compiler/transpiler.
In the same way, I see org charts that put developers above AI managers, which are above AI developers. This is just smoke. You can't have LLMs generating thousands of lines of code independently. Unless you want a dumpster fire very quickly...
That is, the job of a professional programmer includes having produced code that they understand the behavior of. Otherwise you’ve failed to do your due diligence.
If people are using LLMs to generate code, and then actually doing the work of understanding how that code works… that’s fine! Who cares!
If people are just vibe coding and pushing the results to customers without understanding it—they are wildly unethical and irresponsible. (People have been doing this for decades, they didn’t have the AI to optimize the situation, but they managed to do it by copy-pasting from stack overflow).
I have met maybe two people who truly understood the behaviour of their code and both employed formal methods. Everyone else, including myself, are at varying levels of confusion.
(Yes, these are people with developer jobs, often at "serious" companies.)
Maybe you mean people who are bad at interviews? Or people whose job isn't actually programming? Or maybe "lots" means "at least one"? Or maybe they can strictly speaking do fizzbuzz, but are "in any case bad programmers"? If your claim is true, what do these people do all day (or, let's say, did before LLMs were a thing...)?
I’ve met some really terrible programmers, and some programmers who freeze during interviews.
I don't. I think the world is falling into two camps with these tools and models.
> I now circle back to my main point: I have never seen any form of create generative model output (be that image, text, audio, or video) which I would rather see than the original prompt. The resulting output has less substance than the prompt and lacks any human vision in its creation. The whole point of making creative work is to share one’s own experience
Strong disagree with Clayton's conclusion.
We just made this with AI, and I'm pretty sure you don't want to see the raw inputs unless you're a creator:
https://www.youtube.com/watch?v=H4NFXGMuwpY
I think the world will be segregated into two types of AI user:
- Those that use the AI as a complete end-to-end tool
- Those that leverage the AI as tool for their own creativity and workflows, that use it to enhance the work they already do
The latter is absolutely a great use case for AI.
"Tall man, armor that is robotic and mechanical in appearance, NFL logo on chest, blue legs".,
And so on, embedded in node wiring diagrams to fiddly configs and specialized models for bespoke purposes, "camera" movements, etc.
Seeing this non-compelling prompt would tell me right off the bat that I wouldn't be interested in the video either.
I am not a creator but I am interested in generative AI capabilities and their limits, and I even suffered through the entire video which tries to be funny, but really isn't (and it'd be easier to skim through as a script than the full video).
So even in this case, I would be more interested in the prompt than in this video.
The video is not exactly great, IMO.
Because those who recruit based on the degree aren't worth more than those who get a degree by using LLMs.
Maybe it will force a big change in the way students are graded. Maybe, after they have handed in their essay, the teacher should just have a discussion about it, to see how much they actually absorbed from the topic.
Or not, and LLMs will just make everything worse. That's more likely IMO.
Yes I know the subject area for which I write assessments and know if what is generated is factually correct. If I’m not sure, I ask for web references using the web search tool.
https://chatgpt.com/share/6817c46d-0728-8010-a83d-609fe547c1...
> I didn’t realize how much that could throw things off until I saw an example where the object started moving in a strange way when it hit that point.
Would feel off, because why change the person? And even if it's intented, then I'd say it's not formal to do in an assignement.
I use to work at AWS (Professional Services) and there are a few different writing styles depending on what your audience was. I learned how to write in the different “house styles” before LLMs were a thing. So I know when something doesn’t sound right.
I use LLMs all of the time to write. I’m 99% certain that no one can tell the difference between my writing 100% without an LLM to my writing with one
Maybe the problem is that the professor doesn't want to read the student work anyway, since it's all stuff he already knows. If they managed to use their prompts to generate interesting things, he'd stop wanting to see the prompts.
No, this is just the de-facto "house style" of ChatGPT / GPT models, in much the same way that that that particular Thomas Kinkade-like style is the de-facto "house style" of Stable Diffusion models.
You can very easily tell an LLM in your prompt to respond using a different style. (Or you can set it up to do so by telling it that it "is" or "is roleplaying" a specific type-of-person — e.g. an OP-ED writer for the New York Times, a textbook author, etc.)
People just don't ever bother to do this.
https://chatgpt.com/share/6817c9f4-ed48-8010-bc3e-58299140c8...
In the real world I would at least remove the em dashes. It’s a dead give away for LLM generated text.
You can't just say "don't sound like an LLM." The LLM does not in fact know that it is "speaking like an LLM"; it just thinks that it's speaking the way the "average person" speaks, according to everything it's ever been shown. If you told it "just speak like a human being"... that's what it already thought it was doing!
You have to tell the LLM a specific way to speak. Like directing an image generator to use a specific visual style.
You can say "ape the style of [some person who has a lot of public writing in the base model's web training corpus — Paul Graham, maybe?]". But that coverage will be spotty, and it's also questionably ethical (just like style-aping in image generation.)
But an LLM will do even better if you tell it to speak the in some "common mode" of speech: e.g. "an email from HR", or "a shitpost rant on Reddit" or "an article in a pop-science magazine."
I can’t imagine disincentivising actually getting stuck into programming and incentivising being good at regurgitating info in an exam room being a good thing for CS students.
1. Take home projects where we programmed solutions to big problems. 2. Tests where we had to write programs in the exam on paper during the test.
I think the take home projects are likely a lot harder to grade without AI being used. I'd be disappointed if schools have stopped doing the programming live during tests though. Being able to write a program in a time constrained environment is similar to interviewing, and requires knowledge of the language and being able to code algorithms. It also forces you to think through the program and detect if there will be bugs, without being able to actually run the program (great practice for debugging).
Those classes are what taught me how to study and really internalize the material. Helped me so much later in college too. I really can't imagine how kids these days are doing it.
I genuinely believe I had many excellent learning experiences at university, and I can assure you none of them were the times I had to re-write course info and hand it back to them in order to check off a box.
Maybe, if one student does something they might be wrong, but if 90% of students do something, perhaps the assignment is wrong? Doubling down and saying “we’ll force them to do it by hand then!” Is rather blindly missing the point here no?
I guess you could require a special encrypted keyboard in your plan.
In a class setting, maybe make the AI-detection an element of take-home assignments - whoever gets the lowest AI-similarity score gets a few points of extra credit or something
As for computer science courses, I'm guessing it's hard to not write simple code that appears AI-generated...so maybe that kind of work needs a written summary to go along with the code as well
Forcing people to do these things supposedly results in a better, more competitive society. But does it really? Would you rather have someone on your team who did math because it let them solve problems efficiently, or did math because it’s the trick to get the right answer?
Writing is in a similar boat as math now. We’ll have to decide whether we want to force future generations to write against their will.
I was forced to study history against my will. The tests were awful trivia. I hated history for nearly a decade before rediscovering that I love it.
History doesn’t have much economical value. Math does. Writing does. But is forcing students to do these things the best way to extract that value? Or is it just the tradition we inherited and replicate just because our parents did?
I remember another parent ranting about their 3rd grade kids “stupid homework” since it had kids learning different ways of summing numbers. I took a look at the homework and replied “wow, the basics out set theory are in here!” We then had a productive discussion of how that arithmetic exercise led to higher math and ways of framing problems.
Similarly, writing produces a different form of thought than oral communication does.
History is a bit different, but a goal of history and literature is (or it least should be) to socialize students and give them a common frame of reference in society.
Finally there is the “you don’t know when you’ll need it defense.” I have a friend who spent most of the last 20 years as a roofer, but his body is starting to hurt. He’s pivoting to CAD drafting and he’s brushing off a some of those math skills he hated learning in school. And now arguing with his son about why it’s important.
Those are the fundamental defenses- that we are seeking not skills but ways of viewing the world + you don’t know what you’ll need. There are obviously limits and tradeoffs to be made, but to some degree yes, we should be forcing students (who are generally children or at least inexperienced in a domain) to things they don’t like now for benefits later.
One counter argument to yours is that when you do need the skills, you can learn them later. It’s arguably easier than it has been at any point in human history. In that context, why front load people with something they hate doing, just because their parents think it’s a good idea? Let them wait and learn it when they need it.
Maybe professors are too stringent with their evaluation, or maybe they are not good at teaching people what a passable writing style is, or maybe students simply don't want to accept that if they don't excel at writing, a D or a C is perfectly fine. Perhaps teachers that look for good writing should have separate tests which evaluate students in both scenarios: with and without LLM help.
The same holds true for math: not everybody needs to know how to deduce a proof for every theorem, but in technical sciences, showing that ability and capability will demonstrate how much they are able to think and operate with precision on abstract concepts, very much like in programming. Even if coursework is a bit repetitive, practice does turn shallow knowledge into operational knowledge.
There are greater difficulties that people will have to do in their daily lives than being "forced" to learn how to read, write and do arithmetic. Maybe learning the lesson of overcoming smaller, difficult tasks will allow them to adapt to greater difficulties in the future.
To quote Seneca:
A gem can not be polished with friction, nor a man perfected without trials.The "wanting to like things" is a highly undervalued skill/trait. It comes down to building a habit through repetition - not necessarily having fun or getting results, but training your mind like a muscle to think putting in effort isn't that bad an activity.
For those growing up I think this is not something that is taught - usually it is already there as a childlike sense of wonder that gets pruned by controlling interests. If education forcing you to do math removes any enthusiasm you had for math, that's largely determined by circumstance. You'd need someone else to tell you the actual joys of X to offset that (and I'd guess most parents/teachers don't practice math for fun), or just spontaneously figuring out how interesting X is totally on one's own which is even rarer.
I didn't have either so I'm a mathophobe, but I'm alright with that since I have other interests to focus on.
<https://goodreads.com/book/show/585474.Writing_to_Learn>I agree with the broader point of the article in principle. We should be writing to edify ourselves and take education seriously because of how deep interaction with the subject matter will transform us.
But in reality, the mindset the author cites is more common. Most accounting majors probably don't have a deep passion for GAAP, but they believe accounting degrees get good jobs.
And when your degree is utilitarian like that, it just becomes a problem of minimizing time spent to obtain the reward.
I can’t be the only student who had both the experience of wonderful learning moments, AND could see a badly designed assignment a mile off and wasn’t motivated to give such a thing my full attention no?
As a side note, if you want the prompt, simply ask for it in the assignment. Asking students for one thing and then complaining when you don’t get another is insanity.
EDIT: Not a jab at the author per se, more that it's a third or fourth time I see this particular argument in the last few weeks, and I don't recall seeing it even once before.
To actually teach this, you do something like this:
"Here's a little dummy robot arm made out of Tinkertoys. There are three angular joints, a rotating base, a shoulder, and an elbow. Each one has a protractor so you can see the angle.
1. Figure out where the end of the arm will be based on those three angles. Those are Euler angles in action. This isn't too hard.
2. Figure out what the angles should be to touch a specific point on the table. For this robot geometry, there's a simple solution, for which look up "two link kinematics". You don't have to derive it, just be able to work out how to get the arm where you want it. Is the solution unambiguous? (Hint: there may be more than one solution, but not a large number.)
3. Extra credit. Add another link to the robot, a wrist. Now figure out what the angles should be to touch a specific point on the table. Three joints are a lot harder than two joints. There are infinitely many solutions. Look up "N-link kinematics". Come up with a simple solution that works, but don't try too hard to make it optimal. That's for the optimal controls course.
This will give some real understanding of the problems of doing this.
(I know jack all about robotics but that sounds like a pretty common assignment, the kind an LLM would regurgitate someone else's homework.)
The answer might be bogus, but the AI will sound confident all the way through.
No wonder sales and upper management love AI
The goal is to make something legible, but the reality is we are producing slop. I'm back to writing before my brain becomes lazy.
I've grown to respect typos and slightly misconstructed sentences. It's an interesting dynamic that now what appeared lazy to 2021 eyes actually indicates effort and what appeared polished and effortful in 2021 now indicates laziness.
An example is how the admins of my local compute cluster communicate about downtimes and upgrades etc and they are clearly using AI and it's so damn annoying, it feels like biting into cotton candy fluff. Just send the bullet points! I don't need emojis, I don't need the fake politeness. It's no longer polite to be polite. It doesn't signal any effort.
if you can one-shot an answer to some problem, the problem is not interesting.
the result is necessary, but not sufficient. how did you get there? how did you iterate? what were the twists and turns? what was the pacing? what was the vibe?
no matter if with encyclopedia, google, or ai, the medium is the message. the medium is you interacting with the tools at your disposal.
record that as a video with obs, and submit it along with the result.
for high stakes environments, add facecam and other information sources.
reviewers are scrubbing through video in an editor. evaluating the journey, not the destination.
And reviewing video would be a nightmare.
more is better.
you can scrub video with your finger on an iphone. serious review is always high effort, video changes nothing.
Video in itself is not more information by definition. Just look at those automatically generated videos when you try finding a review on an unusual product.
books are great.
hundreds of hours of video of the author writing that book, is strictly more information.
Let's be real... Multi-modal LLMs are scrubbing through the journey :P
not every review is important.
The is especially the case when you are about to complain about style, since that can easily be adjusted, by simply telling the model what you want.
But I think there is a final point that the author is also wrong about, but that is far more interesting: why we write. Personally I write for 3 reasons: to remember, to share and to structure my thoughts.
If an LLM is better then me at writing (and it is) then there is no reason for me to write to communicate - it is not only slower, it is counterproductive.
If the AI is better at wrangling my ideas into some coherent thread, then there is no reason for me to do it. This one I am least convinced about.
AI is already much better than me at strictly remembering, but computers have been that since forever, the issue is mostly convinient input/output. AIs makes this easier thanks to speech to text input.
[0]: See eg. https://www.oneusefulthing.org/p/centaurs-and-cyborgs-on-the....
This is especially true for students.
I think this will be no more of a contest than playing chess has been: humans don't stand a chance, but it also doesn't matter because being better or worse than the AI is besides the point.
This is ridiculous. Even if the author has never typed a single character into a prompt box, he can still come to perfectly valid conclusions about the technology just by observing patterns in the outputs that are shoved into his face.
"I wish these astrophysicists had stated up front that they've never created a galaxy. How can they have a well-formed opinion on cosmic structures if they only ever observe them?"
The most obvious ChatGPT cheating, like that mentioned in this article, is pretty easy to detect.
However, a decent cheater will quickly discover ways to conduce their LLM into producing text that is very difficult to detect.
I think if I was in the teaching profession I'd just leave, to be honest. The joy of reviewing student work will inevitably be ruined by this: there is 0 way of telling if the work is real or not, at which point why bother?
Do you have any examples of this? I've never been able to get direct LLM output that didn't feel distinctly LLM-ish.
A study on whether LLMs can influence people on r/changemymind
Teachers will lament the rise of AI-generated answers, but they will only ever complain about the blatantly obvious responses that are 100% copy-pasted. This is only an emerging phenomenon, and the next wave of prompters will learn from the mistakes of the past. From now on, unless you can proctor a room full of students writing their answers with nothing but pencil and paper, there will be no way to know for certain how much was AI and how much was original/rewritten.
But I know it's easier said than done: if you get a student to realise that the time they spend at school is a unique opportunity for them to learn and grow, then you're job is almost done already.
Rule 3 of the subreddit quite literally bars people from accusing posts of being AI-generated. I have only visited it a few times in recent times, but I noticed quite a few GPT-speak posts with comments calling it out getting removed and punished.
They don’t get an exemption if the parents don’t care.
Quite the assertion. If anything the evidence is in favor of the other direction.
It was eye opening to see that most students cheat. By the same token, most students end up successful. It’s why everyone wants their kids to go to college.
Or, bad money chases out good. Idiots that cheat will get the recommendations for jobs where by maxing the grade. The person that actually works gets set back. Even worse society at large loses and actually educated person. And lastly a school is going to attempt to protect their name by preventing cheating.
Talk to the student, maybe?
I have been an interviewer in some startups. I was not asking leetcode questions or anything like that. My method was this: I would pretend that the interviewee is a new colleague and that I am having coffee with them for the first time. I am generally interested in my colleagues: who are they, what do they like, where do they come from? And then more specifically, what do they know that relates to my work? I want to know if that colleague is interested in a topic that I know better, so that I could help them. And I want to know if that colleague is an expert in a topic where they could help me.
I just have a natural discussion. If the candidate says "I love compilers", I find this interesting and ask questions about compilers. If the person is bullshitting me, they won't manage to maintain an interesting discussion about compilers for 15 minutes, will they?
It was a startup, and the "standard" process became some kind of cargo culting of whatever they thought the interviews at TooBigTech were like: leetcode, system design and whatnot. Multiple times, I could obviously tell in advance that even if this person was really good at passing the test, I didn't think it would be a good fit for the position (both for the company and for them). But our stupid interviews got them hired anyway and guess what? It wasn't a good match.
We underestimate how much we can learn by just having a discussion with a person and actually being interested in whatever they have to say. As opposed to asking them to answer standard questions.
There always was a bunch of realistic options to not actually do your submitted work, and AI is merely makes it easier, more detectable and more scalable.
I think it moves the needle from 40 to 75, which is not great, but you'd already be holding your nose at student work half of the time before AI, so teaching had to be about more than that (and TBH it was, when I was in school teachers gave no fuck about submitted work if they didn't validate it by some additional face to face or test time)
I might argue you couldn't really tell if it was "real" before LLMs, either. But also, reviewing work without some accompanying dialogue is probably rarely considered a joy anyway.
The kids these days got everything...
Sounds to me like they asked the students to just regurgitate genetic course info and then complained when that’s what they received. This wasn’t going to lead to an excellent learning moment for these students whether an LLM was used or not.
As always, I reject wholeheartedly what this skeptical article has to say about LLMs and programming. It takes the (common) perspective of "vibe coders", people who literally don't care what code says as long as something that runs comes out the other side. But smart, professional programmers use LLMs in different ways; in particular, they review and demand alterations to the output, the same way you would doing code review on a team.
The implication there is that this is acceptable to pass a robotics class, and potentially this gives them more information about students' comprehension to further improve their instruction and teaching ("...that they have some kind of internal understanding to share").
On that second point, I have yet to see someone demonstrate a "smart, professional programmer use LLMs" in a way where it produces high quality output in their area of expertise, while improving their efficiency and thus saving time for them (compared to them just using a good, old IDE)!
So, observing a couple of my colleagues (I am an engineering manager, but have switched back and forth between management and IC roles for the last ~20 years), I've seen them either produce crap, or spend so much time tuning the prompts that it would have been faster to do it without an LLM. They mostly used Github Copilot or ChatGPT (most recent versions as of last few months ago).
I am also keeping out a keen eye for any examples of this (on HN in particular), but it usually turns out things like https://news.ycombinator.com/item?id=43573755
Again, I am not saying it's not being done, but I have struggled to find someone who would demonstrate it happen in a convincing enough fashion — I am really trying to imagine how I would best incorporate this into my daily non-work programming activities, so I'd love to see a few examples of someone using it effectively.
https://x.com/adamwathan/status/1911845073286803923
Armin Ronacher also talks about using LLMs quite a bit, but I don't have as good of an example from his tweets of him straightforwardly saying "yes, they are useful to me!"
Part of my performance review is indirectly using bloat to seem sophisticated and thorough.
Over-fitting proxy measures is one of the scourges of modernity.
The only silver lining is if it becomes so wide spread and easy it loses the value of seeming sophisticated and thorough.
Maybe we should let/encourage this to happen. Maybe letting bloated zombie-like organisations bloat themselves to death would thin the herd somewhat, to make space for organisations that are less “broken”.
At the same time, I strive really hard to influence the environment I am in so it does not value content bloat as a unit of productivity, so hopefully there are at least some places where people can have their sanity back!
Documentation is an interesting use case. There are various kinds of documentation (reference, tutorial, architecture, etc.) and LLMs might be useful for things like
- repetitive formatting and summarization of APIs for reference
- tutorials which repeat the same information verbosely in an additive, logical sequence (though probably a human would be better)
- sample code (though human-written would probably be better)
The tasks that I expect might work well involve repetitive reformatting, repetitive expansion, and reduction.
I think they also might be useful for systems analysis, boiling down a large code base into various kinds of summaries and diagrams to describe data flow, computational structure, signaling, etc.
Still, there is probably no substitute for a Caroline Rose[1] type tech writer who carefully thinks about each API call and uses that understanding to identify design flaws.
Any documentation they write at best re-states what is immediately obvious from the surrounding code (Useless: I need to explain why), or is some hallucination trying to pretend it's a React app.
To their credit they've slowly gotten better now that a lot of documentation already exists, but that was me doing the work for them. What I needed them to do was understand the project from existing code, then write documentation for me.
Though I guess once we're at the point AI is that good, we don't need to write any documentation anymore, since every dev can just generate it for themselves with their favorite AI and in the way they prefer to consume it.
* They'll pretend they understand by re-stating what is written in the README, then proceed to produce nonsense.
Without that effort it's a useless sycophant and is functionally extremely lazy (ie takes short cuts all the time).
Don't suppose you've tried that particular model, after getting it to be thorough?
You don't have to play the game the same way to work there. But it helps to accept that others will play it, and manage your own expectations accordingly.
Feel for you or anyone surrounded by such others but it is most definitely not everywhere - that is used to justify your presence in a place of work you should not be
I don't have tons of examples, but in my experience:
* This worked in toxic environments. They deserve it.
* This doesn't work in a functional environment, because they don't have those bullshit metrics.
If you have to rely on those tricks, it's time to look for another job.
If your organisation is functional and you are abusing it by doing that, then you deserve to get fired.
I think that's the answer:
LLMs are primarily useful for data and text translation and reduction, not for expansion.
An exception is repetitive or boilerplate text or code where a verbose format is required to express a small amount of information.
If you aren't aware: (high-parameter-count) LLMs can be used pretty reliably to teach yourself things.
LLM base models "know things" to about the same degree that the Internet itself "knows" those things. For well-understood topics — i.e. subjects where the Internet contains all sorts of open-source textbooks and treatments of the subject — LLMs really do "know their shit": they won't hallucinate, they will correct you when you're misunderstanding the subject, they will calibrate to your own degree of expertise on the subject, they will make valid analogies between domains, etc.
Because of this, you can use an LLM as an infinitely-patient tutor, to learn-through-conversation any (again, well-understood) topic you want — and especially, to shore up any holes in your understanding.
(I wouldn't recommend relying solely on the LLM — but I've found "ChatGPT in one tab, Wikipedia open in another, switching back and forth" to be a very useful learning mode.)
See this much-longer rambling https://news.ycombinator.com/item?id=43797121 for details on why exactly this can be better (sometimes) than just reading one of those open-source textbooks.
It feels like the information is there strewn across the internet, in forums, Reddit posts, stack overflow, specs, books. But to trawl though it all was so time consuming. With an LLM you can quickly distill it down to just the information you need.
Saying that, I do feel like reading the full spec for something is a valuable exercise. There may be unknown unknowns that you can't even ask the LLM about. I was able to become a subject expert in different fields just but sitting down and reading through the specs / RFCs, while other colleagues continued to struggle and guess.
If an LLM can help you understand an RFC, it's great. You're now relying on the RFC.
If an LLM can help you not rely on the RFC, you're doing it wrong.
* Not provide background information and let people figure it out for themselves. This will not help me achieve my goals.
* Link them to Google's SRE book and hope they read it. Still not achieving my goals, because they won't.
* Spend 3 hours writing the relevant background information out for them to read as part of my proposal. This will achieve my goals, but take an extra 3 hours.
* Tell the LLM what I'm looking for and why, then let it write it for me in 2 minutes, instead of 3 hours. I can check it over, make sure it's got everything, refine it a little, and I've still saved 2.5 hours.
So for me, I think the author has missed a primary reason people use LLMs. It saves a bunch of time.
This can go beyond just specific documentation but also include things like "common knowledge" which is what the other poster meant when they talked about "teaching you things".
Note the preamble, FAQs, and that all of the winning entries are now neural networks.
Done, now ai is just lossy prettyprinting.
(See also the famous Pascal quote “This would have been a shorter letter if I had the time”).
P.s. for reference I’ve asked an LLM to compress what I wrote above. Here is the output:
When I have a murky idea that’s hard to articulate, I find it helpful to ramble—typing or dictating a stream of semi-structured thoughts—and then ask an LLM to summarize. It often captures what I mean, but more clearly and effectively.
It's much more useful for answering questions that are public knowledge since it can pull from external sources to add new info.
Ideally there's some selection done, and the fact you're receiving it means it's better than a mean answer. But sometimes they haven't even read the LLM output themselves :-(
I wish to communicate four points of information to you. I’ll ask ChatGPT to fluff those up into multiple paragraphs of text for me to email.
You will receive that email, recognize its length and immediately copy and paste it into ChatGPT, asking it to summarize the points provided.
Somewhere off in the distance a lake evaporates.
It’s like math homework, you always had to show your working not just give the answer. AI gives us an answer without the journey of arriving at one, which removes the purpose of doing it in the first place.
The worst was the answer to the question "How can we utilize AI to greater effect in our work?". A nice open-ended question where they had a beautiful opportunity to show off how knowledgeable and forward thinking they are, right? Especially considering they're the ones behind the massive AI push our product has gone with as of late.
"You can ask it to write emails for you!" Was the one and only thing these multi-milli/billionaires could come up with. Our core product itself is literally an email interface, and we have an AI email generation feature built in...
I had to turn my webcam off because I genuinely laughed out loud at that response for how insanely elementary and useless it was as an answer. It also showed me these people do literally nothing other than answer emails - and even then they're too bloody lazy and give so little of a shit they can't even do that part themselves.
AI usage is a lot higher in my work experience among people who no longer code and are now in business/management roles or engineers who are very new and didn't study engineering. My manager and skip level both use it for all sorts of things that seem pointless and the bootcamp/nontraditional engineers use it heavily. Our college hires we have who went through a CS program don't use it because they are better and faster than it for most tasks. I haven't found it to be useful without an enormous prompt at which point I'd rather just implement the feature myself.
As it turns out, a well written ticket makes a pretty good input into an LLM. However, it has the added benefit of having my original thought process well documented, so sometimes I go through the process of writing a ticket / subtask, even if I ended up giving it to an AI tool in the end.
> Since this is a long thread and we're including a wider audience, I thought I'd add Copilot's summary...
Someone called them out for it, several others defended it. It was brought up in one team's retro and the opinions were divided and very contentious, ranging from, "the summary helped make sure everyone had the same understanding and the person who did it was being conscientious" to "the summary was a pointless distraction and including it was an embarrassing admission of incompetence."
Some people wanted to adopt a practice of not posting summaries in the future but we couldn't agree and had to table it.
If I were to include AI generated stuff into my communication I'd also make it clear as people might guess it anyway.
1. “When copying another person’s words, one doesn’t communicate their own original thoughts, but at least they are communicating a human’s thoughts. A language model, by construction, has no original thoughts of its own; publishing its output is a pointless exercise.”
LLMs, having being trained using the corpus of the web, I would argue communicate other human’s thoughts particularly well. Only in exercising an avoidance of plagiarism are the thoughts of other human’s evolved into something closer to “original thought” for the would-be plagarizer. But yes, at least a straight copy/paste retains the same rhetoric as the original human.
2. I’ve seen a few advertisements recently leverage “the prompt” as a means to resonate visual appeal.
i.e a new fast food delivery service starting their add with some upbeat music and a visual presentation of somebody typing into a LLM interface, “Where’s the best sushi around me?” And then cue the advertisement for the product they offer.
I actually don't think that it is good at that. I have heard of language teachers trying to use it to teach the language (it's a model language, it should be good at it, right?) and realised that it isn't good at that.
Of course I understand the point of your message, which is that you feel your teachers were not helpful and I have empathy for that.
Your benchmark for "long flowing beautiful content" is apple.com? It's competing with Hemingway?
Can you share a link to what you mean?
The school should be drilling into students, at orientation, what some school-wide hard rules are regarding AI.
One of the hard rules is probably that you have to write your own text and code, never copy&paste. (And on occasions when copy&paste is appropriate, like in a quote, or to reuse an off-the-shelf function, it's always cited/credited clearly and unambiguously.)
And no instructors should be contradicting those hard rules.
(That one instructor who tells the class on the first day, "I don't care if you copy&paste from AI for your assignments, as if it's your own work; that just means you went through the learning exercise of interacting with AI, which is what I care about"... is confusing the students, for all their other classes.)
Much of society is telling students that everything is BS, and that their job is to churn BS to get what they want. Early "AI' usage popular practices so far looks to be accelerating that. Schools should be dropping a brick wall in front of that. Well, a padded wall, for the students who can still be saved.
Yes, totally. Unfortunately, it takes time and maturity to understand how this is completely wrong, but I feel like most students go through that belief.
Not sure how relevant it is, but it makes me think of two movies with Robin Williams: Dead Poet's Society and Will Hunting. In the former, Robin's character manages to get students interested in stuff instead of "just passing the exams". In the later, I will just quote this part:
> Personally, I don’t give a shit about all that, because you know what? I can’t learn anything from you I can’t read in some fuckin’ book. Unless you wanna talk about you, who you are. And I’m fascinated. I’m in.
I don't give a shit about whether a student can learn the book by heart or not. I want the student to be able to think on their own; I want to be able to have an interesting discussion with them. I want them to think critically. LLMs fundamentally cannot solve that.
The issue, IMO, is that some people throw in a one-shot, short prompt, and get a generic, boring output. "Garbage in, generic out."
Here's how I actually use LLMs:
- To dump my thoughts and get help organizing them.
- To get feedback on phrasing and transitions (I'm not a native speaker).
- To improve tone, style (while trying to keep it personal!), or just to simplify messy sentences.
- To identify issues, missing information, etc. in my text.
It’s usually an iterative process, and the combined prompt length ends up longer than the final result. And I incorporate the feedback manually.
So sure, if someone types "write a blog post about X" and hits go, the prompt is more interesting than the output. But when there are five rounds of edits and context, would you really rather read all the prompts and drafts instead of the final version?
(if you do: https://chatgpt.com/share/6817dd19-4604-800b-95ee-f2dd05add4...)
I think you missed the point of the article. They did not mean it literally: it's a way to say that they are interested in what you have to say.
And that is the point that is extremely difficult to make students understand. When a teacher asks a student to write about a historical event, it's not just some kind of ceremony on the way to a degree. The end goal is to make the student improve in a number of skills: gathering information, making sense of it, absorbing it, being critical about what they read, eventually building an opinion about it.
When you say "I use an LLM to dump my thoughts and get help organising them", what you say is that you are not interested in improving your ability to actually absorb information. To me, it says that you are not interested in becoming interesting. I would think that it is a maturity issue: some day you will understand.
And that's what the article says: I am interested in hearing what you have to say about a topic that you care about. I am not interested into anything you can do to pretend that you care or know about it. If you can't organise your thoughts yourself, I don't believe that you have reached a point where you are interesting. Not that you will never get there; it just takes practice. But if you don't practice (and use LLMs instead), my concern is that you will never become interesting. This time is wasted, I don't want to read what your LLM generated from that stuff you didn't care to absorb in the first place.
- To "Translate to language XYZ", and that is not sometimes strightforward and needs iterating like "Translate to language <LANGUAGE> used by <PERSON ROLE> living in <CITY>" and so on.
And the author is right, I use it as 2nd-language user, thus LLM produces better text than myself. However I am not going to share the prompt as it is useless (foreign language) and too messy (bits of draft text) to the reader. I would compare it to passing a book draft thru editor and translator.
You speak English? Write and send your message in English. The receiver can copy-paste it in a translator. This way, they will know that they are not reading the original. So if your translated message sounds inaccurate, offensive or anything like that, they can go back to your original message.
Make better assignments.
People say “I saved so much time on perf this year with the aid of ChatGPT,” but ChatGPT doesn’t know anything about your working relationship with your coworker… everything interesting is contained in the prompt. If you’re brain dumping bullet points into an LLM prompt, just make those bullets your feedback and be done with it? Then it’ll be clear what the kernel of feedback is and what’s useless fluff.
I like reading and writing stories. Last month, I compared the ability of various LLMs to rewrite Saki's "The Open Window" from a given prompt.[1] The prompt follows the 13-odd attempts. I am pretty sure in this case that you'd rather read the story than the prompt.
I find the disdain that some people have for LLMs and diffusion models to be rather bizarre. They are tools that are democratizing some trades.
Very few people (basically, those who can afford it) write to "communicate original thoughts." They write because they want to get paid. People who can afford to concentrate on the "art" of writing/painting are pretty rare. Most people are doing these things as a profession with deadlines to meet. Unlike you are GRRM, you cannot spend decades on a single book waiting for inspiration to strike. You need to work on it. Also, authors writing crap/gold at a per-page rate is hardly something new.
LLMs are probably the most interesting thing I have encountered since I did the computer. These puritans should get off of their high horse (or down from their ivory tower) and join the plebes.
[1] Variations on a Theme of Saki (https://gist.github.com/s-i-e-v-e/b4d696bfb08488aeb893cce3a4...)
There's so much bad writing of valuable information out there. The major sins being: burying the lede, no or poor sectioning, and just generally verbose.
In some cases, like in EULAs and patents that's intentional.
I have to admit I was a bit surprised how bad LLMs are at the continue this essay task. When I read it in the blog I suspected this might have been a problem with the prompt or the using one of the smaller variants of Gemini. So I tried it with Gemini 2.5 Pro and iterated quite a bit providing generic feedback without offering solutions. I could not get the model to form a coherent well reasoned argument. Maybe I need to recalibrate my expectations of what LLMs are capable, but I also suspect that current models have heavy guardrails, use a low temperature and have been specifically tuned for problem solving and avoid hallucinations as much as possible.
Every day I'm made more aware of how terrible people are at identifying AI-generated output, but also how obsessed with GenAI-vestigating things they don't like or wouldn't buy because they're bad.
The very first time I enjoyed talking to someone in another language, I was 21. Then an exchange student, I had a pleasant and interesting discussion with someone in that foreign language. On the next day, I realised that I wouldn't have been able to do that without that foreign language. I felt totally stupid: I had been getting very good grades in languages for years at school without ever caring about actually learning the language. And now, it was obvious, but all that time was lost; I couldn't go back and do it better.
A few years earlier, I had this great history teacher in high school. Instead of making us learn facts and dates by heart, she wanted us to actually get an general understanding of a historical event. Actually internalise, absorb the information in such a way that we could think and talk about it. And eventually develop our critical thinking. It was confusing at first, because when we asked "what will the exam be about", she wouldn't say "the material in those pages". She'd be like "well, we've been talking about X for 2 months, it will be about that".
Her exams were weird at first: she would give us articles from newspapers and essentially ask what we could say about them. Stuff like "Who said what, and why? And why does this other article disagree with the first one? And who is right?". At first I was confused, and eventually it clicked and I started getting really good at this. Many students got there as well, of course. Some students never understood and hated her: their way was to learn the material by heart and prove it to get a good grade. And I eventually realised this: those students who were not good at this were actually less interesting when they talked about history. They lacked this critical thinking, they couldn't make their own opinion or actually internalise the material. So whatever they would say in this topic was uninteresting: I had been following the same course, I knew which events happened and in which order. With the other students were it "clicked" as well, I could have interesting discussion: "Why do you think this guy did this? Was it in good faith or not? Did he know about that when he did it? etc".
She was one of my best teachers. Not only she got me interested in history (which had never been my thing), but she got me to understand how to think critically, and how important it is to internalise information in order to do that. I forgot a lot of what we studied in her class. I never lost the critical thinking. LLMs cannot replace that.
There’s a lot of “no, it is the children who are wrong” going on in academia right now and it’s an issue.
It's been incredibly blackpilling seeing how many intelligent professionals and academics don't understand this, especially in education and academia.
They see work as the mere production of output, without ever thinking about how that work builds knowledge and skills and experience.
Students who know least of all and don't understand the purpose of writing or problem solving or the limitations of LLMs are currently wasting years of their lives letting LLMs pull them along as they cheat themselves out of an education, sometimes spending hundreds of thousands of dollars to let their brains atrophy only to get a piece of paper and face the real world where problems get massively more open-ended and LLMs massively decline in meeting the required quality of problem solving.
Anyone who actually struggles to solve problems and learn themselves is going to have massive advantages in the long term.
It means that you are losing your time. If you are a university student and use LLMs for your classes while "challenging your mind" for stuff outside of class, maybe you should just not be studying there in the first place.
If you want to make your own certificates, good luck getting them on the trusted list.
Companies need to bring folks in on a probation period and actually test the skills are there.
While we're talking about things we're grateful for, I am so glad that we've structured the education and employment systems such that not having a degree puts you at significant risk of unemployment, prevents you from ever immigrating anywhere for the first decade of your working life, and generally marks you as a failure.
Regardless of the existence of other ways to exercise your legs which you also will not do, because you're a person with working legs who chooses to use a wheelchair.
But a huge amount of "ugh I'm too smart for this assignment" complaining that students do is just kids being immature rather than an honest attempt at learning through other means.
Writing is hard. Sometimes it means sitting with yourself, for hours, without any progress. Leaning on an LLM to ease through those tough moments is 100% short circuiting the learning process.
To your point, maybe you're learning something else instead, like when/how to prompt an LLM or something. But you're definitely not learning how to write. Whether that's relevant is a separate discussion.
Sounds like "back in my days" type of complaining. Do you have any evidence of this "100% reduction" or is it just "AI bad" bandwagoning?
> But you're definitely not learning how to write.
How would you know? You've never tested him. You're making a far-reaching assumption about someone's learning based on using an aid. It's the equivalent of saying "you're definitely not learning how to ride a bicycle if you use training wheels".
Its basically adults producing texts of slop messages to each other. It is actually atrophying.
You might be in a circle of people that wants to know "why" things work. For example, when there's a bug, we go through several processes of:
There's a bug...why does it happen? What were they thinking when they wrote this? How to prevent this from happening?
This is true even for simple bugs, but nowadays you just vibe code your away into the solution, asking the AI to fix it over and over without ever understanding how it works.
Perhaps its just the way things are. I mean who uses their head to do calculations nowadays? Who knows how to create a blurring effect in physical drawing?
Exactly. I tend to think that the role of a teacher is to get the students to realise what learning is all about and why it matters. The older the students get, the more important it is.
The worst situation is a student finishing university without having had that realisation: they got through all of it with LLMs, and probably didn't learn how to learn or how to think critically. Those who did, on the other hand, didn't need the LLMs in the first place.
I'm there for the degree. If I wanted to learn and engage with material, I could save $60,000 and do that online for free, probably more efficiently. The purpose of a class writing exercise is to get the university to give me the degree, which I cannot do by actually learning the material (and which, for classes I care about, I may have already done without those exercises), but can only do by going through the hoops that professors set up and paying the massive tuition cost. If there were a different system where I could just actually learn something (which probably wouldn't be through the inefficient and antiquated university system) and then get a valid certificate of employability for having actually learned it, that would be great. Unfortunately, however, as long as university professors are gatekeepers of the coveted certificate of employability, they're going to keep dealing with this incentive issue.
I can’t imagine this in my own life. I use concrete things and ways of thinking and working I learned in my CS degree _all the time_.
Academia put itself as a gateway and barrier to the middle class. Why would we be surprised when people with no interest in anything but the goal are not enthralled by the process?
We could make it less meaningful if employers weren’t so keen on using credentials as their own gateway. That may have more of a chance of happening if the OPs perspective becomes more prevalent and the credential becomes an increasingly worse signal for meaningful skills.
How did academia do that? It doesn’t seem like universities would have the power to do that. More likely, either employers put academia as a gateway. Or even: the culture at large misunderstood what pathways existed to middle class life. Or even: pathways to middle class life became scarcer and more insecure, and the real gatekeepers (hiring managers) had no good ways to select which of the many people at the door to let through.
Why don't employers recognize the credentials of a MOOC to the same degree that they would a university degree?
We could similarly ask why employers value the degrees of some universities more than others.
I think it's important to realize that ultimately the decisions come from the employers, not the universities. No one is making the employers do anything. But at least the second question might have a clearer partial answer. In part, there is a selection of a tribe, an implicit "culture fit" that's happening. It isn't uncommon to see employer bias towards specific universities. This is especially true with prestigious universities.
But it's not the universities that are making anyone do anything and that's an important distinction.
I would argue that if it costs $60,000, both your education system and the recruitment in those companies that require this degree are broken. It's not the case in all countries though.
Not that it is your fault, just stating the obvious.
But that's just the job market. The other elephants in the room are inflation and the housing market. People who don't have top-notch jobs (that require degrees) can't afford to buy a house. They can hardly afford rent. Cities don't want to build more housing because that will undermine the equity growth of homeowners.
We are a society of ladder-pullers.
> We are a society of ladder-pullers.
Meh, academic degrees don't come for free, someone has to pay for universities, staff and other expenses. In the US it's everyone for themselves by student loans that can't be discharged in bankruptcies, in Europe it's the tax payers.
The problem is, the ones profiting from the gatekeeping (aka employers) aren't the ones paying for it in either system. If employers had to pay, say, 10.000$ for each job listing that requires an academic degree without an actual valid reason, guess how fast that incentive would lead to employers not requiring academic degrees for paper-pusher bullshit jobs.
Further, some high school graduates (like myself at the time) literally don’t know HOW to learn on their own. I thought I did but college humbled me, made me realize that suddenly i’m in the drivers seat and my teachers won’t be spoon feeding me knowledge step by step. it’s a really big shift.
If you were the perfect high school graduate, then congrats, you’re like the 0.01%! And you should be proud (no sarcasm). This doesn’t describe society at large though.
For the very few that are extremely motivated and know exactly what job they want, i do think we need something in between self guided and college? No BS - strictly focusing on job training. Like a boot camp, but one that’s not a scam haha.
The other aspect of college you ignore is, it is a way to build a network prior to entering the workforce. It’s also one of the best times to date, but that’s another story.
Completely agree that the cost of college in the US is ridiculous though.
I don’t know how generalizable this is. I remember reading a few studies trying to assess if Ivy League education was really more valuable that a state school. The result (IIRC) was that it only matters for students who came from the lowest economic strata; the authors presumed it was due to the network effect. But that also means the network effect was negligible for the majority of students.
> I'm there for the degree
When you're in a position to hire or influence hiring, will you consider those without degrees?
I ask because I hear this sentiment a lot but we still have a system becoming more reliant on degrees. The universities may be the gatekeepers of those degrees but they're not the ones gatekeeping the jobs. They have no influence there. They were not the ones who decided degree = credentials. I ask because many people eventually grow in their jobs to a point where they have significant influence over hiring. So when that time comes will you perpetuate the system you criticize or push against it? Truthfully, this is a thing that can be done with little to no risk to your own employment.
When I was a kid and got an assignement for writing an essey about "why good forces prevailed in Lords of the Rings" as a gate check to see if I actually read the novel I had three choices: (a) read the novel and write the essey myself (b) find an already written essey - not an easy task in pre-internet era but we had books with esseys on most common topics you could "copy-paste" - and risk that the professor is familiar with the source or someone else used the same source (c) ask class mate to give me their essey as a template and rephrase it as my own
A and C would let me learn about the novel and let me polish my writing skills.
Today I can ask ChatGPT to write me a 4 pages essay about a novel I've never heard of and call it a day. There's no value gained in the process.
That's a simple example. The problem is that the same applies to programming. Novice programmer will claim that LLM give them power to take on hard tasks and programm in languages they were not familiar before. But they are not gaining any skill nor knowledege from that experience.
If I ask google maps to plot me a directions from Prague to Brussels it will yield a list of turns that will guide me to my destinations, but by any means I can't claim I've learned topography of Germany in the process.
(I don't usually do that, but it appears so many times in the first few sentences that I had to do it here)
I agree with your points, though, but I think that they are in agreement with the comment you are answering to...
And yeah, and revisiting the OP we're on the same track.
It sounds like you agree with GP.
I figured this out in high school. It can’t be all that uncommon of a thought that if you are already in school and paying and given time to learn, you might as well do so?
Young kids don't get it, they just do what they're asked. That's okay. University students graduating without having figured it out is a problem. And somewhere in the middle is when the average student gets there, hopefully?
> how many intelligent professionals and academics don't understand this
Mastery of a discipline does not imply any pedagogical knowledge, despite anything one of my childhood heroes, Richard Feynman, might have claimed.
Despite frequent claims otherwise, in my experience and sampling of PhDs and Masters of different sorts and grad students working toward those degrees, an advanced degree does not teach anyone how to lead or teach. This is true of even some of the folks I knew studying Education itself who were a little too focused on their own research to understand anything "so simple."
> cheat themselves out of an education
What's "an education," though? For some people, education is focused on how to learn. For others, it's focused on some kind of certification to get a job. Some of us see value in both. And I'm sure there are other minority opinions as well. We, as a society, can't agree. The only thing we can seem to agree on in the US is that college should be expensive and saddle students with ridiculous debt.
I agree that this situation that the author outlines is unsatisfactory but it's mostly the fault of the education system (and by extension the post author). With a class writing exercise like the author describes, of course the students are going to use an LLM, they would be stupid not to if their classmates are using it.
The onus should be on the educators to reframe how they teach and how they test. It's strange how the author can't see this.
Universities and schools must change how they do things with respect to AI, otherwise they are failing the students. I am aware that AI has many potential and actual problems for society but AI, if embraced correctly, also has the potential to transform the educational experience in positive ways.
https://chatgpt.com/share/6817fe76-973c-8011-acf3-ef3138c144...
> Universities and schools must change how they do things with respect to AI, otherwise they are failing the students.
Hard disagree.
Students need to answer a fundamental question of themselves;
Am I here to learn or to get a passing grade?
If it is the latter, the former was not the point to begin with.
For most continued learning it's better if the university uses calculators, compilers, prepared learning materials, and other things that do stuff on behalf of the students instead of setting the bar permanently to "the student should want to engage everything at a base level or they must not be here to learn". It allows much more advanced learning to be done in the long run.
Why would they be stupid? Were people before LLMs stupid for not asking smarter classmate/parent/paid contractor to solve the homework for them?
Large part of education is learning about things that can be easily automated, because you can't learn hard things without learning easy things. Nothing conceptually changed in this regard, like Wolfram Alpha didn't change the way differentiation is taught.
I agree that making assignments not designed with external sources in mind significantly impact the final grade is not ideal. I think this is minor and easily fixable point rather than some failure of the whole education system.
https://www.reddit.com/r/ChatGPT/comments/1hun3e4/my_little_...
I don't know what the answer is. I'm old school, if it was up to me I'd bring back slide rules and log tables, because that's such a visual and tactile way of getting to know mathematics and numbers.
It's interesting to consider how AI is affecting humans' cognition skills. Is it going to make us stupid or free us up to use our mental capacities for higher level activities? Or both?
Even if one won’t need that specific know how after exams - just realization how much one can memorize and trying out some approaches to optimize it is where people grow/learn.
I believe that the same holds true for other facts one might memorize. Yes, the fact may seem like meaningless trivia (and might even be so at times), but in the right situation knowing that fact can help with understanding. You can certainly spend too much time on memorization of facts, but that doesn't mean it has no place either.
Using an LLM to do schoolwork is like taking a forklift to the gym.
If all we were interested in was moving the weights around, you’d be right to use a tool to help you. But we’re doing this work for the effect it will have on you. The reason a teacher asks you a question is not because they don’t know the answer.
This is much larger than a cultural problem with the students of today. They believe, rightfully and accurately, that the university degree is yet another part of the machine that they will become a cog in.
What should be alarming to everyone is that these students will graduate without having learned anything and then go into the workplace where they will continue to not use their atrophied critical thinking skills, to simply do yet more, as a cog in the machine.
* according to the UCLA CIRP freshman survey
Yeah, that's when the great "push for education" came, as well as neoliberalism which preached continuous hustling and individuality. And in the 90s, the ADA and other anti discrimination laws hit, and requiring a college degree was and still is a very useful pre-screening filter for HR to continue discrimination.
For me the impact of the university administrators as they chased higher endowments for more buildings with naming rights and expanded their own bureaucracies with direct hires that did not directly contribute to the faculty mission did more to alter the university experience than anything else.
[0]: https://educationdata.org/average-cost-of-college-by-year
A decent amount of my professors don't know the answers because they bought the course, test questions, and lectures from Cengage. During exam review, they just regurgitate the answer justification that Cengage provided. During the lectures, they struggle to explain certain concepts since they didn't make the slides.
Professors automate themselves out of the teaching process and are upset when students automate themselves out of the learning process.
I can tell when the faculty views teaching as a checkbox that they officially have to devote 40% of their time to. I can tell when we are given busywork to waste our time instead of something challenging.
To use your analogy, I'm being told to move 1000 plush reproductions of barbells from Point A to B by hand because accreditation wants to see students "working out" and the school doesn't want high failure rates.
We are all pulling out the forklift. Some of us are happy because we don't have to work as hard. Others are using the forklift so we can get in a real workout at home, as school is not a good use of our time. Either way, none of us see value moving paperweights all day.
edit:
My favourite course during my Computer Engineering degree was Science Fiction because that professor graded us on substance instead of form. It was considered a hard class because one would get good marks on the essays by focusing on building substantive points instead of strict adherence to the form of a five-paragraph hamburger essay.
The call to action is to make courses harder and stop giving students plush barbells.
For example, University of Toronto Engineering Science (hardest program in Canada) gives first-year students a "vibe coding" lab in which students learn how to solve a problem that AI cannot.
https://www.cs.toronto.edu/~guerzhoy/vibecoding/vibecoding.h...
Essentially, since they are a summary of "the" state of knowledge, the teacher should be able to ask them to put a number on how novel a piece of text is.
Once LLMs are able to evaluate, independently, the soundness of an argument... (Hopefully, this will be achieved AFTER $5 H100s reach the average consumer)
Compare: My piano teacher doesn't give diplomas because none of her students would care, her students actually want to learn. When my piano teacher cancels class, I am disappointed because I wanted to learn. My piano teacher doesn't need to threaten me with bad grades to get me to practice outside of class (analogous to homework), because I actually want to learn.
There are many college students for whom none of these tests would pass. They would not attend if there was no diploma, they're relieved when their professors cancel class, and they need to be bullied into studying outside of class.
What made us think these students were ever interested in learning in the first place? Instead, it seems more likely that they just want a degree because they believe that a degree will give them an advantage in the job market. Many people will never use the information that they supposedly learn in college, and they're aware of this when they enroll.
Personally, the fact that they can now get a degree with even less wasted effort than before doesn't bother me one bit. People who want to learn still have every opportunity to.
Your piano teacher does not give a diploma because she is not offering a university education. If she worked with a few other experts and they designed a coordinated curriculum and shepherded students through it over the course of two to four years, and documented that process to the point where they could file with an accrediting agency, then she could issue a degree in piano.
> then she could issue a degree in piano.
If students find a way to get a diploma without doing the work, it will soon be worth less than the paper on which it is printed.
I remember illustrating a point to a class by posing a question and then calling on a student I figured wasn't smart enough to answer correctly so that everyone could see her make the mistake.
The ethics of that still bother me.
My students however don't understand that the importance is on the process, not the result. My colleagues do.
But don't worry, worst case scenario, all of the kids growing up in this environment that are actually learning will build structures to exploit the prompters, I suspect the present situation where prompters can accidentally find themselves in real jobs is transient and building better filters will become survival imperative for businesses and institutions.
I myself went to college to get the meal ticket, not to learn. But since the system was entirely exam based, I was forced to learn.
The question is: Should we limit AI to keep the old way of learning, or use AI to make the process better? Instead of fixing small errors like grammar, students can focus on bigger ideas like making arguments clearer or connecting with readers. We need to teach students to use AI for deeper thinking by asking better questions.
We need to teach students that asking the right questions is key. By teaching students to question well, we can help them use AI to improve their work in smarter ways. The goal isn’t to go back to old methods for iterating but change how we iterate altogether.
> We need to teach students to use AI for deeper thinking by asking better questions.
Same thing here: the whole point of learning critical thinking is that you don't need to ask someone/something else. Teaching you how to ask the LLM to do it for you is not the same as teaching you how to actually do it.
In my opinion, we need to make students realise that their goal is to learn how to do it themselves (whatever it is). If they need an LLM to do it, then they are not learning. And if they are not learning, there is no point in going to school, they can go work in a field.
My take is teach them to get better at asking questions and then teach them when to use their own understanding to change their answer for the better. How many times has an AI’s answer been 5/10 and with a few fixes it’s a 9/10. That comes with time. Getting them asking questions and learning the “when” later is better at least to me.
Depends, I think. If we are talking about writing an essay (and I believe we are), then the LLM is somewhere between useless and counter-productive.
Of course, if the LLM is used to understand an RFC (I would debate how useful it is for that, but that's another discussion), then it's different. The goal was to understand the RFC, it doesn't really matter how you did it. But the goal of writing an essay is not to end up with a written essay at all. Nobody cares about it, you can burn it right after it's graded.
It feels like we are getting to this weird situation where we just use LLMs as proxies, and the long, boring text is just for LLMs to talk to each other.
For example:
Person A to LLM A: Give me my money.
LLM A to LLM B: Long formal letter.
LLM B to Person B : Give me my money.
Hopefully, nothing is lost in translation.
Even when no errors are introduced in the process, the outcome is always bad: 3 full paragraphs of text with bullets and everything where the actual information is just the original 1-2 sentences that the model was prompted with.
I never am happy reading one of those; it's just a waste of time. A lot of the folks doing it are not native English speakers. But for their use case, older tools like Grammarly that help improve the English writing are effective without the problematic decompression downsides of this class of LLM use.
Regardless of how much LLMs can be an impactful tool for someone who knows how to use one well, definitely one of the impacts of LLMs on society today is that a lot of people think that they can improve their work by having an LLM edit it, and are very wrong.
(Sometimes, just telling the LLM to be concise can improve the output considerably. But clearly many people using LLMs think the overly verbose style it produces is good.)
Back in HS literature class, I had to produce countless essays on a number of authors and their works. It never once occurred to me that it was anything BUT an exercise in producing a reasonably well written piece of text, recounting rote-memorized talking points.
Through-and-through, it was an exercise in memorization. You had to recall the fanciful phrases, the countless asinine professional interpretations, brief bios of the people involved, a bit of the historical and cultural context, and even insert a few verses and quotes here and there. You had to make the word count, and structure your writing properly. There was never any platform for sharing our own thoughts per se, which was sometimes acknowledged explicitly, and this was most likely because the writing was on the wall: nobody cared about these authors or their works, much less enjoyed or took interest in anything about them.
I cannot recount a single thought I memorized for these assignments back then. Passed these with flying colors most usually, but even for me, this was just pure and utter misery. Even in hindsight, the sheer notion that this was supposed to make me think about the subject matter at hand borders on laughable. It took astronomical efforts to even retain all the information required - where would I have found the power in me to go above and beyond, and meaningfully evaluate what was being "taught" to me in addition to all this? How would it have mattered (in specifically the context of the class)? Me actually understanding these topics and pondering about them deeply is completely inobservable through essay writing, which was the sole method of grading. If anything, it made me biased against doing so, as it takes a potentially infinite extra time and effort. And since there was approximately no way for our teacher to make me interested in literature either, he had no chance at achieving such lofty goals with me, if he ever actually aimed for them.
On the other side of the desk, he also had literal checklists. Pretty sure that you do too. Is that any environment for an honest exchange of thoughts? Really?
If you want to read people's original thoughts, maybe you should begin with not trying to coerce them into producing some for you on demand. But that runs contrary to the overarching goal here, so really, maybe it's the type of assignment that needs changing. Or the framework around it. But then academia is set in its ways, so really, there's likely nothing you can specifically do. You don't deserve to have to sift through copious amounts of LLM generated submissions; but the task of essay writing does, and you're now the one forced to carry this novel burden.
LLMs caught incumbent pedagogical practices with their pants down, and it's horrifying to see people still being in denial of it, desperately trying to reason and bargain their ways out of it, spurred on by the institutionally ingrained mutual-hostage scenario that is academia. *
* Naturally, I have absolutely zero formal relation to the field of pedagogy (just like the everyday practice of it in academia to my knowledge). This of course doesn't stop me from having an unreasonably self-confident idea on how to achieve what you think essay writing is supposed to achieve though, so if you want a terrible idea or two, do let me know.
It's the old joke of the teacher who wants students to tried their best and that failure doesn't matter. But when the student follows the process to the best of their ability and fails they are punished while the student who mostly follows the process and then fudges their answer to the correct one is rewarded.
Relying on that to automatically detect their use makes no sense.
From a teaching perspective, if there is any expectation that artificial intelligence is going to stick, we need better teachers. Ones that can come up with exercises that an artificial intelligence can't solve, but are easy for humans.
But I don't expect that to happen. I expect instead text to become more irrelevant. It already has lost a lot of its relevancy.
Can handwriting save us? Partially. It won't prevent anyone from copying artificial intelligence output, but it will make anyone that does so think about what is being written. Maybe think "do I need to be so verborragic?".
Exploring a concept-space with LLM as tutor is a brilliant way to educate yourself. Whereas pasting the output verbatim, passing it as one’s own work, is tragic: skipping the only part that matters.
Vibe coding is fun right up to the point it isn’t. (Better models get you further.) But there’s still no substitute for guiding an LLM as it codes for you, incrementally working and layering code, committing to version control along the way, then putting the result through both AI and human peer code reviews.
Yet these all qualify as “using AI”.
We cannot get new language for discussing emerging distinctions soon enough. Without them we only have platitudes like “AI is a powerful tool with both appropriate and inappropriate uses and determining which is which depends on context”.
Having spent about two decades reading other humans' "original thoughts", I have nothing else to say here other than: doubt.
That part caught my attention. As an English-as-a-second-language speaker myself, I find it so difficult to develop any form of "taste" in English the same way I have in my mother tongue. A badly written sentence in my mother tongue feels painful in a sort of physical way, while bad English usually sound OK to me, especially when asserted in the confident tone LLMs are trained in. I wish I could find a way to develop such sense for the foreign languages I currently use.
Yeah, to recycle a comment [0] from a few months back:
> Yeah, one of their most "effective" uses is to counterfeit signals that we have relied on--wisely or not--to estimate deeper practical truths. Stuff like "did this person invest some time into this" or "does this person have knowledge of a field" or "can they even think straight." [...]we might have to cope by saying stuff like: "Fuck it, personal essays and cover letters are meaningless now, just put down the raw bullet-points."
In other words, when the presentation means nothing, why bother?
Asking students for regurgitated info and then being annoyed because they supplied generic regurgitated info is somewhat telling an attitude no?
You're confusing the artifact with the purpose. Teachers across the nation are not trying to accumulate the largest corpus of distinct human-written reviews of The Great Gatby.
The goal is to elicit some kind of mental practice, and the classic request is for something that helps prove it occurred. The issue is that such proofs are now being counterfeited with unprecedented scale and ease.
When those indicators become debased and meaningless, we need to look for other ways of motivating and validating.
In that way, the prompt is more interesting, and I can’t tell you how many times I’ve gone to go write a prompt because I dunno how to write what I wanna say, and then suddenly writing the prompt makes that shit clear to me.
In general, I’d say that AI is way more useful to compress complex ideas into simple ones than to expand simplistic ideas in to complex ones.
In a world where the LLM can do the building, the engineer is no longer required.
If your assignment can be easily performed by an LLM, it’s a bad assignment. Teachers are just now finding out the hard way that these assignments always sucked and were always about regurgitating information pointlessly and weren’t helpful tools for learning lol. I did heaps of these assignments before the existence of LLMs, and I can assure you that the busywork was mostly a waste of time back then too.
People using LLMs is just proof they don’t respect your assignment - and you know what, if one person doesn’t respect your assignment, they’re probably wrong. But if 90% of people don’t respect your assignment? Maybe you should consider whether the assignment is the problem. It’s not rocket science.
"No worthy use of an LLM involves other human beings reading its output."
If you use a model to generate code, let it be code nobody has to read: one-off scripts, demos, etc. If you want an LLM to prove a theorem, have it generate some Coq and then verify the proof mechanically. If you ask a model to write you a poem, enjoy the poem, and then graciously erase it.
The hardest hit industry by AI has been essay writing services.
If anything, it seems they're noticing because the AI is doing a worse job.
That said, I myself am increasingly reading long texts written by LLMs and learning from them. I have been comparing the output of the Deep Research products from various companies, often prompting for topics that I want to understand more deeply for projects I am working on. I have found those reports very helpful for deepening my knowledge and understanding and for enabling me to make better decisions about how to move forward with my projects.
I tested Gemini and ChatGPT on “utilizing Euler angles for rotation representation,” the example topic used by the author in the linked article. I first ran the following metaprompt through Claude:
Please prepare a prompt that I can give to a reasoning LLM that has web search and “deep research” capability. The prompt should be to ask for a report of the type mentioned by the sample “student paper” given at the beginning of the following blog post: https://claytonwramsey.com/blog/prompt/ Your prompt should ask for a tightly written and incisive report with complete and accurate references. When preparing the prompt, also refer to the following discussion about the above blog post on Hacker News: https://news.ycombinator.com/item?id=43888803
https://docs.google.com/document/d/1sqpeLY4TWD8L4jDSloeH45AI...
Here is the ChatGPT report:
https://chatgpt.com/share/681816ff-2048-8011-8e0f-d8cbad2520...
I know nothing about this topic, so I cannot evaluate the accuracy or appropriateness of the above reports. But when I have had these two Deep Research models produce similar reports on topics I understand better, they have indeed deepened my understanding and, I hope, made me a bit wiser.
The challenge for higher education is trying to decide when to stick to the traditional methods of teaching—in this case, having the students learn through the process of writing on their own—and when to use these powerful new AI tools to promote learning in other ways.
The punchline? Bullet point 3 was wrong (it was a PL assignment and I'm 99% sure the AI was picking up on the word macro and regurgitating facts abut LISP). 0 points all around, better luck next time.
kookamamie•7h ago
There's too much information in the World for it to matter, I think is the underlying reason.
As an example, most enterprise communication nears the levels of noise in its content.
So, why not let a machine generate this noise, instead?
bost-ty•7h ago