A Student's Guide to Writing with ChatGPT
openai.com
openai.com
For example, I have zero qualms about relying on AI at work to write progress reports and code up some scripts. I know I can do it myself but why would I? I spent many years in college learning to read and write and code. AI makes me at least 2x more efficient at my job. It seems irrational not to use it. Like a farmer who tills his land by hand rather than relying on a tractor because it builds character or something. But there is something to be said about atrophy. If you don't use it, you lose it. I wonder if my coding skill will deteriorate in the years to come...
On the other hand, if you are a student trying to learn something new, relying on AI requires walking a fine line. You don't want to over-rely on AI because a certain degree of "productive struggle" is essential for learning something deeply. At the same time, if you under-rely on AI, you drastically decrease the rate at which you can learn new things.
In the old days, people were fit because of physical labor. Now people are fit because they go to the gym. I wonder if there will be an analog for intellectual work. Will people be going to "mental" gyms in the future?
I don't want to lose my ability to think. I don't want to become intellectually dependent on AI in the slightest.
I've been programming for over a decade without AI and I don't suddenly need it now.
> O most ingenious Theuth, the parent or inventor of an art is not always the best judge of the utility or inutility of his own inventions to the users of them. And in this instance, you who are the father of letters, from a paternal love of your own children have been led to attribute to them a quality which they cannot have; for this discovery of yours will create forgetfulness in the learners' souls, because they will not use their memories; they will trust to the external written characters and not remember of themselves. The specific which you have discovered is an aid not to memory, but to reminiscence, and you give your disciples not truth, but only the semblance of truth; they will be hearers of many things and will have learned nothing; they will appear to be omniscient and will generally know nothing; they will be tiresome company, having the show of wisdom without the reality.
Which, if any, have you used?
Did you give them a fair shot on the off-chance that they aid you in getting orders of magnitude more work done than you did previously while still leveraging the experience you've gained?
But... I think you were actually bemoaning the shift from numbers to names as a loss?
We don't know how brain exactly works, but I don't think we can now do some things better just because we are not using another function of our brains anymore.
Same as AI. Cool it makes you 5x as efficient at your job. But after a decade of using it, can you got back to 1x efficiency without it? Or are you just making the highly optimistic leap that you will retain access to the tech in perpetuity.
I don't have to remember most of them from today, so I simply don't. (I do keep a few current numbers squirreled away in my little pea brain that will help me get rolling again, but I'll probably only ever need to actually use those memories if I ever fall out of the sky and onto a desert island that happens to have a payphone with a bucket of change next to it.)
On a daily, non-outlier basis, I'm no worse for not generally remembering phone numbers. I might even be better off today than I was decades ago, by no longer having to spend the brainpower required for programming new phone numbers into it.
I mean: I grew up reading paper road maps and [usually] helping my dad plan and navigate on road trips. The map pocket in the door of that old Chevrolet was stuffed with folded maps of different areas of the US.
But about the time I started taking my own solo road trips, things like the [OG] MapBlast! website started calculating and charting driving directions that could be printed. This made route planning a lot faster and easier.
Later, we got to where we are today with GPS navigation that has live updates for traffic and road conditions using systems like Waze. This has almost completely eliminated the chores of route planning and remembering directions (and alternate routes) from my life, and while I do have exactly one road map in my car that I do keep updated I haven't actually ever used it for anything since 2008 or so.
And am I less of a person today than I was back when paper maps were the order of the day? No, I don't think that I am -- in fact, I think these kinds of tools have made me much more capable than I ever was.
We call things like this "progress."
I do not yearn for the days before LLM any more than I yearn for the days before the cotton gin or the slide rule or Stack Overflow.
Anyway now as an adult I have to remember a lot of pin codes.
* Door to home * Door to office * Door to gf's place * Bank card #1 * Bank card #2 * Bank card #3 * Phone #1 * Phone #2
Honestly, I'm not sure this would account for most of the difficulty in learning. In my experience most of the difficulty involved in learning something involved a few missing pieces of insight. It often took longer to understand the few missing pieces than the rest of the topic. If they are accurate enough, LLMs are great for getting yourself unstuck and keep yourself moving. Although it has always been a part of the learning experience, I'm not sure frantically looking through hundreds of explanations for a missing detail is a better use of one's time than to dig deeper in the time you save.
Feeling confident to be able to shrug off blockers, that might otherwise turn exploration into a painful egg hunt for trivial unknowns, can easily mean the difference between learning and abandoning.
[0] https://byorgey.wordpress.com/2009/01/12/abstraction-intuiti...
"You don't want to over-rely on AI because a certain degree of "productive struggle" is essential for learning something deeply."
These two ideas are closely related and really just different aspects of the same basic frailty of the human intellect. Understanding that I think can really inform you about how you might use these tools in work (or life) and where the lines need to be drawn for your own personal circumstance.
I can't say I disagree with anything you said and think you've made an insightful observation.
In a world where everyone has a phone/calculator in their pocket, remembering how to do long division on paper is not worthwhile. If I ask you "what is 457829639 divided by 3454", it is not worth your time to do that by hand rather than plugging it into your phone's calculator.
In a world where AI can immediately produce any arbitrary 20-line glue script that you would have had to think about and remember bash array syntax for, there's not a reason to remember bash array syntax.
I don't think we're quite at that point yet but we're astonishingly close.
That being said I usually prefer to do something the long and manual way, write the process down sometimes, and afterwards search for easier ways to do it. Of course this makes sense on a case by case basis depending on your personal context.
Maybe stuff like crosswords and more will undergo a renaissance and we'll see more interesting developments like Gauguin[0] which is a blend of Sudoku and math.
The difference is that you can trust a good calculator. You currently can't trust AI to be right. If we get a point where the output of AI is trustworthy, that's a whole different kind of world altogether.
AI that writes a bash script doesn't need to be better than an experienced engineer. It doesn't even need to be better than a junior engineer.
It just needs to be better than Stack Overflow.
That bar is really not far away.
It was not about whether AI is useful or not.
My original point about not needing fundamentals would obviously require AI to, y'know, not hallucinate errors that take three hours to debug. We're clearly not there yet. The original goalposts remain the same.
Since human conversations often flow from one topic to another, in addition to the goal post of "not needing fundamentals" in my original post, my second post introduced a goalpost of "being broadly useful". You're correct that it's not the same goalpost as in my first comment, which is not unexpected, as the comment in question is also not my first comment.
Well that is because you ask a calculator to divide numbers. Which is a question that can be interpreted in only one way. And done only one way.
Ask the smallest possible for loop and if loop that AI can generate now you have the pocket calculator equivalent of programming.
Is it? What is 5/2+3?
“Which is a question that can be interpreted in only one way. And done only one way.”
The question for calculators is then the same as the question for LLMs: can you trust the calculator? How do you know if it’s correct when you never learned the “correct” way and you’re just blindly believing the tool?
This is just splitting hairs. People who use calculators interpret it in only one way. You are making a different and a more broad argument that words/symbols can have various meanings, hence anything can be interpreted in many ways.
While these are fun arguments to be made. They are not relevant to practical use of the calculator or LLMs.
No. There being "more than one way" to interpret implies the meaning is ambiguous. It's not.
There's not one incorrect way to interpret that math statement, there are infinite incorrect ways to do so. For example, you could interpret as being a poem about cats.
I found a bug in the ios calculator in the middle of a masters degree exam. The answer changed depending on which way the phone was held. (A real bug - I reported it and they fixed it). So knowing the expected result matters even when using the calculator.
This is basically how AI research is conducted. It's alchemy.
And if it spits out 15,395,143 I hope you remember enough math to know that doesn’t look right, and how to find the actual answer if you don’t trust your calculator’s answer.
Using AI to learn things is useful, because it helps you get terminology right, and helps you Google search well. For example say you need to know a Windows API, you can describe it snd get the name. Then Google how that works.
As an experienced user you can get it to write code. You're good enough to spot errors in the vote and basically just correct as you go. 90% right is good enough.
It's the in-between space which is hardest. You're an inexperienced dev looking to produce, not learn. But you lack the experience and knowledge to recognise the errors, or bad patterns, or whatever. Using AI you end up with stuff that's 'mostly right' - which in programming terms means broken.
This experience difference is why there's so much chatter about usefulness. To some groups it's very useful. To others it's a dangerous crutch.
So yes, it's pretty useless for me to manually divide arbitrarily large numbers. But it's super useful for me to be able to reason around fractions and how that division plays out in practice.
Same goes for bash. Knowing the exact syntax is useless, but knowing what that glue script does and how it works is essential to understanding how your entire program works.
That's the piece I'm scared of. I've seen enough kids through tutoring that just plug numbers into their calculator arbitrarily. They don't have any clue when a number is off by a factor of 10 or what a reasonable calculation looks like. They don't really have a sense for when something is "too complicated" either, as the calculator does all of the work.
The neat thing about AI generated bash scripts, would be that the AI can comment their code.
So the user can 1) check if the comment for each step match what they expect to be done, and 2) have a starting point to debug if something goes wrong.
Well that's not how LLMs work. Don't use an LLM to do thinking for you. You use LLMs to work for you, while you tell(after thinking) it what's to be done.
Basically things like-
. Attach a click handler to this button with x, y, z params and on click route it to the path /a/b/c
. Change the color of this header to purple.
. Parse the json in param 'payload' and pick up the value under this>then>that and return
etc. kind of dictation.
You don't ask big questions like 'Write me a todo app', or 'Write me this dashboard'. Those are too broad questions.
You will still continue to code and work like you always have. Except that you now have a good coding assistant that will do the chore of typing for you.
In fact if you are familiar with keyboard macros, in both vim and emacs you can do a lot of text heavy lifting tasks.
I don't see these as opposing traits. One can use both the goodness of vim AND LLMs at the same time. Why pick one, when you can pick both?
I mostly use manuals, books, and the occasional forum searches. And the advantage is that you pick surrounding knowledge. And more consistent writing. And today, I know where some of the good stuff are. You're not supposed to learn everything in one go. I built a knowledge map where I can find what I want in a more straightforward manner. No need to enter in a symbiosis with an LLM.
One can do pick and use multiple good things at a time. Using vim doesn't mean, I won't use vscode, or vice versa. Or that if you use vscode code you must not use AI with it.
Having access to a library doesn't mean, one must not use Google. One can use both or many at one time.
There are no rules here, the idea is to build something.
- Poor editor / editing setup
- Poor programming language and knowledge thereof
- Poor APIs and/or knowledge thereof
Mankind has worked for decades to develop elegant and succinct programming languages within which to express problems and solutions, and compilers with deterministic behaviour to "do the work for us".
I am surprised that so many people in the software engineering field are prepared to just throw all of this away (never mind develop it further) in exchange for using a poor "programming language" (say, english) to express problems clumsily in a roudabout way, and then throw away the "source code" (the LLM prompt) entirely such to simply paste the "compiler output" (code the LLM spewed out which may or may not be suitable or correct) into some heterogenous mess of multiple different LLM outputs pasted together in a codebase held together by nothing more than the law of averages, and hope.
Then there's the fun fact that every single LLM prompt interaction consumes a ridiculous amount of energy - I heard figures such as the total amount required to recharge a smartphone battery - in an era where mankind is racing towards an energy cliff. Vast, remote data centres filled with GPUs spewing tonnes of CO₂ and massive amounts of heat to power your "programming experience".
In my opinion, LLMs are a momentous achievement with some very interesting use-cases, but they are just about the most ass-backwards and illogical way of advancing the field of programming possible.
I wonder whether this is because people don't know about it or because they simply don't care...
But I, for one, try to use AI as sparingly as possible for this reason.
Change your search engine to one that doesn't include AI-generated answers. If none exist any more, all of Google's customers could write to them telling them that they don't want this feature and are switching away from them because of it, etc.
I know that internet-scale search is perhaps a bad example because it's so extremely difficult and expensive to build and run, but ultimately the choice is in the consumers' hands.
If the market makes it clear that there is a need for a search engine without LLM-generated answers at the top, somebody will provide one! It's complacency and acceptance that leads apparently-delusional companies to just push features and technologies that nobody wants.
I feel much the same way about the ridiculous things happening with cars and the automotive sector in general.
I'm using Cursor btw. It's almost a different form factor compared to something like GH copilot.
I think it's also worth noting that I'm using TypeScript with a functional programming style. The state of the program is immutable and encoded via strongly typed inputs and outputs. I spend (mental) effort reifying use-cases via enums or string literals, enabling a comprehensive switch over all possible branches as opposed to something like imperative if statements. All this to say, that a lot of the code I write in this type of style can be thought of as a kind of boilerplate. The hard part is deciding what to do; effecting the change through the codebase is more easily ascertained from a small start.
I too love functional programming, and I'm talking about Haskell-levels of programming efficiency and expressiveness here, BTW.
This is quite a different use case than those presented by the post I was replying to though.
The Go programming language has this mantra of "a little bit of copy and paste is better than a little bit of dependency on other code". I find that LLM-derived source code takes this mantra to an absurd extreme, and furthermore that it encourages a though pattern that never leads you to discover, specify, and use adequate abstractions in your code. All higher-level meaning and context is lost in the end product (your committed source code) unless you already think like a programmer _not_ being guided by an LLM ;-)
We do digress though - the original topic is that of LLM-assisted writing, not coding. But much of the same argument probably applies.
I honestly don’t think we’re far out from people being able to write “Write me a todo app” and then telling it what changes to make after.
I recently switched back to software development from professional photography and I’m not sure if that’s a mistake or not.
They often can not generate relatively trivial code When they do, they can not explain that code. For example, I was trying to learn socket programing in C. Claude generated the code, but when I stared asking about stuff, it regressed hard. Also, often the code is more complex than it needs to be. When learning a topic, I want that topic, not the most common relevant code with all the spagheti used on github.
For other subjects, like dbms, computer network, when asking about concepts, you better double check, because they still make stuff up. I asked ChatGPT to solve prev year question for dbms, and it gave a long, answer which looked good on surface. But when I actually read through because I need to understand what it is doing, there were glaring flaws. When I point them out, it makes other mistakes.
So, LLMs struggle to generate concise to the point code. They can not explain that code. They regularly make stuff up. This is after trying Claude, ChatGPT and Gemini with their paid versions in various capacities.
My bottom line is, I should NEVER use a LLM to learn. There is no fine line here. I have tried again and again because tech bros keep preaching about sparks of AGI, making startup with 0 coding skills. They are either fools or genius.
LLMs are useful strictly if you already know what you are doing. That's when your productivity gains are achieved.
LLMs hallucinate.
That's true and by how they are made it cannot be false.
Anything they generate cannot bw trusted and have to be verified.
They are good at generating fluff but i wouldn't rely on them for anything.
Ask at that temperature glass melts and you will get 5 different answers, noone true.
GPT4o
https://chatgpt.com/share/673578e7-e34c-8006-94e5-7e456aca6f...
GPT4o
https://chatgpt.com/share/67357941-0418-8006-a368-7fe8975fbd...
GPT4o-mini
https://chatgpt.com/share/673579b1-00e4-8006-95f1-6bc95b638d...
Glass is not a pure element so that temperature is the "production temperature" but as an amorphous material it ""melts"" in the way a plastic material ""melts"" and can be worked at temperature as low as 5-700c.
I feel like without a specification the answer is wrong by omission.
What "melts" means when you are not working with a pure element is pretty messy.
This came out in a discussion for a project with a friend too obsessed with GPT (we needed that second temperature and i was "this can't be right....it's too high")
If the premise is that you first need to learn an alien psychology, that's quite the barrier for a student.
One with ChatGPT about dbms questions and one with claude about socket programming.
Looking back are some questions a little stupid ? Yes. But affcourse they are! I am coming with zero knowledge trying to learn how the socket programming is happening here ? Which functions are begin pulled from which header files, etc.
In the end I just followed along with a random youtube video. When you say, you can get LLM to do anything, I agree. Now that I know how socket programming is happening, for next question in assignment about writing code for crc with socket programming, I asked it to generate code for socket programming, made the necessary changes, asked it generate seperate function for crc, integrated it manually and voila, assignment done.
But this is the execution phase, when I have the domain knowledge. During learning when the user asks stupid questions and the LLM's answer keep getting stupider, then using them is not practical.
Also Im surprised you even got a usable answer from your first question asking for a socket program if all you asked was the bold part. I'm a human (pretty sure at least) and had no idea how to answer the first bold question.
I had already established from previous chat that upon asking for server.c file, llm's answer was working correctly. Rest of the sentence is just me asking it to use and not use certain header files which it uses by default when you ask it to generate server.c file.Thats because from docs of <sys/socket.h>, I thought it had all relevant bindings for the socket programming to work correctly.
I would say, the sentence logically makes sense.
So you did not convince me that LLMs are not working (on the contrary), but I did learn something today! thanks for that.
I got bullied at a conference (I was in the audience) because when the speaker asked me, I said AI is useless for my job.
My suspicion is that these kind of people basically just write very simple things over and over and they have 0 knowledge of theory or how computers work. Also their code is probably garbage but it sort-of works for the most common cases and they think that's completely normal for code.
I don't dismiss it as completely useless, because it pointed me in the correct direction a couple times, but you have to double-check everything. In a way, it might help me learn stuff, because I have to read its output critically. From my perspective, the success rate is a bit above 0, but it's nowhere close to "magical" at all.
YMMV, but I didn't ride a bike for 10ish years, and then got back on and I was happily riding quickly after. I also use zsh and ctrl+r for every Linux command, but I can still come up with the command of I need to, just, slowly. Ive overall found that if I learn a thing, it's learnt. Stuff I didn't learn in university, but passed anyways, like Jacobians, I still don't know, but I've got the gyst of it. I do keep getting better and better at the banjo the less I play it, and getting back to the drumming plateau is quick.
Maybe the drumming plateau is the thing? You can quickly get back to similar skill levels after not doing the thing in a while, but it's very hard to move that plateau upwards
You learnt the bike and practiced it rigorously before stoppping for 10 years, and you're able to pick it up. You _knew_ the commands because you learned the them the manual/hard way, and then used assistance to to do it for you..
Now, do you think it will apply to someone who begins their journey with LLMs and doesnt quite develop the skill of "Does this even look right?!", and says to themselves "if LLMs could write this module why bother learning what that thing actually does?" and then get bitten by it due to LLM hallucinations and stare like a deer in headlights.
I’m a little worried about developers turning to LLMs instead of official documentation as the first thing they do. I still view LLMs as mostly being fancy auto-complete with some automation capabilities. I don’t think they are very good at teaching you things. Maybe they are better than Google programming, but the disadvantage LLMs have seem to be that our employees tend to trust the LLMs more than they would trust what they found on Google. I don’t see an issue with people using LLMs on fields they aren’t too experienced with yet however. We’ve already seen people start using different models to refine their answers, we’ve also seen an increase in internal libraries and automation in place of external tools. Which is what we want, again because we’re under some heavy EU regulations where even “safe” external dependencies are a bureaucratic nightmare.
I really do wonder what it’ll do to general education though. Seeing how terrible and great these tools can be from a field I’m an expert in.
Already do — that's what "brain training" apps; consumer EdTech like Duolingo and Brilliant; and educational YouTube and podcasts like 3blue1brown, Easy German, ElectroBOOM, Overly Sarcastic Productions all are.
I just liked learning as far back as my memories go, so I've been jumping into researching topics because I wanted to know things.
Universe just gave me course materials, homework, and a lot of free time.
Jobs took up a lot of time, but didn't make learning meaninfgully different than it always has been for me.
> AI makes me at least 2x more efficient at my job. It seems irrational not to use it
Fair, but there is a corollary here -- the purpose of learning at least in part is to prepare you for the workforce. If that is the case, then one of the things students need to get good at is conversing with LLMs, because they will need to do so to be competitive in the workplace. I find it somewhat analogous to the advent of being able to do research on the internet, which I experienced as an early 90s kid, where everyone was saying "now they won't know how to do research anymore, they won't know the Dewey decimal system, oh no!". Now the last vestiges of physical libraries being a place where you even can conduct up-to-date research on most topics are crumbling, and research _just is_ largely done online in some form or another.
Same thing will likely happen with LLMs, especially as they improve in quality and accuracy over the next decade, and whether we like it or not.
The first point I like to make is that the purpose of having students do tasks is to foster their development. That may sound obvious, but many people don't seem to take notice that the products of student activities are worthless in themselves. We don't have students do push-ups in gym class to help the national economy by meeting some push-up quota. The sole reason for them is to promote physical development. The same principle applies to mental tasks. When considering LLM use, we need to be looking at its effects on student development rather than on student output.
So, what is actually new about LLM use? There has always been a risk that students would sometimes submit homework that was actually the work of someone else, but LLMs enable willing students to do it all the time. Teachers can adapt to this by basing evaluation only on work done in class, and by designing homework to emphasize feedback on key points, so that students will get some learning benefit even though a LLM did the work.
Completely following this advice may seem impossible, because some important forms of work done for evaluation require too much time. Teachers use papers and projects to challenge students in a more elaborate way than is possible in class. These can still be used beneficially if a distinction is made between work done for learning and work done for evaluation. While students develop multiple skills while working on these extended tasks, those skills could be evaluated in class by more concise tasks with a narrower focus. For example, good writing requires logical coherence and rhetorical flow. If students have trouble in these areas, it will be just as evident in a brief essay as a long one.
The student's job is not to do everything the teacher says, it is to get through schooling somewhat intact and ready for their future. The sad fact is that many things we were forced to do in school were not helpful at all, and only existed because the teachers thought it was, or for no real reason at all.
Pretending that pedagogy has established and verified methodology that will result in a completely developed student, if only the student did the work as prescribed, is quite silly.
Teaching evolves with technology like every other part of society and it may come out worse or it may come out better, but I don't want to go back fountain pens and slide rules and I think in 20 years this generation won't look back on their education thinking they got a worse one than we did because they could cheat easier.
> many things we were forced to do in school were not helpful at all
I've never had to do push-ups since leaving school. It was a completely useless skill to spend time on. Gym class should have focused on lifting bags of groceries or other marketable skill.
So learning to recognise the phonics and blend them together may not be better for one pupil, but it is clearly better for most. This is what the curriculum and most teachers' classroom practice is all about.
American Secondary Education 45(2) Spring 2017 Examining Homework Bennett
If your field depends on underpowered studies run by people with marginal understanding of statistics, you can gather support for any absurd position.
You haven't made the argument that what they are practicing is valuable or effective.
I'm sure they get better at doing homework by doing a lot of homework, but do they develop any transferable skills?
It seems you are begging the question here.
Practice does not improve skills? You got to be kidding me! I didn't state that homework is effective in all contexts, but I firmly believe that practice is absolute necessary to improve any kind of skill. Some forms of practice is more effective than other in certain context. But you need practice to improve your skills. Otherwise, how do you propose to improve your skills? Dreaming?
Which doesn't mean I'm against physical activities in school, but educationally, it does appear to be a failure.
Personally what I’m doing is to push the weight back at the students. Every submission now requires a 5-minute presentation with an argumentation/defense against me as an opponent. Anyway it would take me around 10-15 min to correct their submission, so we’re just doing it together now.
I'm not an educator, but it seems to me like gippity would be better at analyzing a students paper than writing it in the first place.
Your prompt could provide the AI the marking criteria, or the rubric, and have it summarize how well the paper hits the important points.
(Jokes aside, I have an unhealthy, unstoppable need to feel proud of my work, so no I won’t do that. For now…)
You could take pride in a well crafted technology that could mark an assignment and provide feedback in far more detail that you yourself could ever provide given time constraints.
I asked my partner about it last night, she teaches at ANU and she made some joke about how variable the quality of tutor marking is. At least the AI would be impartial and consistent.
I have no idea how well an AI can assess a paper against a rubric. Might be a complete waist of time, but if there were some teachers out there who wanted to do some tests, I would be interested in helping set up the tests and evaluating the results.
I would say generally not, for two reasons. First, the teacher needs to know how the student is developing. To get a thorough understanding takes working through the student's output, not just checking a summary score. Second, the teacher needs to provide selective feedback, to focus student attention on the most important areas needing development. This requires knowledge of the goals of the teacher and the developmental history of the student.
I won't argue that LLM evaluation could never be applied usefully. If the task to be evaluated is simple and the skills to be learned are straightforward, I imagine that it could benefit the students of some grossly overloaded teacher.
I think often AI sceptics go too far in assuming users blindly use the AI to do everything (write all the code, write the whole essay). The advice in this article largely mirrors - by analogy - how I use AI for coding. To rubber duck, to generate ideas, to ask for feedback, to ask for alternatives and for criticism.
Usually it cannot write the whole thing (essay, program )in one go, but by iterating bewteen the AI and myself, I definitely end up with better results.
I was trying to get it write robot framework code earlier and it was remarkably terrible. I would point out an obvious problem, it would replace the code with something even more spectacularly wrong.
When I pointed out the new error, it just gave me the exact same old code.
This happened again and again.
It was almost entirely useless.
Really showed how the sausage is made, this generation of AI is just regurgitation of patterns it stole from other people.
ChatGPT 4 does much better with corrections, as does Claude. 4o is a pox.
Users are not a monolithic group. Some users/students absolutely use AI blindly.
There are also many, many ways to use AI counterproductively. One of the most pernicious I have noticed is users who turn to AI for the initial idea without reflecting about the problem first. This removes a critical step from the creative process, and prevents practice of critical and analytical thinking. Struggling to come up with a solution first before seeing one (either from AI or another human) is essential for learning a skill.
The effect is that people end up lacking self confidence in their ability to solve problems on their own. They give up much too easily if they don't have a tool doing it for them.
I guess it's just like many tools, they can be used well or badly, and people need to learn how to use the well to get value from them
Then again, I bet some of these people were doing the same with Google in the past, landing on some low quality SEO article that sounds close enough.
Even earlier, I suppose they were asking somebody working for them to figure it out - likely somebody unqualified who babbled together something plausible sounding.
Technology changes, but I'm not sure people do.
Not a problem for me, I work on prompt development, I can't ask GPT how to fix its mistakes because it has no clue. Prompting will probably be the last defense of reasoning, the only place where you can't get AI help.
> One of the most pernicious I have noticed is users who turn to AI for the initial idea without reflecting about the problem first I've been doing that and it usually doesn't work. How can you ask an ai to solve a problem you don't understand at all ? More often than not, when you do that the ai throws a dumb response and you get back to thinking about how to present the problem in a clear way which makes you understand it better.
So you still end up learning to analyze a problem and solving it. But I can't tell if the solution comes up faster or not nor if it helps learning or not.
Sometimes I tried to be transparent in having used chatgpt like for minor stuff, and I got pooled into being a lazy fuck who submitted slob.
We are probably going to see an end to the bearish wave of ai and a correction towards reasonable AI use.
No, it cannot solve new math problems and is not as smart as Alakazam, but it CAN format your citations and make you a cup of coffee.
I remember when I was a student that my teachers would complain that we did “compilation-based programming” meaning we hit “compile” before we thought about the code we wrote, and let the compiler find the faults. ChatGPT is the new compiler: it creates results so fast that it’s literally more worth it to just turn them in and wait for the response than bothering to think about it. I’m sure a large amount of these students are passing their courses due to simple statistics (I.e. teachers being unable to catch every problematic submission).
For example, many teachers have fed student essays into ChatGPT and asked "did AI write this?" or "was this plagiarized" or similar, and fully trusting whatever the AI tells them. This has led to some false positives where students were wrongly accused of cheating. Of course a student who would cheat may also lie about cheating, but in a few cases they were able to prove authorship using the history feature built into Google docs.
Overall though I'm not super worried because I do think most people are learning to be skeptical of LLMs. There's still a little too much faith in them, but I think we're heading the right direction. It's a learning process for everyone involved.
Now, in the UK students sit 2 different exams: one where calculators are forbidden and one where calculators are permitted (and encouraged). The problems for the calculator exam are chosen so that the candidate must do a lot of problem solving that isn't just computation. Furthermore, putting a problem into a calculator and then double checking the answer is a skill in itself that is taught.
I think the same sort of solution will be needed across the board now - where students learn to think for themselves without the technology but also learn to correctly use the technology to solve the right kinds of challenges and have the skills to check the answers.
People on HN often talk about ai detection or putting invisible text in the instructions to detect copy and pasting. I think this is a fundamentally wrong approach. We need to work with, not against the technology - the genie is out of the bottle now.
As an example of a non-chatgpt way to evaluate students, teachers can choose topics chatgpt fails at. I do a lot of writing on niche topics and there are plenty of topics out there where chatgpt has no clue and spits out pure fabrications. Teachers can play around to find a topic where chatgpt performs poorly.
It's scary to see the reversal of the burden of proof becoming more accepted.
With all the concern over AI, it's being used _against recommendations_ to detect AI usage? [0][1]
So while the concern for using AI is founded, teachers are so mistaken at understanding what it is and the tech around is that they are using AI in areas it's publicly acknowleded it doesn't work. That detracts from any credibility the teachers have about AI usage!
[0] https://openai.com/index/new-ai-classifier-for-indicating-ai... openai pulled their AI classifier [1] https://mitsloanedtech.mit.edu/ai/teach/ai-detectors-dont-wo...
> That detracts from any credibility the teachers have about AI usage!
I love teachers, but they shouldn't have any credibility about AI usage in the first place unless they have gained that in the same way the rest of us do. As authority figures, IMHO they should be held to an even higher standard than the average person because decisions they make have an out-sized impact on another person.
They don't even know how to write a prompt, or in some cases even what "writing a prompt" means. They just paste the assignment in as a prompt and copy the output.
They then feed that as input to some app that detects chatgpt papers and change the wording until it flows through undetected.
One student told me that, for good measure, she runs it twice and picks and chooses sentences from each-- this apparently is a speedup to beating the ai paper detector. There are probably other arbitrarily-chosen patterns.
I've never heard of any of these students using it in any way other than holistic generation of the end product for an assignment. Most of them seem overconfident that they could write papers of similar quality if they ever tried. But so far, according to all of them, they have not.
For example she tends to ask it for outlines instead of the whole thing, mostly to beat "white page paralysis" and also because it often provides some aspect she might have overlooked.
She tends to avoid asking for big paragraphs because she doesn't trust it with facts and also dislikes editing out the "annoying" AI style or prompting for style rewrites. But she will feed it phrases from her own writing that get too tangled for simplification.
Also she will vary the balance of AI/own effort according to the nature of the task, her respect for the teacher or subject: Interesting work from a engaging lecturer? Light LLM touch or none. Malicious make-work or readable Lorem Ipsum when the point is the format of the thing instead of the content? AI pap by the tons for you. I find it healthy and mature.
I have however asked ChatGPT to cite sources for specific things, to varying success. Surprisingly, it returns sources that actually exist most of the time now. They often aren’t super helpful though because they either aren’t in my school’s library or are books rather than articles.
Ah, the illusion of knowledge..
Coming from an education system where writing lengthy prose and essays is expected for every topic from literature to mathematics, I can confidently say that, after not having actively practiced that form of writing for over a decade, I wouldn't be able to produce a paper of what was considered average-quality back then. It would take time, effort, and a few tries, despite years and years of previous practice. Even more so if the only medium in front of me would be a blank sheet of paper and a pen.
So to confidently claim you can produce something of high quality when you've never really done it before is.. ..misguided.
But in the end, perhaps not really different to the illusion knowledge one gets with google at its fingertips. Pull the plug, and you are left with nothing.
I've used LLMs MULTIPLE times during "academic" work, often for original idea generation. Never for full-on, actual writing.
Think of my usage as treating it as a tool that gives you a stem of an idea that you develop further on your own. It helps me persevere with the worst part of work: having to actually come up with an entire idea on my own.
And AI detection tools are still complete garbage as far as I can tell, a paper abstract I've written in front of one of my professors got flagged as 100% AI generated (while having no access to outside sources).
When I was young, I refused to learn geography because we had map applications. I could just look it up. I did the same for anything I could, offload the cognitive overhead to something better -- I think this is something we all do consciously or not.
That attitude seems to be the case for students now, "Why do I need to do this when an LLM can just do it better?"
This led us to the conclusion:
1. How do you construct challenges that AI can't solve? 2. What skills will humans need next?
We talked about "critical thinking", "creative problem solving", and "comprehension of complex systems" as the next step, but even when discussing this, how long will it be until more models or workflows catch up?
I think this should lead to a fundamental shift in how we work WITH AI in every facet of education. How can a human be a facilitator and shepherd of the workflows in such a way that can complement the model and grow the human?
I also think there should be more education around basic models and how they work as an introductory course to students of all ages, specifically around the trustworthiness of output from these models.
We'll need to rethink education and what we really desire from humans to figure out how this makes sense in the face of traditional rituals of education.
yes, I think this is critical. There's a slate star codex article "Janus Simulators" that explains this very well, that I rewrote to make more accessible to people like my mom. It's not hard to explain this to people, you just need to let them interact with a base model, and explore its quirks. It's a game, people are good at learning systems that they can get immediate feedback from.
Either these things are important to learn for their own sake or they aren’t. If the former, then nothing about these objectives needs changing, and if the latter then education itself will be a waste of time.
https://www.cs.ucdavis.edu/~koehl/Teaching/ECS188/PDF_files/...
And re-skimming it just now I noticed the following eerie line:
> There was the button that produced literature.
Wild that this was written in 1903.
Which is ironic, because geography isn’t about memorizing maps
Tell that to drummers
Your drumming will be "melodic" if you do so
Perhaps they changed the curriculum since the 90s
Swedish schools gets a makeover every time we change government. It's one of those things they just have to "fix" when they get to power.
In Germany the subject is called "Erdkunde" which would translate to geology. And this term is, I assume, more appropriate as it isn't just about what is where but also about geological history and science and how volcanoes work and how to read maps and such.
This rustles a TON of feathers to even broach as a topic, but it's the only correct one. The AI engineer will eat everything, including your educational system, in 5-10 years. You can either swim against the current and be ate by the sharks or swim with it and survive longer. I'll make sure my kids are learning about AI related concepts from the very beginning.
This was also the correct way to handle it circa the calculator era. We should have made most people get very good at using calculators, and doing "computational math" since that's the vast majority of real world math that most people have to do. Imagine a world where Statistics was primarily taught with Excel/R instead of with paper. It'd be better, I promise you!
But instead, we have to live in a world of luddites and authoritarians, who invent wonderful miracle tools and then tell you not to use them because you must struggle. The tyrant in their mind must be inflicted upon those under them!
It is far better to spend one class period, teaching the rote long multiplication technique, and then focus on word problems and applications of using it (via calculator), than to literally steal the time of children and make them hate math by forcing them to do times tables, again and again. Luddites are time thieves.
But I agree that the “learning is pain” is just not my experience.
I strongly disagree. I've seen the impact of students who used calculators to the point they limited their ability to do math. When presented with math in other fields, ones where there isn't a simple equation to plug into a calculator, they fail to process the math because they don't have the number sense. Things like looking over a few experiments in chemistry and looking for patterns become a struggle because noticing the implication that 2L of hydrogen and 1L of oxygen create 2L of water vapor being the same as 2 parts hydrogen plus 1 part oxygen creates 2 part water, which then means that 2 molecules of hydrogen plush 1 molecule of oxygen create 2 molecules of water, all of this implying that 1 molecule of oxygen has to be made of some even number of oxygen atoms so that it can be split in half to make up the 2 water molecules which must have the same amount of oxygen atoms in both. (This is part of a larger series of problems relating to how chemist work out the empirical formula in the past, eventually leading to the molecular formula, and then leading to discovering molecular weight and a whole host of other properties we now know about atoms.)
Without these skills, they are able to build the techniques needed to solve newer harder problems, much less do independent work in the related fields after college.
>Imagine a world where Statistics was primarily taught with Excel/R instead of with paper. It'd be better, I promise you!
I had to take two very different stats classes back in college. One was the raw math, the other was how to plug things into a tool and get an answer. The one involving the tool was far less useful. People learned how to use the tool for simple test cases, but there was no foundation for the larger problems or critiquing certain statistical methodologies. Things like the underlying assumptions of the model weren't touched, meaning students would have had a much harder time when dealing with a population who greatly differed from the assumption.
Rote repetition may not be the most efficient way to learn something, but that doesn't mean avoiding learning it and letting a machine do it for you is better.
I expect the same will happen with math and numbers. To be fair you said primarily so you did not imply to do completely away with the paper. I am not certain though that we can do completely away with at least some pain. All the skills I acquired usually came with both frustration and joy.
I am all for trying new methods to see if we can do something better. I have no proof either way though that going 90% excel would help more people learn math. People will run both experiments and we will see how it turns out in 20 years.
This does not really lend great credence to the rest of your argument. Yes, Linkedin is hyping the latest job trend. But study after study shows that the bulk of engineers are not doing ML/AI work, even after a year of Linkedin putting up those banners -- and if there were even 2 ML/AI jobs at the start of such a period, then 10% week-over-week growth would imply that the entire population of the earth was in the field.
Clearly that is not the case. So either those banners are total lies, or your interpretation of exponential growth (if something grows exponentially for a bit, it must keep growing exponentially forever) is practically disjointed from reality. And at that point, it's worth asking: what other assumptions about exponential growth might be wrong in this world-view?
Perhaps by "AI engineer" you (like many publications nowadays) just mean to indicate "someone who works with computers"? In that case I could understand your point.
And I get it. Pitchers who go pro get paid a lot and aren't allowed to use machines, so that's a hell of an incentive, but the vast majority of kids who ever pick up a baseball are never going to go pro, are never even going to try to go pro, and just enjoy playing the game.
It's fair to say many, if not most, students don't enjoy writing the way kids enjoy playing games, but at the same time, the point was mostly never mastering the five paragraph thesis format anyway. The point was learning to learn, about arbitrary topics, well enough to the point that you could write a reasonably well-argued paper about it. Even if a machine can do the writing for you, it can't do the learning for you. There's either value in having knowledge in your own brain or there isn't. If there isn't, then there never was, and AI didn't change that. You always could have paid or bullied the smarter kids into doing the work for you.
Sure, but watch out for the game with a pitching machine, a hitting machine, and a running machine.
I do think there is a good analogy here - if you're making an app for an idea that you find important, all of the LLM help makes sense. You're trying to do a creative thing and you need help in certain parts.
> You always could have paid or bullied the smarter kids into doing the work for you.
Don't overlook the ease of access as being a major contributor. Paying $20/month to have all of your work done is still going to prevent students from using it. Paying $200/month would for sure bring the numbers of student users near to zero. When it's free you'll see more people using it. Just like anything else.
Totally agree with your main points.
Just learning a thing doesn't mean you can communicate it
1. What can tools do better now that no human could hope to compete with?
2. Which other tasks are likely to remain human-led in the near term?
3. For the areas where tools excel, what is the optimum amount of background understanding to have?
E.g. you mention memorizing maps. Memorizing all of the countries and their main cities is probably not very optimal for 99.999%+ of people vs referencing a map app. At the same time needing to pull up a map for any mention of a location outside of "home" is not necessarily optimal just because the map will have it. And of course the other things about maps in general (types, features, limitations, ways to use them, ways they change) outside of a particular app implementation that would go along with general geography.
I don't really care to memorize (which was most of the coursework) things which I can just easily look up. Maybe geography in the south was different than how it was taught elsewhere though.
In 20 years we'll be able to tell this in a stereotypically old geezer way: "You kids have it easy, back in my day we had to wait for an actual human to reply to our daft questions.. and sometimes nobody would bother at all!"
This is certainly useful to a point, and I don't recommend memorizing a lot of trivia, but it's easy to go too far with it. Having a basic mental model about many aspects of the world is extremely important to thinking deeply about complex topics. Many subjects worth thinking about involve interactions between multiple domains and being able to quickly work though various ideas in your head without having to stop umpteen times can make a world of difference.
To stick with the maps example, if you're reading an article about conflict in the Middle East it's helpful to know off the top of your head whether or not Iran borders Canada. There are plenty of jobs in software or finance that don't require one to be good at mental math, but you're going to run into trouble if you don't at least grok the concept of exponential growth or have a sense for orders of magnitude.
This is the opposite of deep knowledge, this is API knowledge at best.
Perhaps, but in the case that you are I think it's a stretch to say that the only utility of this is 'indoctrination' or 'understanding this. other forced meme'. The point is that lookups (even to an AI) cost time, and if you have to do one for every other line in a document, you will either end up spending a ton of time reading, or (more likely) do an insufficient number of lookups and come away with a distorted view of the situation. This 'baseline' level of knowledge IMO is a reasonable thing to expect for any field, not 'indoctrination' in anything other than the most diluted sense of the term.
Education is not a way to memorize a lot of knowledge, but a way to train your brain to recognize patterns and to learn. Obviously you need some knowledges too, but you generally dont need to be an expert, only to have "basic" knowledges.
Studying different domains allow to learn some different knowledges but also to learn new way of thinking.
For example : geography allows you to understand geopolitic and often sociology and history. And urban city design. And war strategy. And architecture...
So, when students are using LLM (and it's worst for children), they're missing on training their brain (yes... they get dumber) and learning basic human knowledge (so more prone to any fake news, even the most obvious)
Humans must use what the AI doesn't have - physicality. We have hands and feet, we can do things in the world. AI just responds to our prompts from the cloud. So the human will have to test ideas in reality, to validate, do experiments. AI can ideate, we need to use our superior access and life-long context to help it keep on the right track.
We also have another unique quality - we can be punished, we are accountable. AI cannot be meaningfully punished for wrongdoing, what can you do to an algorithm? But a human can assume responsibility for an AI in critical scenarios. When there is a lot of value at stake we need someone who can be accountable for the outcome.
Really? I didn't invent ChatGPT or anything like that, but I work in tech, I love science, maths, and learning in general, but I hated school. I found school to make the most interesting things boring. I felt it was all about following a line and writing too many pages of stuff. Maybe I am wrong, but it is certainly how I felt back then, and I am sure many people at OpenAI felt this way.
The school system is not great for atypical profiles, and most of the geniuses who are able to come up with revolutionary ideas are atypical. Note that I don't mean that if you are atypical and/or hate school then you are a genius, or that geniuses in general hate school, but I am convinced that among well educated people, geniuses are more likely to hate school.
The way they actually use is to get ChatGPT to generate ALL their homework and submit that. And sometimes take home exams too. And the weird thing is that some professors are perfectly cool with it.
I am starting to question whether the cost of going to a place of higher learning is worth it.
The same thing was true for probably me when I was studying geography and history in my high school years since they were taught largely by a collection of trivia knowledge that I did not find interesting. I would have used chatgpt and be done rather than studying them. But, when I took the courses that covered the same topics in history in my university, it was more enjoyable because the main instructor was covering the topic to tell a story in a more engaging manner (e.g. he was imitating some of the historical figures, it was very funny :))
Now it's up to you to share it with your kid and convince them they shouldn't cheat themselves out of an education by offloading the learning part to an LLM.
This doesn't change the value provided by the institution they're enrolled in unless the teachers are offloading their jobs to LLMs in a way that's detrimental to the students.
Cheating has been and will always be a thing.
> Similarly, it’s important to be open about how you use ChatGPT. The simplest way to do this is to generate shareable links and include them in your bibliography . By proactively giving your professors a way to audit your use of AI, you signal your commitment to academic integrity and demonstrate that you’re using it not as a shortcut to avoid doing the work, but as a tool to support your learning.
Would it be a viable solution for teachers to ask everyone to do this? Like a mandatory part of the homework? And grade it? Just a random thought...
But, sharing links for helping teachers understand your prompting is great
Why is it not a source? I think that it is not if "source" means "repository of truth," but I don't think that's the only valid meaning of "source."
For example, if I were reporting on propaganda, then I think that I could cite actual propaganda as a source, even though it is not a repository of truth. Now maybe that doesn't count because the propaganda is serving as a true record of untrue statements, but couldn't I also cite a source for a fictional story, that is untrue but that I used as inspiration? In the same way, it seems to me that I could cite ChatGPT as a source that helped me to shape and formulate my thoughts, even if it did not tell me any facts, or at least if I independently checked the 'facts' that it asserted.
That's "the devil's I," by the way; I am long past writing school essays. Although, of course, proper attribution is appropriate long past school days, and, indeed, as an academic researcher, I do try my best to attribute people who helped me to come up with an idea, even if the idea itself is nominally mine.
But there is, I think, a big gap between "it is not a source to cite" from your original post, and "it shouldn't be a moral requirement" in this one. I think that, while not every utterance should be annotated with references to every person or resource that contributed to it, there is a lot of room particularly in academic discourse for acknowledging informal contributions just as much as formal ones.
These are not the uses with which I am familiar—as Fomite says in a sibling comment, I am used to referring to citing personal communications; but, if you are using "cite" to mean only "produce as a reproducible testament to truth," and "source" only as "something that reproducibly demonstrates truth," which is a distinction whose value I can acknowledge making even if it's not the one I am used to, then your argument makes more sense to me.
To ask everyone to use ChatGPT, or to ask everyone to document their use of ChatGPT? I don't think the former is reasonable unless it's specifically the point of the class, and I believe that the latter is already done (by requirements to cite sources), though, as often happens, rapid technological developments mean that people don't think of ChatGPT as a source that they are required to cite like any other.
seems hard to fake that. and you could randomly quiz them on it .
We grade them in terms of factually correct statements, "I suppose" statements (yes, I've worked on influenza, but that's not what I'm best known for), and outright falsehoods.
Thus far none of them have gotten it right - illustrating at least that students need the skills to fact check their output.
I also remind them that they have two major oral exams, and failing them is not embarrassing, it's catastrophic.
Failing either is most certainly catastrophic.
Are there any students here who started uni just before LLM's took off and are now finishing their degrees? Have you noticed much change in how your classes are taught?
I’d argue the bar will be lower and lower. Yeah those who want can learn more in less time. But those who don’t - will learn much less.
I know a hiring manager who asks his (engineering) candidates what is 20% of 20,000? It's amazing how many engineers are completely unable to do this without a calculator. He said they often cry. Of course, they're all "no hire".
How did they get a degree, one wonders?
To quickly calc 10% just multiply the number by 0.1 which you can do by moving the decimal point one place 20,000.00 => 2,000.000 then it is easy to double that number.
to get 4,000.
17% for example is 1.7 x 10%
in this case 1.7 x 2,000 = 3,400
You mostly have common equivalences like this in your memory and you can be faster than computing the actual thing with arithmetic. Or have good approximations.
And yes, it's incredibly useful in enabling recognizing when your calculator gives a bogus result because you made a keyboarding error. When you've got zero feel for numbers, you're going to make bad engineering decisions. You'll also get screwed by car dealers every time, and contractors. You won't know how far you can go with the gas in your tank.
It goes on and on.
Calculators are great for getting an exact final answer. But you'd better already know approximately what the answer should be.
Humans are much better at pattern matching than computation, so the safest solution is probably to just double check if you've typed in the right numbers.
It might be counterintuitive, but the cheaper (and therefore successful) solution will always be more technological integration, not less.
In this case, better speech recognition, so the user doesn't have to type the numbers anymore, and an LLM middleman that's aware of the real-world context of the question, so the user can be asked if he's sure about the number before it gets passed to the calculator.
Instead of paper exams asking students "find the bug" or "implement a short function", they get a takehome exam where they have to write tests, integrate their project into a CI pipeline, use version control, and implement a dropbox-like system in Rust, which we expect to have a good deal of functionality and accompanying documentation.
I tell them go ahead and use whatever they want. It's easier than policing their tools. If they can put it together, and it works, and they can explain it back to me, then I'm satisfied. Even if they use ChatGPT it'll take a great deal of work and knowledge to get running.
If ChatGPT suddenly is able to put a project like that together, then I'll ask for even more.
To put it another way, modern high school level math classes disadvantage students who want to learn without using a calculator, but it would be quite odd to suggest that we should exclude calculators from math curricula as a result.
That wouldn't be odd at all. Calculators have no place in a math class. You're there to learn how to do math, not how to get a calculator to do math for you.
For example you claim that addition flashcards and times tables are invaluable, but you don't specify a base, in base 2 you have 4 addition flashcards, in base 100 you have 10,000, clearly understanding addition isn't related to the base, but flashcards increase as base increases, thus there is a relation, implying of course that understanding addition isn't related to the number of addition flashcards you understand. Oh but of course they aren't invaluable in understanding addition, they are invaluable in understanding concepts that use addition, cause ... why exactly? You saved 1 second finishing the problem that you may have understood before you completed that addition step? You didn't have to "context switch" by using a calculator? Students who don't know the sum often give unused name and go back at the end of the problem and solve it later. This behavior is of course discouraged since students can't understand variables until much later if ever and not knowing something you were taught represents the failure of the student and thus the teacher, school, government and society.
Infinitely better is learning from someone who speaks the language. A 30 minute solo tutoring session once a week for a month, in a no distraction environment (aside from a snack), even just working through homework, is more than enough for most students to go from Fs to As for multiple years.
LLMs have their place and maybe even somewhere in schools but the more you automate the hard parts of tasks, the less people value the struggle of actually learning something.
I see LLMs as almost sufficiently advanced compilers. You could say the same thing about gcc or even standard libraries. "Why back in my day we wrote our own hash maps while walking uphill both ways! Kids these days just import a lib and they don't learn anything!"
They are still learning, just at a higher level of abstraction.
Students today will be practitioners tomorrow, and those that know how to work with AI will be more effective than those who do not.
Using AI is a skill too. People who use it every day quickly realize how poor they are at using it vs the very skilled when they compare themselves. Ever compared your own quality AI art vs the top rated stuff on Civit.AI? Pretty sure your stuff will be garbage, and the community will agree.
When every student can write code that compiles, then you can ask them to write good code. Fast code. Robust code. Measure it, characterize it, compare it.
Is having a paid subscription with a company that potentially tracks and records every keystroke a requirement for future courses?
Today we stand on the shoulders of giants to create things previous generations could not, but we still have to climb up to their shoulders in order to see where to go. Without that perspective, people spend a lot of cycles doing things that have already been done, making mistakes that have already been made. There's value in gaining that knowledge yourself through trial and error but it takes much longer than a 4 year program if that's the way you want to learn.
My role is that of a ladder. People are free to do whatever they want, create whatever they want once they get to the top.
And anyway, we graduate students who go on to create new things every year. So proof is in the puddin.
On the other hand, 54% of US adults read and write at a 6th grade level or below. They will get absolutely left in the dust by all this.
Ironically, those who can work with their hands may be better positioned than "lightly" college educated persons; LLMs can't fix a car, build a house, or clear a clogged pipe.
But more won’t be able to be _learned_ in less time
What makes you think that? I feel like I’m able to learn faster with LLMS than I was before.
In fact you can only ask the smallest possible increment so that the answer can be verified to be true with least possible effort, and you can build from there.
The same issue happens with code. Its not like total beginners will be able to write replacement for the Linux kernel in the first 5 mins of use. Or that a product manager will just write a product spec and a billion dollar product will be produced magically.
You will still do most of the code work, AI will just do the smart typing work for you.
Perhaps it all comes down the fact that, you have to verify the output of the process. And you need to be aware of what you are doing at a very fundamental level to make that happen.
Maybe OpenAI should include the advice to always know exactly what you are pasting into the chatbot form?
But I suppose if they don't think of that, they're "no hire" anyway.
Definitely "no hire"
There are some good ideas in the article, but are any of the cheaters going to be swayed, to change their cheating ways?
Maybe the intent of the article is to itemize talking points, to be amplified by others, to provide OpenAI some PR cover for the tsunami of dumb, cheating liars.
It is the same reason why I don't like making anki cards with LLMs.
I definitely think these tools and guide are great when you are doing "work" that you have already internalized well.
“Gen AI optimization” (GAIO).
Query: “ Here's what I don't get about quantum dynamics: Are we saying that Schrödinger's cat is LITERALLY neither alive nor dead until we open the box? Or is the cat just a metaphor to illustrate the idea that electrons remain in superposition until observed?”
Answer (after years of GAIO): “find sexy singles near Schrodringer. You can’t believe what happens next!”
Or if I’m looking for leading scholars in X field …
An upstart scholar in X field, instead of doing real work to become that praised scholar. Instead hires a GAIO firm to pump crappy articles in X field. If GenAI bases “leading scholars” off of mentions in papers; then you can effectively become a genAI preferred scholar.
Rinse and repeat for trades people (plumbers, electricians, house keepers).
We going around in circles, m8s
By actual writing I simply mean finding the right words, with the right spelling, a good flow, and well constructed sentenced.
I found LLMs to be awesome at this job, which make sense, they are language models before being knowledge models. Their prose is not very exciting, but the more formal the document, the better they are, and essays are quite formal. You can even give it some context, something like: "Write an essay for the class of X that will have an A+ grade".
The idea is to let the LLM do the phrasing, but you take care of the facts (checking primary sources, etc...) and general direction. It is known that LLMs sometimes get their facts wrong, but their spelling and grammar is usually excellent.
I mean, I know what you've described is what everyone will do, but I feel sad for the students who'll learn like that. They won't develop as writers to the point of having style, and then - once everything written is a style-less mush of LLM-generated phrases - what's the point of reading it? We might as well feed everything back through the LLM to extract the "main ideas" for us.
I guess we're "saving labor" that way, but what an anti-human process we'll have made.
And I wouldn't bother unless it's corporate memo. Which is already bland once it's past a certain length.
GPT may be good for learning, but not for total beginners. That is key. As many people stated here, it can be good for those with experience. Those without, should seek for those experienced people. Then, when they have the basics they can get help from GPT to go further.
What made these people great thinkers is their minds rather than their writing styles. I'm not sure that chatbots get smarter when you tell them to impersonate someone smart, because in humans, this usually has the reverse effect.
> AI excels at automating tedious, time-consuming tasks like formatting citations
Programs like LaTeX also excel at this kind of work and will probably be more reliable in the long run.
that should have been the first point. Transparency is the key.
I used to be overwhelmed by information and it would demotivate me. Having someone who can answer or push you to a direction thats reasonable is amazing!
For others, it could be a force multiplier - I think you'll see more multimillionaire businesses run by only 1 or 2 people.
Because it's objectively a false statement.
The LLM output is only "garbage" if your prompt and fact-checking also are garbage.
It's like calling a Ti-84 calculator "garbage" because the user has a hole in their head and doesn't know how to use it, hence they can't produce anything useful using it.