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What are the implications of submitting a paper to be graded and to arxiv preprint on the same day?
I submitted my paper to be graded in a university class and to the arvix preprint on the same day. Are there any implications for the grading professor and myself if the arxiv preprint is announced before I am graded?

@PraphullaKoushik hopefully my edits make it clearer: a project paper and to the ARXIV preprint.

@PraphullaKoushik graded for a class. I tagged graduate-school, sorry if it was not clear.

I don't see any problem..

What kind of problem do you think about?

If it is a reasonable prof, no problem.

The only problem I can see is: why didn't the professor recommend that you submit the paper on arXiv? If he hasn't seen the paper yet (since the paper was just submitted for grading, it seems it is the case), did anyone proofread and check your article before you put it on arXiv?

No, there are no implications. Professors grading your work don’t care what you submit to arXiv, and the arXiv doesn’t care what you submit for grading. At any university I have ever had experience with, those two things would not affect each other in any way.
But what you did still doesn’t make sense to me. The only type of work that’s suitable for submitting to arXiv is a research paper. And I have never heard of a student submitting a research paper as a class project to be graded - it’s not that that’s not allowed or anything but research papers usually materialize as a result of extended effort that goes outside the scope of work done for a specific class. It would be a bit like submitting a class project that’s, say, five times longer and harder to write than the requirement. You would get an A, surely, but again, for obvious reasons that’s not something you ever see students doing.
(Edit: according to the comments, writing research papers for a class project is not uncommon in some disciplines, or at least may have not been uncommon several decades ago.)
My suspicion is you have some misconception about the benefits of submitting your class paper to arXiv. Perhaps you should rethink whether that’s something that you want to do and/or consult some experienced people about whether it makes sense. But regardless, for your actual question I don’t see any problems for you related specifically to the simultaneous submission to arXiv and to the professor.

Apart from the contents, I would find it odd to submit something to the arXiv while at the same time submitting it for formal feedback from elsewhere. Unless there is a reason to hurry with the publication, it would make more sense to get that feedback so it can be incorporated before submitting (no point in ending up with more versions than necessary on arXiv).

Some thoughts on why it might be an OK move: (1) a review paper that is worth sharing with the community but not publishing; (2) research on an existing dataset; (3) could be part of multiple-semester or capstone project (re: extended effort)

@TobiasKildetoft "while at the same time submitting it for formal feedback" - submitting to a journal falls under that category.

"The only type of work that’s suitable for submitting to arXiv is a research paper."  That's not entirely true - people can and do post on arXiv things like surveys, lecture notes, expository papers, and so on.  But I agree that there's not much overlap between papers one might submit for a course assignment, and papers suitable for arXiv.

@NateEldredge yes, I wrote that knowing it was only approximately true, but it’s a very good approximation (I would say 99% accurate for experienced researchers, and essentially 100% accurate from the perspective of a graduate student thinking whether to upload something they wrote to arXiv). Thanks for the added detail though.

"I have never heard of a student submitting a research paper as a class project to be graded"  This is a bizarre claim.  It is standard practice in the social sciences.

All this answer really needed was the first sentence.

@AnonymousPhysicist that’s interesting (about social sciences). Do you have some link to back up this claim?

@AnonymousPhysicist and, just to be precise, by “research paper” I mean original research, e.g., something that can be submitted to a journal and/or become a part of a student’s PhD thesis.

I've been told that students in my father-in-law's graduate crystallography course regularly published the results of their projects for that course.

Yes I knew what you meant, and no I do not have a link, and no I never do reference requests on Stack Exchange anyway.

@AnonymousPhysicist good that you stand by your principles! I guess I will have to live with not knowing whether what you wrote is true.

Thanks @MarkMeckes, I stand corrected, and edited my answer. I guess this happens in some discipline. Just to satisfy my curiosity, by “published” do you mean in peer-reviewer journals? ArXiv? Both?

@MarkMeckes: Do you have links to back up this claim?

I mean in peer-reviewed journals --- this would have been mostly before the arXiv existed, and it was experimental chemistry which even now has no presence on arXiv.  I don't have time for a lot of detective work, but a quick search turns up this probable example: https://pubs.acs.org/doi/pdf/10.1021/ic00222a026

@CaptainEmacs Right, but when submitting to a journal, I expect that it will be a long time before I get any feedback, and there will not have been any reason for me to not ask for feedback in other ways beforehand. In this case, there is (presumably) an explicit expectation that the OP will not have asked for much feedback, since it is a project to be graded, and they can hopefully expect to get feedback within a short timeframe.


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What's the exact criterion of dual submission?
Does submitting two (substantially different) versions of a same paper count as dual submission?
To be specific, let me take this as an example:
Say I've done a research and a paper A was written about the research. However, later I found that I can make some improvements on the method of A, and after making these improvements I've got B, a second version of A. A and B are same in their abstracts, introductions and related works, but largely different in their methods and experiments.
I've submitted B to conference X which will inform its final decision to authors in late June and I'm going to submit A to another conference Y whose submission deadline is in late April.
Question is, will this be considered as a dual submission? If the answer is positive, then which part(s) of the content of A should I modify to avoid such a problem? Thanks.
Note that my field is Computer Science, and most conferences in this field consider their proceedings as publications.

If you think of these as "two different versions of the same paper", then likely so will the program committee(s), so they will likely consider it double submission (and immediately reject both versions). In any case, now that you have the stronger result, why would you want to publish the weaker one?

@JeffE Because the methods proposed by paper A and B are quite different, although the motivation of A and B is the same. A and B also solve the same problem. I don't know how to clearly describe such a relationship between A and B, perhaps "same" is not a proper one.

@JeffE although the results of A is weaker than B, the method of A does have some advantages compared to previous proposed methods. On the other hand, the acceptance rate of conference Y is greater than that of conference X by ~20%, and
now I desperately need a published paper for some important reasons. So that's why I decided to submit B to X and A to Y

If B improves over A, wouldn't your abstract/motivation/related work mention what A does and how B improves over it?

@GoodDeeds Surely not, because both A and B are our works, and none of them are published or released to the public yet.

That last comment convinces me that you're trying to cheat the system.  Even if you do submit both papers (or both versions of the paper), each submission must cite the other, because you know that they exist.

It is up to the editor/conference committee/reviewers to decide if two papers have too much overlap.  Show them about both versions of the paper, and let them decide.
Since there is no objective way to decide if two papers are adequately different, the criterion is the opinion of the editor.
If the editor is unaware of the situation, then you are at risk of committing misconduct.  If the editor knows and approves, you are likely to be behaving appropriately.

Thanks. Could you further explain what's the most common criterion? For example, if I re-write the title, the abstract, the related works and the introduction of paper A, how probable is the two papers considered overlapped?

"Could you further explain what's the most common criterion?"  No, there is no such thing.

I think you mean both editors (or in this case, both program committees), not just one.

Instead of calling your papers A and B, you can call them

Some paper in computer science, I
Some paper in computer science, II

Change both abstracts restricting to the content of that particular paper. You can also mention in both that there are two pats for this.
This practice is usually followed when the length of the paper exceeds certain limit.

I agree especially with "Change both abstracts." You've described (particularly in the comments) differences that seem (to me) significant, and I think the abstracts should reflect that.

@AndreasBlass Thanks for your supporting comment...

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How can I deal some outdated claims/conclusions when I translate old thesis?
Here's some backgound...
I wrote a master thesis several years ago in my native language (Not in English).
And recently I am planning to further my studies and so I think an English version of the paper will help my applications for English-speaking schools.
And here goes some tricky parts...
How should I deal with some claims in the paper , which were the most reason why I did the research,  like "no one has yet applied tech A into field B" which was true by then (appreantly not true now)
Should I untouch these claims or should I adjust them to the nowadays facts.

If you produce an "annotated translation" rather than a "strict" one then the problem goes away. You can asterisk the phrase "no one has yet applied tech A into field B" with a footnote that updates the claim with a more modern one, providing a citation.
You could actually go further by producing an "updated translation" that gives even more information if you have it now but didn't then. If you've done research on the topic since then it might be useful to do this.
A third option is to product a new paper that isn't a translation at all, but a new work based on the old one, citing it appropriately and with newer results. That is probably more than you want at the moment, I'd guess.

Good advice! Also possible to add a chapter with new results. Just a slight risk to get bogged down and spend too much time.

A zeroth option would be a note in the preface: "some of the claims in the paper were true at the time, but have since become false".

@Teepeemm When only the claims about being the first one to do it are untrue I would make clear that not the results are proven false. And one could adapt the formulations a bit like "The author was to his knowledge in 20xx the first one to show ... since then there were a few other papers published about the topic" (optional: Cite a few to add value to the new version)

+1. I often see footnotes that literally begin "translator's note:" to make it clear which footnotes were in the original, and which were added in translation. "How to use translator's notes"

Before you actually start translating your master's thesis, you really should ask yourself whether the effort that you are going to expend on this is proportionate to your intended goal. If you intend to embark on such a time-consuming process, it should be based on more solid evidence than simply "I think an English version of the paper will help my applications for English-speaking schools". In particular, if this really is your main motivation, then it may be a good idea to post a separate question here asking to what extent such a translation can be expected to help your application.
Concerning your actual question, there is no reason why translating your thesis would need to involve updating it. A translation is just that: a rendering of an existing document which was written in one language in another language. You wouldn't go back and revise your thesis simply because it no longer reflects the state of the art, therefore there is no need to do so in a translation.
That said, while there is no need to do this, you can certainly choose to add some updates to your thesis. However, such material would need to be clearly marked, i.e. you would need to clearly distinguish between the original text and any later additions. One reasonable solution would be to add footnotes in some appropriate places. Such footnotes would then need to be clearly distinguished from footnotes which are part of the original thesis.

hey Adam thanks a lot for your advices. the reason why I am doing this is mainly because that I have not done research for serveral years and I do not have much to demo my academic potential. The thesis topic seemed somehow creative at 2018 :-) so bascially I am doing evething I can to take a shot

I am not sure why you think translating your thesis will help you with admissions, but I will make the assumption that you want to be able to submit documents in English that demonstrate your previous research work.
In terms of translating your master thesis, simply indicate when the original was written and when the translation was made. The fact that some assertions in your master thesis are no longer true does not make it "wrong".
Whatever you do, do not modify the content of your thesis when you translate it. If you do, than that would no longer be a translation and you risk falling into self-plagiarism.
The fact that you are aware of more recent research related to what you did a few years ago is a good thing that you will be able to discuss during interviews. But that doesn't allow you to make a revision of your Master thesis.

No, it wouldn't be self plagiarism if the original is properly cited.

Also, can we take this opportunity to acknowledge what an incredibly non-sensical concept "self-plagiarism" is? No, of course translating your own thesis is not any form of plagiarism. It may become some other kind of academic misconduct if you try to double-dip by pretending that the translation is some new research work, but it's still not plagiarism since you are still the author of all involved works.

My suggestion would be to instead write a condensed paper in English, describing your research question, approach, and main results, properly referring to the relevant sections or page numbers in the thesis.
This paper would then also include a "Discussion" section that details those advances in the field since your thesis was done that you mentioned, and maybe some other insights you've since developed, or ideas for future work. If you could find some link to the research area of the group you're applying to, that might also go in that section.
You could then even publish that paper on ArxiV, to make your results more accessible for everybody.

When making claims in a paper, it is important to be accurate and specific. In your case, if you are claiming that "no one has yet applied tech A into field B," you should make sure that this is actually the case. Citing sources that support your claim can help to add credibility to your argument. However, if it turns out that your claim is inaccurate, you may need to revise your paper. In some cases, it may be possible to explain away the inaccuracy by providing context or additional information. For instance, you could note that your claim was true at the time of your research but is no longer accurate. Alternatively, you could qualify your claim by adding a time frame or geographic location (e.g., "to the best of my knowledge, no one has yet applied tech A into field B in the United States"). If none of these options are possible or desirable, you may need to delete the claim from your paper altogether.

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Is it OK to use GPT-3+ to rewrite your own paper to have better English?
As an example, someone who's not a native speaker of English might come up with the following paragraph (generated using a second-class translation service):

Basic principle of gamification is providing of receipt of permanent,
measurable feed-back from an user, providing possibility dynamic
adjustment of user behavior and, as a result, rapid mastering of all
functional possibilities of appendix and stage-by-stage immersion of
user in more thin moments. Another method of gamification is creation
of legend, history, provided with dramatic receptions, that
accompanies the process of the use of application. It assists creation
for the users of feeling of complicity, contribution to common cause,
interest in the achievement of some invented aims.

One could then use GPT-3+ (aka ChatGPT) to rewrite the above into something closer to what a native English speaker might write:

The fundamental idea of gamification is to give users continual, measurable feedback, allowing them to adjust their behavior dynamically and quickly learn all the features of the app. Another way of gamification is to create a story with dramatic elements that accompanies the user's experience. This helps to create a sense of involvement, contribution to a shared goal, and interest in achieving the set objectives.

Ignoring the question of how good of a job GPT-3 did here, is such use of neural networks acceptable in academia? All the original ideas are still yours but GPT-3 helps you convey them using shorter phrases and better English.
Update: Since the example I've provided generated a lot of debate, here's a slightly better original text with a correction provided by GPT-3:

On enormous territory of park there are lakes, rivers, canyons and caves. Lake Yellowstone, one of the greatest alpine lakes in North America, is located in the center of Yellowstone caldera, greatest supervolcano on a continent. Caldera is considered a dozing supervolcano; he erupted with enormous force several times for last two million years. Greater part of territory of park is covered by hardening lava; in a park there is one of five existing in the world of the geyser fields.

Corrected to:

On the enormous territory of the park, there are lakes, rivers, canyons, and caves. Lake Yellowstone, one of the greatest alpine lakes in North America, is located in the center of the Yellowstone caldera, the greatest supervolcano on the continent. The caldera is considered a dormant supervolcano; it has erupted with enormous force several times over the last two million years. The majority of the park's territory is covered by hardened lava; within the park, there is one of the five existing geyser fields in the world.

As you can see, an extremely poor text is probably a bad use-case for GPT, but a few errors here and there are well within its capabilities.

Does this answer your question? Is it OK to generate parts of a research paper using a large language model such as ChatGPT?

@Snijderfrey no, that question is about generating novel content vs. rewriting your own content

The answer for publication might be very different for a paper written as a course requirement. Which do you intend?

@Buffy publication in a journal

If I were an English teacher, I'd give the output result about a C-.  The input had "Another method of gamification..." that was translated to "Another way of gamification..."  A correct expression might be "Another way of gamifying [something] is..." What's there is just plain wrong; it needs a noun. Of course, the translation is trying to deal with the horror of "gamification."

The listed output seems to have less content than the original. Pretty, but empty.

Are you going to acknowledge the use of GPT-3+ model when you submit your paper?

@DmitrySavostyanov do you have to acknowledge the use of Grammarly or Google Translate when submitting your papers? If Yes, then Yes for GPT-3. If No, then No.

If your academic writing is like in the first example, I recommend taking a language/writing class. (Ironically, as I wrote this comment, "LanguageTool" recommended replacing "recommend to take a" by "recommend taking a", and I accepted the recommendation).

What question do you ask ChatGPT exactly so that it can "rewrite" your paper?

@Buffy added a less extreme example

@BobBrown I agree that a really poor input text is beyond GPT's capabilities at the moment, so added a less extreme example that was likewise generated by a very basic auto-translator.

@JonathanReez yes, I interpret this question to refer to using AI to improve the wording or phraseology of your own content rather than generating "new" content or "new" insights. It's analogous to using a machine translation service to help translate your paper.

Edit: the question after the edit is so different from the original version that I wrote a completely new answer. My original answer is preserved at the bottom of the post.
1. New answer (added following OP's update to the question, which effectively turns it into a new question)
Your added example shows the use of ChatGPT to improve a text written in intelligible, but unidiomatic, English. ChatGPT apparently outputs a grammatically flawless version of the same text, without any of the meaning having been altered in any way.
So, is it okay to use ChatGPT in such a way? Yes, absolutely — as I said before, it's exactly as "OK" as it is to use Google Translate, Grammarly and similar tools. That is, as long as you are not submitting the text as a class assignment meant to test your English writing abilities, in which case it would obviously be cheating. Otherwise, I can't think of any plausible objections anyone could raise here. The issues of accidental plagiarism, giving credit to the creators of ChatGPT and the texts it was trained on, etc, that were raised by some people previously, seem completely irrelevant in this scenario. All you are doing is improving grammar, and that's perfectly fine if indeed ChatGPT is capable of making such improvements.
To be clear, my analysis above pertains only to the types of usage illustrated by the specific example you posted in the updated question. I still have some doubts about whether putting too much trust in ChatGPT's capabilities is a good idea, and can imagine situations where a writer with a poor grasp of English language nuances would not realize that ChatGPT was making their text worse in some ways, or even modifying the ideas of the text or committing plagiarism. But that's a secondary concern that's not so pertinent for the current discussion. Ethically, there is nothing inherently wrong in using automated language improvement tools, as long as we remember that at the end of the day, writers are responsible for the content they are putting out into the world, in the same way that the driver behind the wheel of a Tesla running on (the misleadingly named) "Autopilot" mode is responsible for what their car is doing on the road. If ChatGPT messed up and you ended up putting your name to an ethically compromised text, that's on you. With responsible use, this can be avoided.

2. Original answer and edits prior to OP's update to the question. This addresses the "bad" example in the original question and seems no longer relevant for what OP actually wants to know
It's exactly as "OK" as it is to use Google Translate, Grammarly, or any of the other automated language-transformation tools that purport to turn all of us into budding Hemingways with zero effort on our part.
To be more precise, there isn't anything unethical about it, but as of the time of this writing, it doesn't work well enough to be anything but a complete waste of time (as your own example illustrates). So if by "is it OK" you mean "is it a good idea", the answer is no.
Edit: to be clear, it’s possible that some ways of using ChatGPT, when used with caution and some effort by a person with already reasonably good English language proficiency, may actually help the person produce writing of slightly better quality. See @anjama’s comments below. I make no claim one way or the other on this issue. My answer above was addressing OP’s specific suggested use case, as illustrated by their example.
Edit 2: I took a closer look at OP’s example input text for the “improvement” workflow. Honestly, the text is pure gibberish, and the idea that an intelligence, whether artificial or human, could penetrate through the poor writing and fathom the writer’s “actual ideas”, then express them more eloquently, is simply absurd. Garbage in, garbage out, as they say. I suppose you could interpret it as a sort of general prompt for someone to discuss gamification and what it’s about. If I had to rewrite it, I could use my own knowledge of what gamification is to write something plausible, but it would bear little relation to whatever it was that the writer of the text actually wanted to express.
To summarize, OP specifically asked for his premise not to be criticized, but the question simply makes no sense in its current form: either the premise is completely false, or the example OP used to illustrate the situation he had in mind is atrociously bad. In the absence of another, better example that lives up to OP’s premise, the question cannot be given a meaningful answer.

"it doesn't work well enough to be anything but a complete waste of time" I would argue this is plain wrong. Sure it doesn't always work, but in my case, I'm writing video scripts for lessons I need to record. I tend to be overly pedantic, so asking ChatGPT if there is anything I can make more concise and easy to understand is shaving 5-10% of the length off my videos. Shorter videos are easier to record without mistakes, saving me time, and over the lifespan of the videos, it'll save hundreds if not thousands of hours of students' time. I call those the opposite of "a complete waste of time"

With that said, I don't just copy/paste the complete ChatGPT output; rather, I merge its best suggestions with my work to avoid losing too much of my overall "voice" or style. That takes time over just copy-pasting, but it's still a net gain in the quality of the lessons and reduces how frequently I end up re-recording lessons

@anjama If you can, it might be better to ask another person to review your scripts. You'll probably get similar feedback, and also the kind of feedback you can only get from someone who actually thinks about the material.

@anjama sure, I did not mean to claim that any use of ChatGPT is a complete waste of time. I’ve played with it myself and I can tell it’s a remarkably useful tool. I was addressing the particular use asked about by OP by a non-native English speaker, as illustrated by the example in the question. As for your particular way of using ChatGPT, I can certainly believe you derive some benefit out of it, and while I may still have some doubts that that benefit may not be as great as you think it is, I would not go as far as claiming it’s a waste of time.

The discussion of whether tools of this kind do a good job is completely tied to current capabilities, and to the linguistic/presentation skills of any authors using such tools. Neither is a fixed quantity; capabilities will increase (I'd predict drastically in the next few years, but then again it's a policy question, not a prediction one), and user skill may actually increase through tool use (personally, I have picked up quite a few bits of useful programming knowledge from Codex already).

@wizzwizz4 So my workflow right now is Write > Grammarly clean up > ChatGPT suggestions > record > human review of video. Ideally, there would be human review of the script before recording, but it's already dozens of hours of video content to review (an entire semester's worth of teaching).  Having them check the scripts first might save some re-recording and re-reviewing effort, but it effectively doubles their workload, which is a lot to ask for when they seem to be really happy with the videos coming from the current workflow.

@DanRomik I was kinda worried I wasn't picking up on some contextual nuance from your answer. I just am worried about the consequences of people underestimating what these tools are capable of, and how much more capable these tools will become in the near future, so I went a bit to the opposite extreme with my comment

+1, but I don't think that the question should be dismissed just because ChatGPT is not good enough yet. It's entirely plausible that language models will soon become much better. However, your comparison with Google Translate is very useful. Unfortunately, I have been given papers to review whose language was so bad that I had to refuse to look at them at all. Some machine translation was no doubt involved. If the authors don't have a good enough command of English to even verify that the translator output is reasonable, then they should not be using a translator at all. It's dangerous.

The danger is even greater with language models than with translators, as they are very good at making up plausible nonsense. In the end, it is probably less work to write it yourself (even with imperfect, but clear language) than to correct the language model output. Sadly, the people who will resort to using such tools are most likely to be those who are unable to correct the output.

Added a less extreme example of text correction just to show that GPT can indeed be quite useful for non-native speakers.

"My answer above was addressing OP’s specific suggested use case", I really don't want to contradict your answer, but GPT3 seems to be used by at least a few people for the suggested purpose: Could AI help you to write your next paper?

@JonathanReez see my edited answer.

@Snijderfrey see my edited answer.

Re: Plagiarism: In the case of using AI just for the purposes of wording and flow, I would treat it like machine translation or spelling/grammar check. No citation is needed as you are using ordinary mechanical tools rather than tools that create or alter academic content such as theories, methodologies, analyses, findings, etc. If I need to verify the usage of a fancy long word in a dictionary before using it in my paper, I don't cite the dictionary. Now, if you ask ChatGPT to suggest a methodology for showing that X>2T(Q+1) and you actually use it, I would cite it or maybe give authorship.

Op didn't change the question, you ignored the question to rant about the badly chosen example the first time ...

Even if the course is not specifically grading for your "English writing abilities" they are still likely grading you on proper grammar, spelling, and other aspects of the English writing. Part of writing a paper is to ensure that you can do it in the business world which might not be very happy with people submitting internal company info to an AI service like that.

@DonQuiKong without an example that represents what OP has in mind, the question is too vague to be given a meaningful answer. Anyway, I attempted to engage with the question (both versions of it) and answer it to the best of my abilities. Sorry that’s not good enough for you.

@JoeW I interpreted the question as being mostly about academic publication rather than about class assignments. In the context of class assignments you may be right: in any assignment where the quality and clarity of the writing is part of what’s being graded, I would not assume that it’s necessarily okay to use ChatGPT to improve the writing. Whether it’s okay or not would probably depend on additional factors — primarily the instructor’s policies for the class.

@DanRomik then you should have voted to close. Imho it was clear.

Ignoring the question of how good of a job GPT-3 did here…

I think a bit of a frame challenge is necessary here. If GPT-3 or some other language model was capable of preserving meaning exactly (not adding, not removing, and not altering) while making purely stylistic changes, that would be one thing. But that's not what the GPT family does. It produces convincing-sounding waffling that is in many cases not quite semantically equivalent to the input, or, when asked to answer a new question, not quite correct. In academia, the devil is really in the details. So while asking GPT-3 for writing help can be useful (just the other day, it helped me discover the term "pleading necessity"), copying and pasting its responses is a dangerous road to go down. Think of it as a very diligent but overconfident and intellectually shallow assistant.

I think this answers the question most directly. I don't think anyone in any specialty or field can use GPT-3 blindly. You need to do more than proofread it. You need proficiency in your own native language, English, and your area of research to really know if the ideas and concepts have translated accurately. This is a dangerous road, indeed. Your target audience is humans, and semantics are important. GPT-3 isn't yet up to this task.

Added a less extreme example that could plausibly be generated by a non-native speaker (I've seen hundreds of texts like this during my undergrad in Europe) and a GPT's correction that didn't add or remove any content.

@GregBurghardt: 100% this.  ChatGPT invents false statements presented in well-written ways that make them look plausible if you aren't an expert reading carefully.  In Stack Overflow's [cpu-architecture] / [simd] tags, every ChatGPT answer I've seen has been useless, or worse: misleading and wrong, asserting false statements as the premise for its argument (detailed example).  (Of course those are low-effort trolls that asked it to answer the question itself, not improve existing "expert" text, but those errors could creep in anywhere).

Thank you for this answer. I was editing my SOP using gpt and it does a really bad job of preserving the important details regarding my projects and research experience.

It is not only AI that gets nuance wrong. We had the case with an actual native English speaker on the difference between "right choice" and "correct choice", in a context where the distinction was meaningful.

I would treat this approximately as I would treat proofreading by a human. If it results in substantial changes to the presentation, it should be mentioned in the acknowledgments ("Our thanks go out to the developers of GPT-4, which made numerous useful suggestions on the presentation of the ideas put forward in this paper. In addition, it spotted a crucial error in an earlier version of the proof of Lemma 5, without which Theorem 7 would not have been possible, and assisted us greatly in formalizing the proof of Theorem 8...."). But there is nothing unethical about getting feedback on a paper from anyone, irrespective of whether they are a person or a software.

Do people routinely acknowledge Grammarly and Google Translate currently?

I don't think they do. But then, neither do many people in my field use those tools (we get a lot of papers that are easy to recognize as written by non native English speakers, and a subset of those would probably profit from tools that make them sound more native). Personally, I would think Google Translate use should be acknowledged if such use had been substantial in a paper. I'm less sure about Grammarly, as I've never tried it.

ChatGPT (and future models) will not only fix grammar but possibly improve the material of papers, as @Polytropos suggests. That is, in my opinion, more worthy of acknowledgement than grammar corrections.

No!
GPT does not understand the input it is given. You can have no confidence that GPT will not change a correct statement into an incorrect one, possibly in subtle ways that will be difficult to spot for our hypothetical non-native speaker. For example, in your sample text, GPT changed the word "permanent" into "continual," which doesn't mean the same thing. It also changed the first sentence from the original, which was about collecting feedback from a user, into one about providing feedback to the user.
You may argue that these changes make the text more correct. That is a reasonable argument. However, the reason GPT is able to do this is that it is trained on a broad corpus of texts. Not knowing any better, it will combine the input text it is given with appropriate selections from its training data, thereby incorporating elements that are not the author's original work. Unless these sources are identified (likely impossible) and cited appropriately, this may constitute plagiarism.

I specifically wrote Ignoring the question of how good of a job GPT-3 did here in my question to avoid debates on how good of a job it did here. GPT-4 is coming out next year and will likely do much better. And even for GPT-3 there are many texts where the improvements would be meaningful.

@JonathanReez My answer is not based on this sample alone. These issues are inherent in the technology GPT uses, and GPT-4 will have the same flaws in this regard. At a very high level, GPT only answers the question "what text is most likely to come after another given text?" GPT will never understand the input it is given because its underlying technology is not capable of understanding, and it will always be prone to incorporating ideas from other sources because that's what it does. I draw examples from your given sample, but the conclusions are general.

Still, for a non-native English speaker GPT-3 combined with human judgement can render better texts than said human working alone. Copying it verbatim is a recipe for disaster but using it to improve your own text is a different story.

@JonathanReez I will allow that, combined with the judgement of a fluent English speaker, GPT may produce better texts. The non-fluent speaker will not likely catch these errors. Even still, the potential for accidental plagiarism remains, so for academic writing in particular, my answer is still no.

How is accidental plagiarism possible if you're just rewriting your own sentences? Can you provide a specific example?

GPT doesn't just rewrite your own sentences. GPT can't just rewrite your own sentences. GPT doesn't understand the concept of rewriting sentences. In your own example, GPT correctly writes in its first sentence that gamification is about feedback to the user when the input text incorrectly says it's about feedback from the user. It's pulling that correct information from somewhere, and that somewhere is not the input text. In an academic paper, if you can't cite a source for that, it's plagiarism, whether you meant it to be or not.

Presumably you'd have to cite a source for the very same sentence in your own paper, no? So you'll use the same source for the GPT version. And if you don't need a citation for it, then why would the GPT version need a citation?

@JonathanReez Sure, if I wrote that very same sentence in a paper about gamification, I would want either to provide a source, or claim it as my own "common knowledge." GPT doesn't have common knowledge--- everything it knows comes from source material, so it needs a citation. Unfortunately, GPT also can't tell you where it got that information from; it can't cite its own sources. So a human reviewer would also need to look carefully for any information added or changed by GPT and find an appropriate source for that information or discard it if no source is found.

everything it knows comes from source material => how is that different from how humans work? Any "common knowledge" you know must come from source material. So either a reference is needed even without GPT or it's not needed even with GPT.

Let us continue this discussion in chat.

Added a less extreme example that could plausibly be generated by a non-native speaker (I've seen hundreds of texts like this during my undergrad in Europe) and a GPT's correction that didn't add or remove any content.

The answer for me is Yes,
Using neural network to "rephrase" your passage is acceptable in academia. As far as I know, no rule of journal states that you can't use a language tool to revise your paper. And it doesn't matter if that's ChatGPT or other NLP model.
But acknowledge that ChatGPT is called ChatGPT for a reason, no matter what the true intention is, using a model optimizing for dialogue to an academic scenario just because that is "closer to native English speaker" is truly not a wise idea.
Just be careful that ChatGPT may(actually probably) lose content that help convey your idea of original work, and the rest of the stuff is take your own risk and mention in the acknowledgements

If the outcome really only includes your own ideas and they haven't been extended by the language model then it should be fine. Then it's like a German English translation but bad English into good English. This is all based on the assumption that writing a good English text isn't part of the rating.

I think this is accurate at this time since the tool doesn't seem to know much about content (doesn't probe wikipedia, for example). But that could change, and probably will. We are in perilous times.

You're also assuming that no relevant texts were included in its training data. I don't think it is knowable whether the output consists solely of the author's own ideas. An apropos word substitution could subtly alter the content towards something in GPT's training set. Showing that this didn't happen likely requires more human effort than revising by hand would.

Instead of transforming the text directly, you can ask it for points on how to improve the text.
That way, you can decide for yourself whether the points it suggests are worth incorporating into your text or not. It also reduces the probability of GPT messing up your original thoughts/arguments in the text.
When generating text from scratch or rewriting, you need to be very careful as it can make things up and/or change the meaning of what you originally intended. It can be similar to reviewing text from an inexperienced student who is good at bullshitting. If you are confident in your ability to spot the bullshit, fine, but that's your responsibility.
Ultimately, I don't think it is possible to ban the use of these language models, and every person needs to decide for themselves whether it creates value for them or not.

I have not used ChatGPT, but I have used a different, less intrusive tool called LanguageTool, in particular when I write German (to help with die/der/das/dem/den/des), but to a (much) lesser degree when I write English.  LanguageTool is an automated checker for spelling, grammar, and style, but it also suggests how to reformulate a sentence, such that a strict language teacher might argue the text is no longer entirely my own.
Check the rules of the journal.  Chances are they allow automated spelling, grammar, and style checkers, but not text generators such as ChatGPT.
Whatever you end up using, you should mention in the acknowledgements. You would thank a colleague for proofreading your English; the same applies to artificial intelligence or automated corrections based on other technologies.  Such an acknowledgement also provides full disclosure, protecting you from allegations of academic dishonesty by people who don't think you should use those tools or that you must be open about it if you do.  The terms and conditions of the tool and/or the journal may also require such disclosure.
For example:
Parts of this text were processed using aspell/LanguageTool/ChatGPT/Deepl Translate/Google Translate/... to improve language.

Do people routinely acknowledge LanguageTool in their papers?

Please see https://academia.stackexchange.com/questions/192077/are-there-examples-of-journals-with-an-explicit-policy-on-gpt-3-and-equivalent-l

@JonathanReez I don't know if people routinely acknowledge LanguageTool, but I think they really should.  As for your linked question; I don't know if journals have such a policy so I can't answer the question; I expect at least some of them would have, though.  Let's see if you get a good answer there.

It's as okay as is using Google Translate, automated spellchecking, finding synonyms with MS Word, etc. I don't see any downsides.
Yes, a person may appear more fluent than they are, but if they do it consistently what's the issue? Yes, it may hamper someone's language development, but that's their choice. Yes, it may give you an edge over competitors, but it's their own fault for not using all the tools at their disposal.
All the Luddites who think this is a bad idea will go the way of the dodo. Don't mind them.

Generally speaking, the answer is an emphatic no.
The main reason for this no answer is as follows: the purpose of writing a paper is to demonstrate your learning and your work. Asking some tool or some automation to take what you can produce, then "improve" it, and finally present that "improved output" as "your work" is misrepresenting the reality. When it gets discovered that "your work" was not really yours, it will result in adverse consequences. These consequences will be much harsher than when your work was just not "good enough" but at least it is "yours". So, it is in your best interest to not mis-represent some one else's or something else's work as your work.
That said, it is ok to use ChatGPT or any other tool for improving you. The main reason is as follows: every tool that analyzes your work and provides a feedback for possible improvements is helping you in improving yourself. An improved you will produce a better quality work. So, one should use these tools to "learn", rather than "copy". That learning will, over time, result in a better quality output from oneself. By using these tools to "learn", you will have gained skills. By using these tools to "copy" you miss an opportunity to make yourself better.

This answer presupposes that excellent command of the English language is a prerequisite of being a scientist?

As long as we are only limiting our usage of AI to paraphrase and rewrite our own text and original work (That is not plagiarised), It is not merely OK, but important and essential to leverage and utilise every tool available to you to improve your writing and the final output.
Your writing has to evoke interest, be precise, short and convey the ideas, findings and such with as much flair as possible.
AI tools can help non-native writers overcome language, idiom, grammar and syntax challenges. And this is welcome.
The caveat at the beginning of this answer stands at the end too - given the power of AI to create text based on prompts, it is challenging to say a clear yes or no. So, as long as we limit the use of AI for paraphrasing, it is a welcome use.

A GPT can't paraphrase OP's own text without learning from a huge database of other texts. Many of those texts were used to train the AI without permission from authors, which is an ethical issue. Also, the paraphrased text is a combination of OP's work and the papers from the train set, which closely resembles the definition of plagiarism

@DmitrySavostyanov OP's own text is also a result of learning from a "huge database of other texts" unless OP invented the concept of gamification in the first place :-)

Exactly. And OP's text is the result of OP's own learning and work. Presenting anyone else's work as OP's own would be plagiarism

It’s either plagiarism or it’s not. Whether or not ChatGPT or you wrote any given sentence doesn’t matter.

At the time of this writing, the answer is likely to be fluidly changing, and dependent on the exact journal in question. For example, it was reported today that the International Conference on Machine Learning (ICML) has banned the use of large-scale language model (LLM) programs such as ChatGPT in conference papers, writing in their newest call for papers:

Papers that include text generated from a large-scale language model
(LLM) such as ChatGPT are prohibited unless the produced text is
presented as a part of the paper’s experimental analysis.

As a follow-up to public interest queries, the ICML wrote a longer clarification, including the note:

The Large Language Model (LLM) policy for ICML 2023 prohibits text
produced entirely by LLMs (i.e., “generated”). This does not prohibit
authors from using LLMs for editing or polishing author-written text.

However, it's debatable whether LLMs like ChatGPT are actually suitable for that purpose of editing and polishing text. For example, interviewed by the Verge, Deb Raji (AI research fellow, Mozilla Foundation) said:

I see LLMs as quite distinct from something like auto-correct or
Grammarly, which are corrective and educational tools... Although it
can be used for this purpose, LLMs are not explicitly designed to
adjust the structure and language of text that is already written — it
has other more problematic capabilities as well, such as the
generation of novel text and spam.

The last quote is technically correct but highly misleading (possibly intentionally). Yes, it wasn't necessarily designed for correcting text but it doesn't mean it's bad at doing that. And one can pretty easy verify on their own that it's quite good at correcting English text.

Let's avoid the discussion about the content/quality (of Chatwhatever AI) and let's stick to the formal part.
The worst thing you are doing is transferring some text that should be confidential (where "some text" is "your review") to a service over the web on which you have no control where/how is storing and accessing said text.

Aren’t papers intended to be public? In many fields they go into Arxiv long before peer review is complete. So even if OpenAI is hacked and the text is leaked, you won’t suffer any harm.

you are right. I copied my answer to a closed question https://academia.stackexchange.com/q/194898/128758 , because I thought it was the same question.
Instead, there are some subtle differences:  now voted for reopening that question

@JonathanReez I certainly wouldn't want a draft of my paper leaked across the internet before I'd sent it to submission.

@user438383 at least for me, my idea was about authors of the paper beinmg free to do whatever they want before submission, even posting it publicly to some AI server, if they think it makes sense. However a review has to be confidential from the very beginning, not from submission. In my view it is a huge difference (and I was lead to error because of the other question being marked as a suplicate)


text:
How to overcome fear of answering/asking questions in big class
Maybe it's because I'm a very shy person, but whenever I am in a big classroom setting (lecture), I am too afraid to raise my hand and ask questions or answer questions in class. It's really odd to me how some people can just do it so naturally. Recently, I've been trying to participate more in class, but after I say what I want to say, I completely zone out due to the pressure. So when my professor asks me something again, I tend to almost freeze. There's also the knowledge that there's a lot of smart students in the room, which adds to the fear of getting humiliated. Is there a way to overcome this?

In the worst case, what do you think what would happen when you ask a question in a big and crowded classroom ?

There are several difficulties you're dealing with:

It is genuinely not easy to formulate and ask a good question. You know how they say that a good question is more than a half of the answer. That's why it is so important to learn how to ask a question and how to split a question in several smaller questions.
You usually do not have a lot of time in class as the lecturer tends to move on quickly. It is harder to come up with a good question in limited time.
Other students are also present and you may feel that they are "smarter" and they are "judging" you. Whether this is true or not, is another question (spoiler: it's probably not true), but the perception makes it harder to act.

Now, what can we do about it? My suggestion is to decouple the problems. First, start by learning to ask a question outside of the classroom setting.

As soon as you have a question in a class, write it down on paper. After the class, re-read it, and try to find the answer yourself. Try also to re-formulate the question in a more clear, concise and answerable way.
If your professor offers office hours, attend them and ask your question in person. Talking to your professor one-to-one will help you both to learn the subject and to learn how to communicate more efficiently. It will also improve your confidence and build a good relationship with your professor.
When you are ready, ask more questions in class. Write down professors' answers and (if you have time) note your own reaction. Hopefully you will find it easier to ask questions as you do it more often. If not, meet your professor and discuss your questions again.

I don't agree with this answer, because I think the asker's problem is lack of confidence, not lack of quality questions.  What they need is practice with public speaking.

There is more than one way to work on your confidence. You can practice to be talk and look confidently, generally. Or you can recognise that what they talk or ask about makes sense and gain more specific confidence through this.

Nobody asks questions, or speaks about anything in public, naturally.  Everyone has to learn.  The best way to develop public speaking skills is to practice.  You can practice speaking in a room by yourself, and you can practice in front of a group of people.  As @roland mentioned, Toastmasters is an organization that will help you practice.  Ask your professors for other opportunities.
Now I have to stop writing this answer and go practice my conference talk.

You can "play a role" of a confident, extroverted, person, until you become one. Acting is actually helpful as you can "imagine" that it is someone else doing this, so it doesn't work on  your psyche. Fake it till you make it, can actually be good advice for some.

...as long, of course, as faking something agrees with your personal values and ethics.

This is what you "see":

This is what the lecturer sees:

Can you see the person who is talking? They are standing near the door, wearing a black shirt. So you see, in the lecturer's viewpoint, you are just a dot in another hundred dots. After you have finished talking, their attention is not on you anymore. And even when you are talking at them, they are not fully listening to your words. In their and other students' minds, there is always a topic much much more important than you: themselves. You are insignificant in their life, let alone your fear.
Whenever I have an anxiety like this, I always try to put myself into other's perspective, and see how unimportant I am in their eyes. At that time, I suddenly realize this:

Wait, what exactly am I fearing at? Nothing. NOTHING. I just assume that everyone is looking at me carefully, while in fact they are just busy on their problems. They don't even know that I fear at them. I make up an illusion, and then I fear of it. This is silly.

At the time you can see how silly your fear is, then not only it will disappear like it never exists, and but also a sympathy for their problems will take place instead. Sometimes in my imagination I think that I cut off my fear with the sharpest sword I have. I really like the feeling of having my fear cut off and my mind become crytall clear again. It makes me feel courage.
If you are interested in how I visualize the distortion we make in our minds, so that you can cut down your fear regardless the context, check out my article: A theory of perspective. Skip to the section "Communication and perspective-taking" if you don't have time.


text:
Publishing Valuable but Incremental Result
I am a Ph.D. student and recently I solved an important problem in my domain. Unfortunately, the paper got rejected stating that the results are interesting but incremental.
I discussed this issue with my peers. They said that they have gone through the similar rejection scenarios.
This thing is worrying me a lot. I picked that particular problem statement thinking that solving it would be a great contribution. And, my supervisor thought the same. However, we were able to solve the problem based on existing techniques and there are about 1-2 novel ideas in our work. It means our solution is simple and elegant.
If somebody would have solved the same problem with a rather complicated set of techniques while overlooking the simpler ones; that paper probably would have got accepted. My question is why the paper is judged based on it being incremental while it still being non-trivial? Why a work is not given more importance based on the value of the result instead of the method of obtaining it? Furthermore, I believe that simpler results should be preferred by the community instead of the complicated ones. And, they should completely omit the term "incremental" in their reviews. It is quite frustrating. Even the Google Scholar's tagline is "Stand on the shoulders of giants" :/

General advice.  When your paper is rejected, try another journal.  Perhaps a less prestigious journal.  (The other half of the advice: If your papers are always accepted the first place you sent them, then you are aiming too low.)

Hi, welcome to the site. I empathize with your situation, but this seems more like a rant than a question. Is this the question you want answered: 'why the paper is judged based on it being incremental while it still being non-trivial?'

@AppliedAcademic I am giving my opinion. Apologies, if it looks like a rant. Yes, this is the question and the following one too. Thank you.

I agree wholeheartedly that this is a problem. But I don't see a good question in your post.

My interpretation is that the reviewer(s) consider that solving the specific problem is not a particularly significant outcome. Assuming that they are right in this judgement (they may not be), the paper must distinguish itself in other ways.
Maybe the problem/solution is shown to have parallels with some other problem/solution. Maybe the solution is radically different. Or maybe, as in your stated case, the solution is simpler than expected. You must identify which of these is most correct and focus on presenting that as the centerpiece of your work. If the simplicity/elegance is what stands out most, find a way to validate and quantify the simplicity, and use that as the central theme.
If, on the other hand, you are convinced that the solving the problem is an important outcome by itself, then stick with the paper and submit it elsewhere. It is very common that reviewer assessments of relevance/novelty vary widely.
Onto the larger point of incremental improvement being generally given less respect; this is the reality with most products, research output being one. The current model of scholarly publication favours work that will get more attention and generate more downloads/citations. This certainly has demerits, but it is the norm and it's best to live with it. This doesn't necessarily mean you should not pursue this line of enquiry, but you should assess what and why it can be more appealing to the readership. (A demonstrably simple, reproducible solution to a problem of my interest would definitely appeal to me).

"If the simplicity/elegance is what stands out most, find a way to validate and quantify the simplicity, and use that as the central theme." I will do this when sending my paper the next time.  thanks!

The fact that the results are "interesting but incremental" means you have done something of value, so your paper is probably publishable somewhere.  You have two options you should consider: (1) try to expand the work to make it more than incremental before trying to publish; or (2) accept the existing incremental result and try publishing in another journal.  Either of those approaches are reasonable, depending on whether or not you think you can expand your work.


text:
Which is better, certificate of studies at something (not degree) or years of experience?
I am currently paying for a master degree, at the same time, I am interested in doing some python extra study, but I dont want to spend money in a python certification, so I am planning to get a course at Udemy or something similar. After that, apply Python in my job. What would it be better for future jobs interviews, years of experience or a diploma?
My question is because I sometimes see people with no high certifications in higher positions that people with certifications.

Without a question, experience is more valuable than certification which is almost worthless.  It is not even close.


text:
How do Canadian chairs work
I know there are tiers of Canadian chairs (Tier I and II). Tier I is for full professors and Tier II is for assistant professors.
I want to know how Canadian chairs work such as how to apply for them and who are sponsors. Does a Canadian chair holder need to have an affiliation with a particular department in a Canadian university before applying for the chair? Are Canadian chair holders sponsored so that they do not need to teach regularly?

What has your research shown so far? Are your questions (in part) answered here? https://www.chairs-chaires.gc.ca/program-programme/allocation-attribution-eng.aspx

https://en.wikipedia.org/wiki/Canada_Research_Chair

simply reading the program regulations will take you a long way…

From the wiki page, Canada Chair is managed by universities. But it still is not clear how to apply for it. Maybe it has to be nominated by a university?

For most Canadian universities, one has to be nominated by the university to apply since universities have a limited amount of token. Chair applications are on a specific topic and there are generally internal competitions or external competitions for the token. You are mistaken however regarding the fact that level II chairs are for assistant professors. If I recall correctly, the official rule is that the PI must be below 40 years old or something similar.

No, I do not think there is an age limit. Recently, an old Indian woman was appointed as Canadian Chair (Tier II, I think) by the UBC math department. The fact that Tier II is equivalent to assistant professor is on the official webpage.


text:
Sabbatical from work as a non-student
I have already finished my studies and work in a private  company. I would like to know that I could do my sabbatical as a researcher in a university. Is there any specific procedures or limitation for it?

Generally you would need to contact the university and/or some specific research group to see if they could accommodate you. I think that this is rare enough that you won't find a lot of places with policies designed for it, so you would need to work through individual contacts.
Some research groups may be able to hire outside researchers, but many have funds intended only for supporting students.
If you can go visit some nearby university and make contact with a researcher or department head you would learn more.
You might also need to worry about what your company permits you to do outside the company. Some will want to impose restrictions. Others not.
It would be different if you can bring research funding from your company with you. Universities are usually pretty happy about outside funding.


text:
Advisor suggests to change my research topic after unsuccessful start, unsure what to do
I am a first-year doctoral student in Computer Vision, and have some confusion about my research direction recently. I hope I can get some help and advice there.
A couple of months ago, I was arranged to do research about optical flow, which is a kind of motion representation and can be used in many computer vision tasks. In these days, I read many papers and did lots of experiment about optical flow about its history, datasets and criterion. Therefore, I began to believe maybe I could make some contribution to that. Since some previous ideas are proved not working, the professor suggested me to give up the research on optical flow and change to another.
Now I am very hesitant about the next plan, there are two reasons. On the one hand, I am not content to give up just like that, and I think we could do more. On the other hand, I know very little other directions in computer vision, so I am not certain which direction I should choose. For computer vision, I want to try everything. Although optical flow is not my favorite one, I can insist on it. I really want to choose a research direction, and spend several years or the whole PhD stage to do some meaningful works.
What should I do? I hope experienced people can give me advice. Thank you!

This is a discussion you should have with your advisor(s)...

So, if I understand correctly (1) optical flow is not your favourite topic and (2) your advisor suggests to change to another topic? Sounds like a solution rather than a problem.

You should be talking to your advisor(s) and you may have some good points to discuss.
If you think you have some relevant ideas, then you should discuss them with your advisor - one may be the "key" you are looking for.
If, after considering your points, the advisor still has good reason to think that there is no future, then you should accept that decision, clear in the knowledge that you put your points across.
So, discuss with your advisor and make an informed choice with them.

Thank you, I think so. Maybe I should more assertive.

It's not about assertive. How well can you trust your instincts? Do you have experience in it? Your supervisor can be saving you 3-4 years of your life by suggesting a switch of topics. Of course, you may be right and you should be pursuing optical flow. Do you have experience in the past where you went on your own and were scientifically successful? If so, give it more time. If not, perhaps give your advisor more consideration.

Some people have enough ideas and a secure enough background to be able to carry on doctoral studies with very little advice or direction and be successful. Others depend more on their advisors, who usually have more experience and insight into what is likely to work and what is probably unproductive.
You need to decide whether you are more like one of these or the other. Often the professional insight needed to be successful in research doesn't come in the doctoral study years, but within (hopefully) a few years afterwards.
I worry about your statement that "I know very little other directions in computer vision", which makes me wonder if you are ready to take the lead at this moment. If not, then you might follow your advisor's advice at this time and wait until later to explore the wider world. But you need to decide for yourself whether the possibly more risky path is right for you.
People can be successful either way, but not always the same people.

It depends how convinced you are and how willing to go against accepted opinion. History is full of predictions of impossibility, for example the advancement of AI in general and neural networks in particular nearly came to a dead stop at one point because of a book written by influential authors.

In 1969 Minsky wrote the book Perceptrons (with Seymour Papert), which
  became the foundational work in the analysis of artificial neural
  networks. This book is the center of a long-standing controversy in
  the study of artificial intelligence ... It is claimed that
  pessimistic predictions made by the authors were responsible for an
  erroneous change in the direction of research in AI, concentrating
  efforts on so-called “symbolic” systems, and contributing to the
  so-called AI winter, a period of reduced funding and interest in
  artificial intelligence research. This decision, supposedly, proved to
  be unfortunate in the 1980s, when new discoveries showed that the
  prognostics in the book were wrong.
  http://scihi.org/marvin-minsky-artificial-neural-networks/

I did a Masters back then and wanted to do my dissertation on neural networks. I was strongly advised not to because 'the subject is dead'.  I went ahead anyway, got my Distinction and sure enough a few years later neural networks were back in fashion. My paltry efforts were soon overtaken by cleverer people but by then I had moved on to something else.
If you are very confident of your abilities and are willing to gamble somewhat then you may prove everyone else wrong - it's happened many times in history, just read a few biographies. Are you tough enough?

Thank you, it is full of chance and challenge, and I would think about it carefully.


text:
How to handle a 7 days time limit for major revisions over Christmas?
I am in an uncomfortable situation: Last week, shortly prior to Christmas, I submitted my (me as first-author) manuscript. The journal replied on Dec 23rd, saying that the manuscript needs major revisions (and included their suggestions).
The time period for the revision is stated as being 7 days, starting with Dec 24. Obviously, as it was Christmas, I have received hardly any answers from my co-authors, including from my supervisor who has the data that needs to be revised. I have already asked the journal for an extension, but they have not replied. What happens if I miss the deadline?

You received a review in less than a week, at Christmastime? Are you sure this is a legitimate journal? Although I suppose why would they bother with revisions?

The answers here might help you: Request an extension of revision deadline over the holidays or Asking for extension in submission of revision

@Snijderfrey I meant the interval between submission and Dec. 23 reply, not the 7-day deadline

@Snijderfrey The issue is less about the 7 day time limit (which is in fact short), but about the unlikelihood that a paper submitted last week would already be reviewed properly by now.

@BryanKrause there are people/cultures in the world that don't celebrate Christmas. This also sounds like an MDPI journal, i.e. runs on a hyper-accelerated schedule.

@Allure Indeed, and from what I can tell that hyper-accelerated schedule is all about getting money in their pocket with little care for quality.

@BryanKrause Are you suggesting MDPI write their own reviews and fake them as written by actual reviewers?

@Allure No, I am suggesting that their hyper-accelerated review schedule is intended to get money in their pockets quickly with little care for quality.

@BryanKrause are you suggesting MDPI are able to control what their reviewers write? Because I am not seeing a mechanism which links a journal's average review time with quality. Conversely, if taking longer implies higher quality, all journals would just sit on their hands for a few months before starting review.

@Allure I don't understand why you keep inventing straw man arguments that I have supposedly made, and I assure you that this argument style does not inspire confidence.

@BryanKrause You have said "I am suggesting that their hyper-accelerated review schedule is intended to get money in their pockets quickly with little care for quality", but you have not been able to suggest a mechanism whereby hyper-accelerated review results in "little care for quality". You could say it's strawman, I suppose - but it's what you've written.

Unless it is for a special issue with a firm deadline (or a conference), probably nothing happens, though there will likely be a delay in publishing so that it can be rescheduled.
It seems a bit unreasonable, but do the best you can and either resubmit it or give the editor a firm-ish date on which you can.
It is unlikely that you will get a rejection for that alone, except in the special issue or conference situation.
One reason for a short deadline might be that someone, say a reviewer, on their end already missed a deadline while the editor wants to fill and issue and so they are squeezing you. Alas.
And, as the comments imply, there may be something fishy with this journal. Assure yourself that it is reputable.
But, even for a reputable publisher the deadlines for completion can be very firm since the paper can't be pushed to a future issue. It might be possible, however, for them to remove it from the "special" and schedule it for a future release. That might be the best you can hope for.

well, it is a special issue, and I think it was already the extended deadline when we submitted. But as my supervisor has the data, but does not reply, I cannot do it. Shit luck.

@AnnaBanana How are you in a situation where you don't have the data you wrote a paper on?

my supervisor, prior to the submission, "took away" the manuscript, and did all the statistics and removed mine from the results section; he said I should not have standardized the data before doing the stats (even though, he told me in the beginning that I should do that). As time was running, he changed the whole results section, and has not sent me the data until now. As a first author I did not agree on this of course, but he simply moved on, and as he is my supervisor, I could not do much against that.

@AnnaBanana Oof. Let me guess, submitting this year was also important for grant funding or some such? Key takeaway from this whole situation: you can not bear nor accept responsibility for something out of your control. Set the boundaries, lest you be ruthlessly abused. You have the right to strive for high quality in your work, sometimes that means you would make others (including your supervisor) do things they agreed on doing, explicitly or implicitly, and sometimes it means pestering them until they finally get to do them.

I have had this before (a ridiculously tight deadline) and sent an email to the editor to ask for a new date. He told me that the journal deadlines were only designed to encourage authors and everyone in the journal basically ignored them anyway. You can hope your editor takes the same view!
But to be sure, why not send an email saying something like "I am so sorry I won't be able to meet that deadline because [reasons]. However we can get the paper back to you on [date]. Please let me know if that will be a problem." They won't reply, and you can use their silence as evidence that they accepted the new date if (and they probably won't) they complain later on.

Ultimately the answer to the title question is You don't. If it's not ready, it's not ready. You can't submit a major revision that doesn't address the reviewer's comments. Therefore, you will simply have to miss the deadline and deal with the consequences.
However, don't assume the worst. The journal wants to publish your paper too, or they'd have given a reject decision. For most journals, what I wrote in this answer applies (I used to work in academic publishing so I know this firsthand):

For context, journals usually give these deadlines because they want to keep the author thinking about the manuscript, and are hoping for a timely revision. The last line about your paper possibly being withdrawn is because there are authors who, after a revise decision, decide not to revise their paper but do not inform the journal. If you exceed the deadline your submission gets marked as inactive. When the journal decides to do spring cleaning and remove all the inactive submissions, that’s when your submission might be withdrawn.

A caveat to this is that if your paper is part of a special issue with a fixed publication deadline, then they might have no choice but to publish without your paper. Since in a comment you wrote "it was already the extended deadline when we submitted", you can't really expect them to hold up the special issue even more for your paper.
One more thing. From your description of your paper's review process, it sounds like you are dealing with an MDPI journal. MDPI is an unconventional publisher - their processes are all hyper-accelerated. Their reviews are fast, their decision time is fast, and the time they give for revisions is also fast. As far as I'm aware, this is because they treat the time taken from submission to publication as a key performance metric (comparatively other publishers won't even measure this metric).
If you are dealing with an MDPI journal, then because their processes are atypical & I don't have an internal view of them, I can't tell what will happen. I would nonetheless guess that they will probably accommodate you, because again they want to publish your paper or they'd have given a reject decision. Also, MDPI's special issues are atypical in that they aren't extra issues of a journal (e.g. a journal that publishes 8 issues a year could have a "special issue" and have 9 issues that year). Instead they appear to be collections of papers on a selected topic with an invited guest editor. If this picture is right, then there is no need for a publication deadline. From their website it indeed looks like there is no publication deadline, only a submission deadline. If so, then they should be able to accommodate you.
A final caveat is that I can see them changing the status to "reject & resubmit", which is effectively the same decision as "revise", it just lets them game their time-to-publication metric. I don't say that they will, but I can see it happening (I can also see it not happening because technically it is past the submission deadline).


text:
How to acknowledge a research internship in a paper?
In an accepted paper, I have written my acknowledgments to the funding entities and now I want to also acknowledge the fact that this work was mostly carried out in X place thanks to an internship Funding. However, I am not sure if I should phrase it like that or if there is a typical sentence in these cases.

Ask X how they would like to be acknowledged.
If this was written as part of you internship it could even be the case that it is more appropriate to list X as an affiliation, but that depends on the details of the arrangement.

Just keep it short and simple. If X provided the internship funding, make sure you say that. But focus, in any case, on the source of funds, not necessarily the place. What you wrote implies that X was the funder, which may be your intention. If it is otherwise, make it more clear.
If the internship has a specific name, mention that as well. "Roy and Dale Rogers Internship for the Study of Late Cowboy Culture".


text:
Is it possible to earn an advanced degree based on work done as a lab employee?
Has anyone been a non-student staff member of a research lab at a (reputable, US) university starting from an entry/Bachelor's degree-level position, then leveraged their years of employment as the basis for a dissertation to receive an advanced degree from the same university with some minimal additional coursework/research/etc.?
Would this route be frowned upon at all, or have some other downsides I've yet to consider?
If such an option exists, is there a name or label usually used to describe this? Is this more plausible for a Master's (rather than a PhD)?
To clarify, I am not asking about honorary doctorates. I'm satisfied with my contributions over the past ~4 years, but nonetheless I haven't made exceptional contributions to qualify for such a thing, nor am I interested in getting an empty title from some disreputable source.
I am trying to understand if this is something that is actually plausible and worth looking into or not.

Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.

"worth looking": it depends on you. For someone it is worth to get a position where you earn the double , for someone else it is worth to work in precarious conditions to develop new things...

What "advanced degree"?

If this is your situation, why not ask your employer for your options?

There are schools that offer Master's Degrees based upon life experience.  https://www.degreemajor.com/life-experience-masters-degree is one such "school", but I offer that as an example, not as an endorsement of the practice or recommendation of any particular "school".
I know about such programs because my department tried to hire a person with such a degree as a laboratory engineer.  HR disallowed the credential, and we couldn't hire the person -- so such a degree clearly will not be counted as a real degree in every situation (or maybe even "any" situation -- all I can attest to is that our own HR department would not recognize the degree)

years of employment as the basis for a dissertation

Years of employment do not provide any basis by themselves. Were you actively developing new tools, or designing new experiments in these years?
If yes, you will be quite efficient in finishing your PhD quickly, because you are already doing what PhD students do.
Are you ready to do what you do now, for 3 years, receiving ~half of the wage (or nothing at all)?


text:
I just realized that my published papers are based on a buggy version of my dataset (due to a SQL error). How to proceed? How to tell my advisor?
I am in an advanced stage of my phd, finishing next year. I have 2-first author publications in which I used data from my group. My research group has different datasets stored in MySQL server, in which each researcher accessed their relevant dataset.
In my 2 previous publications, I exported my dataset into a csv file and worked with it. During this data export, I had to perform a couple of table join operations, involving filtered data in a kind of "summary" table, joining it with a table containing original raw data.
Last week, I was working on my third review paper, and I needed to get some statistics from the MySQL server, when I discovered that the dataset I used in my previous research was actually incorrect. Incorrect in the sense that I had an error in my data filter SQL query (used to export it), therefore the exported dataset was incomplete, kind of a "subset" of the actual data.
All my conclusions in previous work were drawn from this incorrect data. I am in panic, and don't know what to do. I am not sure what the reactions of my supervisor would be, if I explain this problem to him. He is such a person that expects "near perfect" from his students.
I am now sure how this will affect my PhD. The years and efforts invested are likely in vain. What is the best course of action?

When this happens to a tenured professor

Highly related: D.V.M. Bishop (2018), Fallibility in Science: Responding to Errors in the Work of Oneself and Others

@JacobManaker even worse, here https://www.businessinsider.com/reinhart-and-rogoff-admit-excel-blunder-2013-4?r=US&IR=T ... admit blunder, but claim it does not change much.

" I am not sure what the reactions of my supervisor " don't worry: the longer you wait to discover the reaction, the worse it will be.
It is in the human error to make mistakes. Don't worry about your supervisor reactions, but discuss with them the next urgent steps.

Bad news doesn't get better with age. Neither you or your advisor can change what happened, but you can control what steps you take next. Don't beat yourself up. These things happen. Be honest and up front, fix it, and move on.

Are there significant differences in the results when you re-run your analysis on the correct data?

Oh no! This is bad news for sure, but try not to panic.
The main thing is to rerun your analysis with the corrected dataset. Hopefully, you have been organizing your work in such a way that you can just replace the csv file, push the button, and have results quickly. If not, this is a good opportunity to improve your old code such that you can quickly reproduce the old numbers and then produce the new numbers. If possible, I would try to do this quietly (i.e., before telling others of the potential problem).
Once you have these results, you'll be in a better position to talk to your advisor. If you are lucky, your results will be the same (or even better, since you have more data) and it is just a matter of updating the paper without changing your conclusions. This shouldn't be too much of a problem. You should still tell your advisor, as a corrigendum may be required, but his anger should be limited.
You should also consider approaching this from the other side -- what claims did you make in the paper about your dataset? If you claim to have used, say, ImageNet, then there is not much wiggle room. But if you claim to have used "500 original datapoints from a medical database," then you might be able to simply replace this with "300 original datapoints from a medical database" and there is no need to change the papers' results or conclusions.
Of course, it's also possible that your bug biased the dataset in such a way that your conclusions are no longer valid. This would put you in a much more serious situation for which there is not much advice to give -- all you can really do is tell you advisor, show him the before/after numbers, and hope that his reaction is reasonable. The way forward will depend heavily on the technical details of your paper, so only your advisor's advice is really valuable in this case. Good luck.

I will repeat the experiments with the correct data since I have until end of July to meet my advisor again. Really feel really bad.

This is a good answer, but I would suggest involving the supervisor at an early stage for two reasons: (1) Their name is on the paper, they deserve to know as soon as you find out, and (2) One of the reasons the mistake got published is because you were working alone. Having someone to work with may help prevent this kind of mistakes. Most likely your supervisor will be very disappointed, but they will understand: everybody makes mistakes.

@Louic - I'm not sure I agree with that as long as getting results from the new data is fast (less than a day or two). Simply because going to someone with solutions/complete information is far nicer for both parties than going to them with a problem.
If it's going to take longer than that, then I agree with you wholeheartedly

@ScottishTapWater agreed

Agreed: unless (s)he's the type with explosive personality disorder who will physically kick you out of their office before you finish the sentence, the next thing (s)he is guaranteed to say is "so, how does this error affect the outcome or conclusion?"  If you already have that answer, you can proceed and get their advice more quickly and, probably, less annoyance on their part.

Having that answer will keep your advisor from dwelling on the worst case scenario (unless that's where you are).  I once worked for a PI who had to withdraw a paper from Nature because the 2nd author messed up, and the first thing anyone ever told me when I asked about him was that incident. Errors can get highly emotional, and (s)he is liable to go right to the worst case in their mind.  If you can reduce that to a few seconds, you'll both be much better off.  I would think even a week wouldn't be too long in this case, given the fairly high chance that you're not in the worst case.

If the data can be corrected within a week, I agree with not telling your advisor until it's fixed. That's a short time scale, and the risk of your PI catastrophizing the situation is non-negligible, when it could all end up being super-fixable and not a big deal. Head down, fix the data quickly, then you are better able to judge what to do. If there are still issues a week from now, then go to your advisor.

In addition to the reasons already listed, I would suggest that having important conversations while in panic mode is rarely a good idea. By taking a few days to calm down and study the problem more, OP can be sure that their diagnosis is correct before getting others all worked up. I've certainly had the situation where I thought I had forgotten to include important steps only to discover that I hadn't forgotten them, but had implemented them in a non-obvious place (probably for some good reason that I had since forgotten about).

Don't worry, everyone makes mistakes. Identifying and correcting mistakes is a crucial component of academic research and developing new theories.
From my experience, as long as you correct the mistake in a honest way, the journal will not retract your paper and your PhD will be unaffected.

Most papers are force-retracted because of ethical problems or political problems. No paper from my knowledge is forced to be retracted due to honest mistake (unless you require it)
If mistakes are identified, the best scenario is that you correct it by yourself. The second best scenario is someone else find it first and correct it for you.
Even if someone else find your mistakes, your paper will usually not be retracted. For example, see this example in the most rigorous journal in economic statistics: https://www.econjobrumors.com/topic/kasy-and-sautmann-econometrica-2021-proven-wrong/page/16

Here is what I would do:

I would use the corrected dataset to rerun all the analysis and start writing a corrigendum or addendum.
For some results that still hold, I would write that those conclusions are verified with the new dataset.
If some new results are different from the old results, I would update the new version of the results in paper, explain why the new results are correct in this scenario, and write a short discussion on under which scenarios would the old results likely to hold and under which scenarios would the new results are likely to hold.
Finally, you can post your addendum/corrigendum online and send it to the same journal.

In my opinion, (4) should read "Next, you send the editor of the journal your corrigendum and await their decision. Publishing the corrigendum online is not enough – the correction needs to be as available as the publication that contains the faulty analysis." Or perhaps there should be a (0): "Contact the editor, and tell them about the issue. Suggest to write a corrigendum."

@Schmuddi Good points!

First of all, don't panic.
This is not necessarily a fatal flaw. You said that the analysis was done on a subset of the data. Rerun the analysis on the full data set. If the conclusions still hold and follow from the hypothesis, your work will not be substantially affected. You can revise the paper even after its been published. Mistakes are happening in science. This happens particularly in the more difficult fields like theoretical math, where the results are very difficult to understand (even by experts).
The only way you are in trouble is if the conclusion is changed.
In any case, its better to find the mistake yourself than to have someone else find it and rub your face in it.
Regardless, you are going to have to tell your advisor. These things happen though. You aren't the first person to have this happen to them. At least you are still in your PhD program. You can at least fix everything before publishing your thesis.
You need to go about this the right way. The worst thing you could do right now is to go into a full blown panic and start calling everyone you know, including the journal and telling them you made this terrible mistake. Take some time and understand the mistake. Try to understand if it can easily be fixed or if the entire paper is going to change. Once you do that, then go to your advisor first with the complete analysis of the situation. Due to the highly political and sensitive nature of the academic world, you need to handle this correctly and carefully. If this is a minor error, it can likely be corrected with a simple revision and it should not be blown out of proportion. I would recommend going through the appropriate channels and deferring to those that have more research experience on how to correct this mistake correctly.

This is really good advice. Just to amplify something said here: in addition to "don't beat yourself up" (which others have said), make sure you don't try and convict yourself in front of your peers/mentors/colleagues. Often, people will respond to the energy with which you present a piece of information. If you present the information as a terrible mistake that you should have seen if you were competent, people will tend to believe you. If you present it as a minor oversight that does not affect the main conclusions, people will also tend to believe you. Don't sell yourself short to others.


text:
What is the most reliable math phd ranking?
There are a lot of subjects rankings of world universities availible: usworld, qs, shanghai. They all agree on the first 10 positions but differ dramatically even on the top 50. Moreover, I am sure they take some data into account which may be irrelevant for grad school, like quality of undergrad courses. What is the best way to estimate the prestige of a grad school?
For example, take The Hebrew University of Jerusalem. It is considered top 25 in Shanghai ranking, but is not in the top 100 for usworld and qs.
Lets assume "prestige" to be the ratio of good postdoc positions obtained by graduates.

A good way is to ask a researcher in the area you are interested in.

Why is this important?

How do you define “good postdoc positions”? I think your definition is self-referential. By the way, this self-referential property is actually a standard phenomenon with ranking questions. One answer to this is offered by the PageRank algorithm, but I don’t think anyone has implemented a version of it in the context of grad school rankings. (In other words, your question, while interesting, is basically an open problem.)

If you care about job placement of graduates, then you're probably better off researching this directly than using any of the rankings that you've mentioned.  One way to do this is to look at some fairly recent PhD graduates (you probably want to see how grads are doing 3-5 years after completing the PhD)  and see where they are employed.

"Lets assume "prestige" to be the ratio of good postdoc positions obtained by graduates." Why am I under the impression that this is quite a stretch of the word "prestige"...?

Lets assume "prestige" to be the ratio of good postdoc positions obtained by graduates

This is exactly why rankings differ dramatically. "Prestige" is extremely subjective, and the ranking depends on what you assumptions you make.
If you want to know where a PhD program's graduates ended up, you might be able to find that with some internet sleuthing. It would be tedious to come up with an entire ranking this way, but you could certainly compare a few programs which you're interested in.
Which is what you should do anyway. Rankings are rough guides, but they are no substitute for you thinking long and hard about what is important to you, and then researching which programs best meet your needs.


text:
Is/was there a final university examination where the student was required to write down everything they knew about the subject (e.g. History)?
In about 1990, I heard that in Japan, after completing a History bachelor's degree course of several years duration, the student was required to write down everything they knew about History. It would take the student several days to write it all down, and to prevent cheating, the student had to stay in a sort of hotel room, and was supplied with food and drink by the examiners until he or she had finished.
Was there ever, in Japan, or anywhere else, a university examination like this?

Bad ideas abound.

The old Cambridge Mathematical Tripos was notoriously broad and difficult. It was divided into 2 parts each consisting of multiple exam papers to be completed over a few days.

That seems strange! nobody knows what he knows, unless they get into a situation that helps to know what they should remember. Mind is not like a computer with ordered/numbered cells of memory to check and recall the cells one by one.

I find it very unlikely considering I know people who graduated in Japan in the 90s. What is the source of information? It might be a confusion: History BSc theses are often just a very, very long (in the 90s typically hands-written) loosely organized braindumps. The particular person may have just referred his/her experience. Since libraries are generally packed, it is plausible that someone who is running out of time and has to write several hundred pages by hand just playing a hermit in a hotel room, but as an exam it is very unlikely.


text:
An organizational chart to ensure central management of laboratories in a college
I was asked the other day to suggest how different labs in our college should be centrally managed, or what organizational chart would be suitable, to make all these labs abide by input from the quality assurance department in the college. We all know that charts can never improve management by themselves, but what I am looking for is a consensus or an estimate of what such an organizational structure might look like. What would be the best organization structure to facilitate better management of labs and have them follow a unified code with a view of accreditation in the future.
The college is a medical one which has several faculties with their respective labs inside: anatomy, chemistry, physiology, microbiology, pathology, pharmacology, computers, and medical research unit.
Any suggestions would be much appreciated.
Notes: below are some key considerations:

Lab accreditation
Lab devices, equipment, materials, procurement
Lab personnel training and qualifying
IT support
Calibration and maintenance
Lab safety
Store

PS
- No need to provide a chart to illustrate the organizational structure, a structured text as lists would be fine.

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/3.0/