9 ms·

The idea that a field can be reformed by making it worse until it suddenly faces a reckoning and emerges much better is ... I don't know where people keep getti

by tlb 5y ago

The idea that a field can be reformed by making it worse until it suddenly faces a reckoning and emerges much better is ... I don't know where people keep getting the idea that this might work. It has never worked in any field ever in history. The thing that can happen: fields gradually split into rigorous and non-rigorous camps. Like with evidence-based medicine, or chemistry/alchemy. Depending on the field, either might prevail in the market. Medical research and bridge-building are mostly rigorous, programming is mostly non-rigorous. AI/ML has a range of rigor levels, from fairly good to total crap. I think people in the field have a reasonable idea which is which. It's frustrating to people outside the field that think they can just take the technique with highest reported performance numbers and expect good results.

josalhor 5y ago
Well, I wonder if this could force people to start publishing their code so others can replicate the results. This would help greatly with the kinds of issues that are being discussed: - It would be much harder to hide makeup results and cherry-picked seeds - Useless research would not be as easy to hide - The real-world impact of the research would be more valued. - Researchers would be forced to recheck their code for it to be presentable, which could be a net benefit in terms of finding mistakes - and so on.. I do imagine this could harm the number of replications of the research. However, I think there can be a net positive effect from such a policy. What do you think?
- the_snooze 5y ago
  I'm active in the security and privacy field, and some conferences have started "artifact reviews" [1][2], where authors can optionally submit the data and code that go along with their work. It's definitely an improvement, but the devil is in the details. Should these be mandatory? If they're mandatory, then what quality standards should artifacts meet? Who's ensuring those standards are followed? How should proprietary code and sensitive human-subjects research data be handled? There's also the question of what code should be made public: Is it just the analysis that produced the metrics on the paper (e.g., R scripts, Jupyter notebooks, etc.)? Or should it also include the data collection and pre-processing? How about the code that didn't produce immediately publishable results, but could be useful for future work? [1] https://petsymposium.org/artifacts.php https://petsymposium.org/artifacts.php [2] https://www.usenix.org/conference/usenixsecurity21/call-for-artifacts https://www.usenix.org/conference/usenixsecurity21/call-for-...
  - josalhor 5y ago
    > where authors can optionally submit the data and code that go along with their work. So cool. I wish there were more steps in this direction. > Should these be mandatory? As an outsider, I would say no. However that should have a detrimental effect on the ability to get published if you are from an academic institution. I can totally see a gray area when it comes to non-academic publishers. > There's also the question of what code should be made public Shouldn't it be as much as possible? The way I see it is that the easier to replicate a paper, the more people can focus on its true utility. That is, by abstracting ourselves away over the details of the paper, what does this really contribute? From my point of view, publishing as much as possible maximizes the real-world impact of the research.
- bonoboTP 5y ago
  These discussions remind me of how there's a years long delay before these ideas first reach boards like HN and then popular magazines and the taxi driver. Yes, code releasing is a big talking point and most major groups release code. It doesn't solve the subtler problems,like claiming 1% improvements as a solid contribution, unequal hyperparameter tuning, cherry picking datasets, overclaiming the novelty, salami publishing, lacking ablations, not comparing against better methods etc etc. Releasing code is a step but not a panacea. At least it partially protects against fully made up numbers which is not nothing, but it's just scratching the surface. In many cases the code is not so easy to use and the original authors may not be willing to help you with reproducing it, ignoring emails and GitHub issues. If you can't reproduce it, they can always claim something. In one case I was told they rewrote the codebase in a new framework since they generated the results in the paper and there may be small discrepancies due to that. Apparently that was enough change that they were no longer the best in the benchmark comparison. But nobody cares at this point, the paper was published, they are on to their next project. People also have the attitude that it doesn't matter anymore, the field has moved on already, a retraction doesn't change much just hurts the author and the group. The vast majority of papers isn't used anywhere actually in practice, the field is moving so fast that any method can only remain on top for a couple of months. Also, I don't think AI really is worse at this. It's rather that we are more open about it because we have less to lose and professors don't have an iron grip, due to the existence of industry. In some of academia your whole future depends on whose ass you kiss, it's better in CS. It's a bit like sexism accusations directed at nerds, as if they particularly bad. The molochian cancer is everywhere. The less you see it may just indicate its more effectively hidden.
- bumby 5y ago
  Publishing the code may not be enough. Part of the problem is that replication of a study isn’t usually rewarded in the academic sphere because of the obsession with novelty within publications. Most researchers want to spend their finite time on something they find interesting and also may help their career. One way may be to create a movement to foster good replication studies that can be published and rewarded.
  - unishark 5y ago
    Novelty is in the job description though. I think the problem, especially in AI/ML is the obsession with "State of the Art" algorithms. This can be gamed by making claims with numerical "experiments". If an idea had to stand on its own, and be interesting even if it may not make a better pattern-recognition product, most of them probably wouldn't make it. Indeed that's essentially the requirements presented by reviewers and editors: "prove we should read about this otherwise-uninteresting method by giving numerical test results where it wins".
    - bumby 5y ago
      I’m not stating novelty isn’t be part of the aim of research, I just think we need to be aware of the perverse incentives it creates when it becomes the sole underlying criteria by which merit is judged. I think there’s room for expanding the definition of what is good research to include replication of prior art because the field in general will be better off for it. I imagine novel research would still be more prized but it wouldn’t relegate replication to being considered a waste of time.
- OminousWeapons 5y ago
  > Well, I wonder if this could force people to start publishing their code so others can replicate the results. This would help greatly with the kinds of issues that are being discussed: The real solution to the problem is to set a rule that says if you use a lab's published code base AT ALL for new work, you must cite the people who produced it along with the PI of that lab. No exceptions; acknowledgements don't count. Labs (especially those outside of CS such as life sciences labs) view code as IP and they don't want to release their code bases for two reasons: First, they don't think they will be properly cited when people use it (valid concern). Second, they think that they will lose their monopoly on future publications using the code base (less valid concern). Mandatory citation would solve both of those problems. Until a rule like this is in place, most labs will fight you tooth and nail on releasing code.
- miltondts 5y ago
  Great news! There are journals that do this! E.g. https://academic.oup.com/gigascience https://academic.oup.com/gigascience They require the full means for replication to be open access, and reviewers take this very seriously! They will run your code, open github issues if they detect bugs etc.
- wiz21c 5y ago
  AI seems to me as quite experimental. Designing better benchmarks (that is less cheatable) will help. Publishing code will go against IP policies, that should be handled at the political level : do you research for profit or for knowledge ? But yeah, code adds transparency and a whole lot of cheats... The funny thing is that any well organized coder will automate report production, so it's not like it is hard to do...
- ssivark 5y ago
  In the steel-manned implementation, I would like the plots and tables to be auto-re-generated on publication, from submitted code. That would ensure that the results can have no “hidden state” and the paper cannot claim anything more than what their publicly shared implementation guarantees. This is par for the course in theoretical work; we should strive to apply the same standards of rigor to experimental work.
Kye 5y ago
It's interesting that programming used to be rigorous. Whole programs were written out and verified before anyone would invest the computer time. IBM had a range of flowchart templates for it! Then the growing availability of computing resources flipped the cost-benefit calculation in favor of getting it done at any cost, and quality control processes haven't been able to keep up.
- salawat 5y ago
  It's funny you mention that. I'm often surprised how little literature there is out there on Quality in general, and how often I have to depart from Software in general and head into manufacturing/metrology to get any new/useful insight on Quality Control methodologies for software. There is, however, more to the story than Moore's Law at work. #1 In the early days, you had very little between you and the executing machine. Your programs were more ways of doing things to ensure a particular machine would get you a reasonable answer, and it was far easier to communicate the totality of the stack, software+hardware combined. #2 Compare the picture of the 60's-90's programmer to the programmer of today. You had to know the hardware, and do cartwheels to decompose your problem to be solvable within the constraints of the machine which you had to run it on. You were a professional optimization problem solver+a studious cross-referencer (libraries and dependency management were not as mature as they are now, nor were there as many Virtual Machine constructs to foster write once, run anywhere. #3 Libraries were a case of build it yourself, or you ported something else by figuring out the toolchain + operating in a much smaller network of professionals to reach out to for guidance. #4 There weren't many if any concessions to programmer "comfort" (IDE's), less static analysis (as far as I'm aware), and good luck finding documentation without paying for it. Now: >Many programmers are blissfully unaware of cache coherency, memory hierarchy, or the quirks of the hardware/filesystems they run on. The hardware is "the compiler writer's job" or "those driver writer's problem" (smh?), the filesystem is Someone Else's Problem... >More and more, the solution looks more and more like "throw more hardware" at the problem to create more abstraction, which requires more intermediate steps, which takes more compute... If for no other reason, software is disgustingly hard to Quality Control for because nowadays, it's more about having access to development talent to make the core system architecture, no matter how inefficient or bloated, maintain it, and extend it rather than boiling things down to least computtational overhead. When you've got a thing built on a constantly shifting Tower of Babel, where your artisan knows less than a 10th of what their total set of dependencies are doing, and oftentimes are selected for their willingness to sit down, crank out the requirements, and not balk; it truly is a miracle when Quality software actually happens. Test coverage alone isn't it. Nor is refactorability, or readability. You have to have the right Software for the right people, at the right time, for the right costs, to bring about the right constellation of jobs done, to create value. None of that value, in modern thinking, should be intrinsically tied to the people making it. In fact, I have a theory that the market is trying to get away from that by favoring designs where everything you need is in the automation code itself, as it obviates the concept of the "heroic wizard coder" as we know it. Part of this Quality too, is you have to actually care about the people you are implementing for. Any programmer can create a program that works (painfully). It takes a sharp one to make something intuitive, quick to learn, and a pleasure to work with without being godawful slow. I'd say Quality software is not something you set out to build (at least the way the Market driven ecosystem we have today works), but a happy accident when you combine all the right factors to get the job done you set out to do and keep it that way. But the roadmap to getting there is the hard part. If anyone ever wrote a dynamic code generator that worked like a database optimizer (imagine Select implementation FROM C Where INPUTS(x1,..,xn) AND OUTPUTS (y1,...,yn) WITH CONSTRAINTS (z1,...,zn)), we'd all be out of a job, possibly all the healthier for it. Who am I kidding, we'd all just be query writers and optimization engine babysitters for it.
ceilingcorner 5y ago
Most improvements to safety have come after serious disasters. Seatbelts, non-hydrogen zeppelins, life boats on cruise ships, etc.
raphlinus 5y ago
I'll give my reading, and a tip to the author. To me, this did not read as a serious proposal to actually commit more fraud. Rather, it was a "modest proposal" in the tradition of Swift, in which the actual call to action was for the field to be more critical of papers, especially to be on the lookout for all fraud, both the obvious kind and the more subtle variants, the latter of which also do great damage to the field. The tip: humor like this is fun and appreciated by people who run in the same circles as the author, but an essay like this will be read by a diverse cross-section of people. Some won't have the cultural references, some won't have English as a first language, etc. Almost always when I've snuck jokes into my writing, I've found it causes confusion. So I might have written this a slightly different way, something along the lines of: the community is structurally more equipped to deal with blatant than subtle fraud. Ironically, now that we're seeing more egregious examples of fraud, there's a better chance that things will get better; we would have tolerated the subtle types for a long time, as lots of people benefit from the status quo. If the author were actually legitimately calling for more fraud, then I apologize for misunderstanding.
- dahart 5y ago
  I assumed it was tongue in cheek, but the end of the article really convinced me that the author is serious about his ‘let it get worse so it can get better proposal’ stance. “Widespread fraud would force us to re-strengthen our community’s academic norms, transforming the way we do research, and improving our collective ability to progress humanity’s knowledge. “So this is a call to action: please commit more academic fraud. [...] “Let’s make explicit academic fraud commonplace enough to cast doubt into the minds of every scientist reading an AI paper. Overall, science will benefit.” Given the number of people in academics and politics and software and just about any field who argue from the ‘let’s burn it down and rebuild it’ position, even if it is satire, it’s basically impossible to tell.
  - jacobbuckman 5y ago
    Author here -- it's intended as satire, don't worry, haha. I know that nobody would ever actually do this so I decided to just lean into it. If I had any real power by which to fix things, I would have a much more nuanced take.
dalbasal 5y ago
In politics and other areas, the concept is known as "accelerationism." It tends to be associated with political extremism... "bring on the revolution/war/etc." As an angry or disillusioned response, I think it's a close relative of nihilism. Cover for being destructive. That's not to say "worse before better" isn't a thing... it's just not a thing we can do usually.
- Aperocky 5y ago
  I have the opposite view. Is AI as a field not destined for destruction? There were good stuff, but it's been out for a while and now it's just mountains upon mountains of crap. What does society stand to lose?
  - dalbasal 5y ago
    I suppose everything is headed for destruction. In any case, I wasn't arguing that accelerationism is incorrect... just that it tends to be associated with angry feelings, political, professional or otherwise. Also, while I can think of many things that got worse and then better... I can't think of any cases where it happened deliberately.
mjburgess 5y ago
Well it does apply to individual people. Since animal psychology is mostly design to keep animals safe (not, eg., to flourish), we can persist in severely bad circumstances merely because they arent unsafe. What "getting to rock bottom" does is drive a person to acute unsafety so they are able to overcome the inherent risk of change. This "personal accelerationism" does work, indeed, it may be the only thing that works. I do agree that this likely does not generalise to institutions.
deleted 5y ago
[deleted]
biztos 5y ago
> Medical research and bridge-building are mostly rigorous, programming is mostly non-rigorous. Medical research and bridge-building are increasingly dependent on programming. Maybe they are less rigorous than we like to think?
fullshark 5y ago
This struck me mostly as tongue in cheek, and the the author's main point is the blatant fraud is just the tip of the iceberg, the subtle fraud is happening under the surface and affecting almost every field.
dennis_jeeves 5y ago
>Medical research Lol.
joe_the_user 5y ago
I loved your first paragraph but I choked your second. All modern fields of study use quantitative measurement so splits in practice now would have to do with different ideas of rigor rather than rigor-nonrigor. The problem of AI/ML isn't the blatant cheating but the way that the goal is often getting n% higher than soto on X benchmark. Just chasing benchmarks makes any connection to broad dubious imo. It might or might-not give you something practically useful but definitely give you something career-wise useful. But just as much, when the field is just a giant race where no cares about any broader understanding, cynicism seems like a natural result. The ideal of academia, for all it's failings, is to give people some amount of space to speculate and explore wider vistas. It would be good if X number of people had the space to explore a variety of visions of "AI" other than the dominant one. But despite the vast number of people being sucked into the field, my guess is this is getting harder, not easier. And, of course, the mere appearance of "rigor", of quantitative measurement, is not helping things, again in my opinion as someone of no authority at all in field.
TwoNineFive 5y ago
You didn't read the article.
om2 5y ago
If medical research is rigorous then rigor isn’t worth much. It’s one of the fields most affected by the replication crisis, along with social science. https://en.wikipedia.org/wiki/Replication_crisis https://en.wikipedia.org/wiki/Replication_crisis

by tlb 5y ago

josalhor 5y ago

Well, I wonder if this could force people to start publishing their code so others can replicate the results. This would help greatly with the kinds of issues that are being discussed: - It would be much harder to hide makeup results and cherry-picked seeds - Useless research would not be as easy to hide - The real-world impact of the research would be more valued. - Researchers would be forced to recheck their code for it to be presentable, which could be a net benefit in terms of finding mistakes - and so on.. I do imagine this could harm the number of replications of the research. However, I think there can be a net positive effect from such a policy. What do you think?

the_snooze 5y ago
I'm active in the security and privacy field, and some conferences have started "artifact reviews" [1][2], where authors can optionally submit the data and code that go along with their work. It's definitely an improvement, but the devil is in the details. Should these be mandatory? If they're mandatory, then what quality standards should artifacts meet? Who's ensuring those standards are followed? How should proprietary code and sensitive human-subjects research data be handled? There's also the question of what code should be made public: Is it just the analysis that produced the metrics on the paper (e.g., R scripts, Jupyter notebooks, etc.)? Or should it also include the data collection and pre-processing? How about the code that didn't produce immediately publishable results, but could be useful for future work? [1] https://petsymposium.org/artifacts.php https://petsymposium.org/artifacts.php [2] https://www.usenix.org/conference/usenixsecurity21/call-for-artifacts https://www.usenix.org/conference/usenixsecurity21/call-for-...
- josalhor 5y ago
  > where authors can optionally submit the data and code that go along with their work. So cool. I wish there were more steps in this direction. > Should these be mandatory? As an outsider, I would say no. However that should have a detrimental effect on the ability to get published if you are from an academic institution. I can totally see a gray area when it comes to non-academic publishers. > There's also the question of what code should be made public Shouldn't it be as much as possible? The way I see it is that the easier to replicate a paper, the more people can focus on its true utility. That is, by abstracting ourselves away over the details of the paper, what does this really contribute? From my point of view, publishing as much as possible maximizes the real-world impact of the research.
bonoboTP 5y ago
These discussions remind me of how there's a years long delay before these ideas first reach boards like HN and then popular magazines and the taxi driver. Yes, code releasing is a big talking point and most major groups release code. It doesn't solve the subtler problems,like claiming 1% improvements as a solid contribution, unequal hyperparameter tuning, cherry picking datasets, overclaiming the novelty, salami publishing, lacking ablations, not comparing against better methods etc etc. Releasing code is a step but not a panacea. At least it partially protects against fully made up numbers which is not nothing, but it's just scratching the surface. In many cases the code is not so easy to use and the original authors may not be willing to help you with reproducing it, ignoring emails and GitHub issues. If you can't reproduce it, they can always claim something. In one case I was told they rewrote the codebase in a new framework since they generated the results in the paper and there may be small discrepancies due to that. Apparently that was enough change that they were no longer the best in the benchmark comparison. But nobody cares at this point, the paper was published, they are on to their next project. People also have the attitude that it doesn't matter anymore, the field has moved on already, a retraction doesn't change much just hurts the author and the group. The vast majority of papers isn't used anywhere actually in practice, the field is moving so fast that any method can only remain on top for a couple of months. Also, I don't think AI really is worse at this. It's rather that we are more open about it because we have less to lose and professors don't have an iron grip, due to the existence of industry. In some of academia your whole future depends on whose ass you kiss, it's better in CS. It's a bit like sexism accusations directed at nerds, as if they particularly bad. The molochian cancer is everywhere. The less you see it may just indicate its more effectively hidden.
bumby 5y ago
Publishing the code may not be enough. Part of the problem is that replication of a study isn’t usually rewarded in the academic sphere because of the obsession with novelty within publications. Most researchers want to spend their finite time on something they find interesting and also may help their career. One way may be to create a movement to foster good replication studies that can be published and rewarded.
- unishark 5y ago
  Novelty is in the job description though. I think the problem, especially in AI/ML is the obsession with "State of the Art" algorithms. This can be gamed by making claims with numerical "experiments". If an idea had to stand on its own, and be interesting even if it may not make a better pattern-recognition product, most of them probably wouldn't make it. Indeed that's essentially the requirements presented by reviewers and editors: "prove we should read about this otherwise-uninteresting method by giving numerical test results where it wins".
  - bumby 5y ago
    I’m not stating novelty isn’t be part of the aim of research, I just think we need to be aware of the perverse incentives it creates when it becomes the sole underlying criteria by which merit is judged. I think there’s room for expanding the definition of what is good research to include replication of prior art because the field in general will be better off for it. I imagine novel research would still be more prized but it wouldn’t relegate replication to being considered a waste of time.
OminousWeapons 5y ago
> Well, I wonder if this could force people to start publishing their code so others can replicate the results. This would help greatly with the kinds of issues that are being discussed: The real solution to the problem is to set a rule that says if you use a lab's published code base AT ALL for new work, you must cite the people who produced it along with the PI of that lab. No exceptions; acknowledgements don't count. Labs (especially those outside of CS such as life sciences labs) view code as IP and they don't want to release their code bases for two reasons: First, they don't think they will be properly cited when people use it (valid concern). Second, they think that they will lose their monopoly on future publications using the code base (less valid concern). Mandatory citation would solve both of those problems. Until a rule like this is in place, most labs will fight you tooth and nail on releasing code.
miltondts 5y ago
Great news! There are journals that do this! E.g. https://academic.oup.com/gigascience https://academic.oup.com/gigascience They require the full means for replication to be open access, and reviewers take this very seriously! They will run your code, open github issues if they detect bugs etc.
wiz21c 5y ago
AI seems to me as quite experimental. Designing better benchmarks (that is less cheatable) will help. Publishing code will go against IP policies, that should be handled at the political level : do you research for profit or for knowledge ? But yeah, code adds transparency and a whole lot of cheats... The funny thing is that any well organized coder will automate report production, so it's not like it is hard to do...
ssivark 5y ago
In the steel-manned implementation, I would like the plots and tables to be auto-re-generated on publication, from submitted code. That would ensure that the results can have no “hidden state” and the paper cannot claim anything more than what their publicly shared implementation guarantees. This is par for the course in theoretical work; we should strive to apply the same standards of rigor to experimental work.

Kye 5y ago

It's interesting that programming used to be rigorous. Whole programs were written out and verified before anyone would invest the computer time. IBM had a range of flowchart templates for it! Then the growing availability of computing resources flipped the cost-benefit calculation in favor of getting it done at any cost, and quality control processes haven't been able to keep up.

salawat 5y ago
It's funny you mention that. I'm often surprised how little literature there is out there on Quality in general, and how often I have to depart from Software in general and head into manufacturing/metrology to get any new/useful insight on Quality Control methodologies for software. There is, however, more to the story than Moore's Law at work. #1 In the early days, you had very little between you and the executing machine. Your programs were more ways of doing things to ensure a particular machine would get you a reasonable answer, and it was far easier to communicate the totality of the stack, software+hardware combined. #2 Compare the picture of the 60's-90's programmer to the programmer of today. You had to know the hardware, and do cartwheels to decompose your problem to be solvable within the constraints of the machine which you had to run it on. You were a professional optimization problem solver+a studious cross-referencer (libraries and dependency management were not as mature as they are now, nor were there as many Virtual Machine constructs to foster write once, run anywhere. #3 Libraries were a case of build it yourself, or you ported something else by figuring out the toolchain + operating in a much smaller network of professionals to reach out to for guidance. #4 There weren't many if any concessions to programmer "comfort" (IDE's), less static analysis (as far as I'm aware), and good luck finding documentation without paying for it. Now: >Many programmers are blissfully unaware of cache coherency, memory hierarchy, or the quirks of the hardware/filesystems they run on. The hardware is "the compiler writer's job" or "those driver writer's problem" (smh?), the filesystem is Someone Else's Problem... >More and more, the solution looks more and more like "throw more hardware" at the problem to create more abstraction, which requires more intermediate steps, which takes more compute... If for no other reason, software is disgustingly hard to Quality Control for because nowadays, it's more about having access to development talent to make the core system architecture, no matter how inefficient or bloated, maintain it, and extend it rather than boiling things down to least computtational overhead. When you've got a thing built on a constantly shifting Tower of Babel, where your artisan knows less than a 10th of what their total set of dependencies are doing, and oftentimes are selected for their willingness to sit down, crank out the requirements, and not balk; it truly is a miracle when Quality software actually happens. Test coverage alone isn't it. Nor is refactorability, or readability. You have to have the right Software for the right people, at the right time, for the right costs, to bring about the right constellation of jobs done, to create value. None of that value, in modern thinking, should be intrinsically tied to the people making it. In fact, I have a theory that the market is trying to get away from that by favoring designs where everything you need is in the automation code itself, as it obviates the concept of the "heroic wizard coder" as we know it. Part of this Quality too, is you have to actually care about the people you are implementing for. Any programmer can create a program that works (painfully). It takes a sharp one to make something intuitive, quick to learn, and a pleasure to work with without being godawful slow. I'd say Quality software is not something you set out to build (at least the way the Market driven ecosystem we have today works), but a happy accident when you combine all the right factors to get the job done you set out to do and keep it that way. But the roadmap to getting there is the hard part. If anyone ever wrote a dynamic code generator that worked like a database optimizer (imagine Select implementation FROM C Where INPUTS(x1,..,xn) AND OUTPUTS (y1,...,yn) WITH CONSTRAINTS (z1,...,zn)), we'd all be out of a job, possibly all the healthier for it. Who am I kidding, we'd all just be query writers and optimization engine babysitters for it.

ceilingcorner 5y ago

Most improvements to safety have come after serious disasters. Seatbelts, non-hydrogen zeppelins, life boats on cruise ships, etc.

raphlinus 5y ago

I'll give my reading, and a tip to the author. To me, this did not read as a serious proposal to actually commit more fraud. Rather, it was a "modest proposal" in the tradition of Swift, in which the actual call to action was for the field to be more critical of papers, especially to be on the lookout for all fraud, both the obvious kind and the more subtle variants, the latter of which also do great damage to the field. The tip: humor like this is fun and appreciated by people who run in the same circles as the author, but an essay like this will be read by a diverse cross-section of people. Some won't have the cultural references, some won't have English as a first language, etc. Almost always when I've snuck jokes into my writing, I've found it causes confusion. So I might have written this a slightly different way, something along the lines of: the community is structurally more equipped to deal with blatant than subtle fraud. Ironically, now that we're seeing more egregious examples of fraud, there's a better chance that things will get better; we would have tolerated the subtle types for a long time, as lots of people benefit from the status quo. If the author were actually legitimately calling for more fraud, then I apologize for misunderstanding.

dahart 5y ago
I assumed it was tongue in cheek, but the end of the article really convinced me that the author is serious about his ‘let it get worse so it can get better proposal’ stance. “Widespread fraud would force us to re-strengthen our community’s academic norms, transforming the way we do research, and improving our collective ability to progress humanity’s knowledge. “So this is a call to action: please commit more academic fraud. [...] “Let’s make explicit academic fraud commonplace enough to cast doubt into the minds of every scientist reading an AI paper. Overall, science will benefit.” Given the number of people in academics and politics and software and just about any field who argue from the ‘let’s burn it down and rebuild it’ position, even if it is satire, it’s basically impossible to tell.
- jacobbuckman 5y ago
  Author here -- it's intended as satire, don't worry, haha. I know that nobody would ever actually do this so I decided to just lean into it. If I had any real power by which to fix things, I would have a much more nuanced take.

dalbasal 5y ago

In politics and other areas, the concept is known as "accelerationism." It tends to be associated with political extremism... "bring on the revolution/war/etc." As an angry or disillusioned response, I think it's a close relative of nihilism. Cover for being destructive. That's not to say "worse before better" isn't a thing... it's just not a thing we can do usually.

Aperocky 5y ago
I have the opposite view. Is AI as a field not destined for destruction? There were good stuff, but it's been out for a while and now it's just mountains upon mountains of crap. What does society stand to lose?
- dalbasal 5y ago
  I suppose everything is headed for destruction. In any case, I wasn't arguing that accelerationism is incorrect... just that it tends to be associated with angry feelings, political, professional or otherwise. Also, while I can think of many things that got worse and then better... I can't think of any cases where it happened deliberately.

mjburgess 5y ago

Well it does apply to individual people. Since animal psychology is mostly design to keep animals safe (not, eg., to flourish), we can persist in severely bad circumstances merely because they arent unsafe. What "getting to rock bottom" does is drive a person to acute unsafety so they are able to overcome the inherent risk of change. This "personal accelerationism" does work, indeed, it may be the only thing that works. I do agree that this likely does not generalise to institutions.

deleted 5y ago

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biztos 5y ago

> Medical research and bridge-building are mostly rigorous, programming is mostly non-rigorous. Medical research and bridge-building are increasingly dependent on programming. Maybe they are less rigorous than we like to think?

fullshark 5y ago

This struck me mostly as tongue in cheek, and the the author's main point is the blatant fraud is just the tip of the iceberg, the subtle fraud is happening under the surface and affecting almost every field.

dennis_jeeves 5y ago

>Medical research Lol.

joe_the_user 5y ago

I loved your first paragraph but I choked your second. All modern fields of study use quantitative measurement so splits in practice now would have to do with different ideas of rigor rather than rigor-nonrigor. The problem of AI/ML isn't the blatant cheating but the way that the goal is often getting n% higher than soto on X benchmark. Just chasing benchmarks makes any connection to broad dubious imo. It might or might-not give you something practically useful but definitely give you something career-wise useful. But just as much, when the field is just a giant race where no cares about any broader understanding, cynicism seems like a natural result. The ideal of academia, for all it's failings, is to give people some amount of space to speculate and explore wider vistas. It would be good if X number of people had the space to explore a variety of visions of "AI" other than the dominant one. But despite the vast number of people being sucked into the field, my guess is this is getting harder, not easier. And, of course, the mere appearance of "rigor", of quantitative measurement, is not helping things, again in my opinion as someone of no authority at all in field.

TwoNineFive 5y ago

You didn't read the article.

om2 5y ago

If medical research is rigorous then rigor isn’t worth much. It’s one of the fields most affected by the replication crisis, along with social science. https://en.wikipedia.org/wiki/Replication_crisis https://en.wikipedia.org/wiki/Replication_crisis