A lot of this seems to be a problem with academic publishing, peer review, and the pedantry contained therein.
I tried (twice!) to publish a paper that was scientifically sound, but written such that it could be understood by a lay audience. It was rejected; quoting a reviewer: "This paper lacks math." That sentiment made me lose a lot of faith in academia. Now, I simply refuse to contribute any more time to writing papers or performing peer review (esp. in non-open publications). I'm sure I'm not alone; I know of at least one "seminal" robotics paper that was rejected from top venues multiple times for "simplicity" (lack of mathematics content) that went on to become a foundational paper in the field after appearing in a lower-tier venue years later.
The irony: it takes researchers a lot of time to make a paper dense & concise. If they "showed all steps", it would probably improve researcher productivity & make the material more approachable to newcomers. Instead publishers enforce length restrictions... for which authors dedicate upto 25% to related work (some of which is useful; much of which is pandering to likely peer reviewers in small, niche fields). Length restrictions seem equally foolish in the age of digital publishing. And again, inadvertent pedantry is the only explanation I can imagine... but happy to be wrong.
This may be intentional - for most academic publishing venues, their target audience is other researchers of that domain. It's counterproductive to optimize an article for a lay audience if everybody in the actual audience has (or is working on) a PhD in that domain and is generally expected to be (or become) up to speed with the terminology and earlier results on that topic. The journal or conference is generally not meant to communicate their results to wider audience, it's generally a tool that some particular research community made for themselves to help their research by an exchange of ideas and results. An article in academic publishing is meant for someone who will use that to further their own research in a related field - and the needs of a person like that are very far from a lay audience, they will want to see entirely different things in that paper - you want the paper to focus on the novelty, on the "delta" between this result and what was bleeding edge a few months ago; not spend two thirds of the paper describing what was already known before.
There are publishing venues intended for a lay audience, but most academic publishing is not, they have incompatible goals.
And perversely, this makes it so that people feel compelled to add mathy things that add no additional explanatory power to the results.
I've frequently heard complaints that people in some CS subfields in particular will just add complicated math because they have to rather than because it helps. It sucks because the authors don't have that great a grasp on the math anyway, the readers don't care about the math -- they just care about the intuition and process, and communication suffers overall.
My best papers had at most like two equations, and they were just there to clarify models for fits and the like in case the reader wasn't familiar with terms like "Arrhenius relation". So, you know, to actually make reading it less burdensome rather than just making it look more sophisticated.
Here are my thoughts based on my experience with peer reviewed publication.
There are two high level criteria for publication: novelty and difficulty (this is in my field of Programming Languages and Systems so keep that in mind).
The novelty requirement is important and I trust that you satisfied it but (as you pointed out in a child comment) you may not have met the difficulty requirement and the reviewer did their "best" to articulate that in a way that isn't the all together ridiculous "not hard enough".
Naturally we might wonder why "difficulty" is a requirement at all. Shouldn't the importance or impact of the work be the thing that matters regardless of how difficult it was to achieve? The problem is that it's _extremely_ hard to know what work will be impactful and so reviewers, who have to reject something like 90% of submissions, use the heuristic of "difficulty" to estimate.
This is a problem to be sure but I think it would be a problem in other settings as well.
> A lot of this seems to be a problem with academic publishing, peer review, and the pedantry contained therein.
Ayup. It's too annoying to translate the academic speak, but you can shortcut it.
For example, anything CS nowadays (I include crypto in this) must be 1) publicly available on the web, 2) come with running code that runs some basic tests, and 3) be limited to a single compilable file. Failure in any of the criteria means I move on. If the source code involves anything like "configure" it's an immediate fail. If the code passes all three of those criteria, it's probably more useful to read the code rather than the paper.
I used to love ISSCC for the reason that they used to demand both A) a die photograph of a chip and B) actual oscilloscope traces. You can't hide when you have to make test equipment produce data. Sadly, they got rid of that requirement in the late 90's, and the information content of the conference suffered correspondingly.
If what you write is difficult to understand people will assume you're smart. If you've ever learned a foreign language you'll recognize this: Blithering idiots talking in that language sound smart because you're having a hard time understanding them.
In my experience, papers often take more than just two submissions to be published. It's often a question of finding a good community fit. One paper I coauthored took 8 years and probably 10 submissions before it was finally accepted.
Length restrictions can be a bummer, but if you have a publishable result that you simply can't squeeze into 10-20 pages (depending on the venue), typically you split it in to two or more publications. This has the added advantage of ensuring that each published unit is a smaller, tighter piece of work. I don't think it's just pedantry.
Carl Sagan was famously denied tenure at Harvard and membership in the National Academy of Sciences for his science advocacy. Different takes on it range from "jealousy" of his peers over his broader popularity to anger from them for his making science accessible and explaining concepts in a way that allowed readers to understand them:
I used to see this with my peers in computer programming in the 1990s. There was a lot of anger and jealousy when everyday normal people started putting up websites. Several of my CS friends were of the opinion that this was almost polluting the WWW with bad code.
"I know of at least one "seminal" robotics paper that was rejected from top venues multiple times for "simplicity" (lack of mathematics content) that went on to become a foundational paper in the field after appearing in a lower-tier venue years later."
Correct me if I'm wrong, but if you don't value what a publisher is offering, you could publish your paper on your own, make it accessible to an audience of your choosing with a delivery of your choice (for example, the wonderful work of Aphyr, here: http://jepsen.io/).
I don't get why people would try to publish in high-tier venue. To me it seems much more about polishing one's ego instead of improving the research quality.
"What is the role of human scientists in an age when the frontiers of scientific inquiry have moved beyond the comprehension of humans?"
The above quote is from Ted Chiang's short story "The Evolution of Human Science," originally published in Nature as "Catching crumbs from the table" [0]. It's a brilliant depiction of this very problem: when new developments contribute to an increasing gap between those who can make new developments, and those attempting to understand the state of the art, the entire process of scientific inquiry becomes less efficient. In fact, the scenario depicted is one where the majority of researchers become "distillers," to use the language of the original post.
While Chiang posits a science-fiction reason for the divide, "normal" research/technical debt is insidious as well. Without incentives to reduce debt, the knowledge gap widens until only a handful of experts can make significant contributions. It's a problem that needs to be tackled head-on in both research and engineering. I'd love to see more initiatives like Distill.
There's another subtle aspect to this: the same or very similar ideas, methods, and tools show up or are reinvented again and again, under different guises, in different disciplines and subfields that have their own jargon, unnecessarily making human comprehension even harder.
I can't count how many times I've invested meaningful time and effort to grok the key ideas and intuition of a new AI/DL/ML paper, only to feel that those ideas and intuitions could have been explained much better, less formally, with a couple of napkin diagrams.
Alas, authors normally have no incentive (or time, for that matter!) to publish nice concise explanations of their intuitions with easy-to-follow diagrams and clear notation... so the mountain of research debt continues to grow to the detriment of everyone.
I LOVE what Olah, Carter et al are trying to do here.
I really love this effort. Research papers are low bandwidth way to get information into our brains. They take a lot of effort to read, even if the ideas are not particularly complicated. Often when reading complicated material, I have to come up with metaphors in my mind to make sense of it. This is somewhat of a wasted effort, as the author who wrote the material surely had metaphors for their own mind when writing, but too often they don't share these metaphors, and stick to purely technical writing. I think this is one reason why ideas like general relativity are so popular, even though the material is actually quite complicated. The average educated person can give a reasonable explanation of general relativity because the metaphors used to explain it are so powerful, even though its very unlikely they understand any of the math involved.
I've often thought it would be great for the Arxiv to make it easy to link to a video of the 'talk' that generally goes with a paper. The talk is very often the distillation of the ideas in the paper, as conducted by one of the involved researchers. Indeed, one of the main points of conferences is to allow us to trade these distilled versions of our research with one another and place them in the context of everything else going on...
> Noise – Being a researcher is like standing in the middle of a construction site. Countless papers scream for your attention and there’s no easy way to filter or summarize them.2 We think noise is the main way experts experience research debt.
This is a big part of how I don't understand why some type of annotation standard hasn't taken off for research papers. Everyone does the same duplicative, time-consuming work of turning a paper into knowledge in their own head, so many wheels are reinvented. Where is the GitHub for research ideas?
I like the ideas put forth in this article. I wonder, though, if "distillation" is a re-casting of "scholarship" as considered by the humanities.
People studying topics ranging from Biblical Studies to History to Literature often do not create new source material, unlike in STEM. Yet there is a large degree of effort taken to "distill" existing facts through new lenses, producing novel concepts and interpretations. These efforts can transform our understanding of many areas of human endeavour.
One thing I believe to be of great value which is not made explicit in this article (which I think is an awesome article), is that research debt, as they describe it, is basically _education_.
In other words, improve the educational resources for complex subjects.
In our age where we're blessed with cheap printing of books and the possibility of creating complex interactive media, I think the question of designing user-friendly, powerful, and beautiful educational resources is a huge opportunity and pressing question.
Not just for people seeking to achieve a research-level understanding of a complex subject, but for all subjects and all people.
Consider the social value of beautiful, well-designed and nontrivial educational material for mathematics or basic science being widely available for all classes of people at all ages.
I'd argue that when news organizations use infographics or interactive journalism at its best, they are also performing this educational function.
Sorry for the long post, but to summarize, I think it's useful to recognize research debt as a specific case of the art and practice of creating media for education.
There are 'distillers' of large bodies of scientific research. Traditionally, they are science communicators, and more specifically science journalists.
The goal of a practicing scientist is very much at odds with someone whose job is to translate science into larger audiences. I've had very well-intentioned rational research scientists tell me with a straight face that "my job is to produce science results, not to communicate it. that's someone else's job", usually with the attitude that it's less respected or somehow self-aggrandizing. "The best science will be self-evident" attitude that all researchers secretly aspire for, not realizing that 99% of impactful science has had effort spent to promote, frame, or distribute it.
This weird stereotype is somehow beaten into scientists from the very beginning, and I haven't been able to figure out where this comes from. Obviously, yes, it's a lack of tools and accessibility into letting scientists also become distillers themselves. But the motivations and incentives at the center of the whole system is what's making this whole imbalance. I think there are parts of our research system that actually say "No, you cannot and should not distill your science".
Ultimately, for me, it gets back to funding. If review articles and outreach weighed just as much as citation count in tenure and grant committees, then maybe this could start to change. Yet, these committees still don't value open access, and look how tough that battle has been.
Also - this solution is really great and commendable, but I don't see how this works outside of ML/CS where research outputs are more like software development - gists, snippets, prototypes that are immediately shared, pushed, forked. More science fields like ecology, synthetic biology, anthropology, will look like this, but it will take a few human generations.
I think you miss the point. The article proposes a path for distillers whose target audience is researchers, not the lay public.
Science journalists are fairly poor distillers of knowledge, actually.
What's needed is more like a way for senior researchers to write more and better review papers that lay out and summarize all of the issues around a particular sub-field, for active researchers in that field.
Science communicators/journalists can turn "Methodological observation of the sociometrical behavior tendencies of prematurated isolates indicates that a casual relationship exists between groundward tropism and lachrimatory, or 'crying,' behavior forms." into "Scientists find that falling down makes babies cry" but they're less good at expressing concepts like "math equation forms the basis for all current modeling, papers A, B, and C each use a different special case of the equation to reach their conclusions."
Isaac Asimov anticipated the idea of a research distiller in "The Dead Past" with the character of Nimmo, a professional science writer:
"Nimmo received his first assignment at the age of twenty-five, after he had completed his apprenticeship and been out in the field for less than three months. It came in the shape of a clotted manuscript whose language would impart no glimmering of understanding to any reader, however qualified, without careful study and some inspired guesswork. Nimmo took it apart and put it together again (after five long and exasperating interviews with the authors, who were biophysicists), making the language taut and meaningful and smoothing the style to a pleasant gloss."
In the story Nimmo has less prestige than a "real researcher" but the role pays well and he is in high demand.
I don't see anything wrong with the "textbook" -> "review" -> "article" strategy to climbing a mountain of debt. A good review paper should hit a sweet spot in terms of exposition, digestion, abstraction, and noise filtering. Transformative new ways of visual thinking, well, that's a different story.
The problem is just that the pace of research in the authors field is too fast at the moment. What's the hurry? Over time, the citation graph will reveal the most significant work, and the community will naturally distill that research for maximum effect. The danger is that beautiful distillation of an extremely "niche topic" will not change the fact that it has limited scope and may even limit abstraction. Of course, I say danger with tounge-in-cheek...
You're right that reviews and textbooks do valuable distillation, but I'd claim we can do much much better.
I think we can distill things massively better -- create massively better explanations and ways of thinking about ideas -- if only we invested more in it. It isn't just when the distillation happens, it's the quality.
That's a really bold claim and all I can do is point to the occasional example where someone put effort into distilling and did something wonderful. My intuition is that these aren't one-off miracles, but are the expected result when the community takes this really seriously.
The problem is that distillation is requires focused energy. Reviews and textbooks give us a bit, but it's diffuse. I think we can do much better, and I hope we'll be able to demonstrate that with Distill.
I do think that the ML / CS / etc. community is actually more open than other academic fields, and so this is definitely the right subfield to start in. Putting open access preprints online is not common practice in all disciplines, although it really should be.
I wonder if it makes sense for Distill to also publish on fields outside of pure ML - e.g. as applied to specific problems in other domains. I work in materials informatics, and I suspect that research in such fields (ML + applied sciences) might benefit quite a bit from having key results 'distilled' in this format.
In the very long run, I'd like to see Distill or Distill-like journals cover all of math/cs/science.
But I think the right approach is to start with a narrow topic and do really well there. I guess startup people would say that we're focusing on a single vertical. :)
We haven't done very good line drawing for journal scope yet, and I'm not sure how we'll handle cross-disciplinary work.
Article is really good, but it's reference to the pi vs tau debate is kind of silly. It really isn't a big deal. When I did my math phd I wrote 2 pi all the time, but this didn't matter. The tiny convenience gained by changing to tau is totally trivial and doesn't even deserve a mention compared to the rest of the things in the article.
2 pi doesn't seem like a big deal to you because you've internalized the notation. You paid the cost a long time ago (and perhaps it was a small one for you).
I think it can be hard to empathize with what it's like to be a beginner. I learned about the pi definition debate a few years after I learned trigonometry -- there were a bunch of essays prior to the tauday one -- and it seemed tremendously better. I was also still in high school at that point, and I saw students struggling daily with these ideas and it seemed like part of the pi definition was the stumbling block.
So, of course the pi vs 2 pi thing looks trivial to a mature mathematician: we aren't the people paying the cost.
> but it's reference to the pi vs tau debate is kind of silly
This struck me as odd as well, but here's a possible justification.
The ramshackleness of people's understanding, the degree to which it's compromised by misconceptions, grows surprisingly rapidly as one moves away from their specific research focus. A rule of thumb is a post-doc in a subfield, looks elsewhere in the field like a graduate or undergraduate student, and in other fields like an undergraduate or pre-college student. Because that's when they last wrestled with the subject. And science education research shows the wrestling isn't going very well.
It can be startling to be told by first-tier (non-astronomy) physical sciences grad students "the Sun doesn't have a color - it's lots of different colors - it's rainbow colored". Or to encounter first-tier medical school grad students, with no idea how big a red blood cell is, beyond "really really small".
With science and engineering education working so badly, I feel uncomfortable dismissing even "trivial" improvements to widespread abstractions, without looking for education research. I don't feel I can reliably judge how badly it might be screwing us.
When I've talked to senior researchers about these problems, they say that they have no problem finding distilled information about new results; they get it from in-person conversations at conferences that they attend frequently. The publishing of distilled research would most benefit low-status, newer researchers (like Ph.D. students), but it needs to be valued by senior researchers to make it into the incentive systems of hiring and grant funding. It seems like a tricky problem to fix the incentives here.
I think one of the biggest things missing is a means to communicate openly about published research. What I'd like to see is a forum for every paper, and this forum should be properly moderated (perhaps by a peer-review system).
Such a forum could make it much easier to decipher published work, and to fill in details which were missing. Also, errors in publications become clear more quickly.
Brilliant thing! As a person pursuing a PhD I'd something that is in my opinion the best way to avoid research debt: have a good tutor or a good group (peers). This way you can learn new things (or ask for something you don't understand) and really get to the peak sooner that doing everything alone.
3blue1brown's Essence of Linear Algebra series on YouTube should get some sort of honorary inclusion in Distill. There have been many "visualization of algorithms" posts on HN over the years. A collection of those would be good as well.
What I think is funny is that so many people complain about how bad science has become and how the papers and funding institutions enforce a very ineffective way of doing it. Why is nobody attempting or proposing new ways to make money with science? E.g. have people tried to do science in a subscription based model (like artists)? Or using free pappers+consulting on how to replicate the experiments or apply them in real projects (like open source)?
Ooooh. I've actually been working on a blog post where I try reading a science paper (knowing only a little about the science) and learn / explain as I go.
This seems like the better way to do it (my way is taking way to many words) - something like better science reporting.
My concern is the act of writing - Medium has a real nice web editor. What can I use to write an article using this HTML/CSS/JS without literally writing the HTML?
Shameless self promotion: I've been doing the same thing, for systemsy papers. http://blog.elvinyung.com/
I genuinely think it's really unfortunate that academic research are often so hard to approach without getting used to it. Often the concepts underneath are very cool and useful, and aren't actually that complicated. There is much to benefit from bridging the gap between academia and the wider community.
As an (aspirant) programming language theorist, we have a really great advantage in this field: advances in the field of prog lang have a natural distillation process: getting into a "real" programming language.
And it's great, it means I can toy with features and consider "what could I do if it was in a real language ?". Also, we can observe idioms and usage that gets developed when some advanced feature start being used by "normal" programmers.
Computer science, in general, does have the advantage that the distance to applications is quite often much shorter than math, which forces part of this distillation process to proceed a bit quicker.
On the other hand, the formalization is highly non-uniform, due to how young it is as a science.
In my experience this is already being practiced. Maybe it's different in machine learning, but in type theory simpler explanations are well worth publishing and are published all the time even in high impact venues.
As far as I can tell, progress towards eliminating "research debt" takes two forms: on the one hand there is a place for good exposition, typically in the form of textbooks, and on the other hand concepts get better understood over time and people come up with simpler explanations. In both cases the results can already be published...
Is the situation in machine learning really so bad that nobody is going to publish simpler explanations of known results?
[+] [-] beambot|9 years ago|reply
I tried (twice!) to publish a paper that was scientifically sound, but written such that it could be understood by a lay audience. It was rejected; quoting a reviewer: "This paper lacks math." That sentiment made me lose a lot of faith in academia. Now, I simply refuse to contribute any more time to writing papers or performing peer review (esp. in non-open publications). I'm sure I'm not alone; I know of at least one "seminal" robotics paper that was rejected from top venues multiple times for "simplicity" (lack of mathematics content) that went on to become a foundational paper in the field after appearing in a lower-tier venue years later.
The irony: it takes researchers a lot of time to make a paper dense & concise. If they "showed all steps", it would probably improve researcher productivity & make the material more approachable to newcomers. Instead publishers enforce length restrictions... for which authors dedicate upto 25% to related work (some of which is useful; much of which is pandering to likely peer reviewers in small, niche fields). Length restrictions seem equally foolish in the age of digital publishing. And again, inadvertent pedantry is the only explanation I can imagine... but happy to be wrong.
[+] [-] PeterisP|9 years ago|reply
There are publishing venues intended for a lay audience, but most academic publishing is not, they have incompatible goals.
[+] [-] neltnerb|9 years ago|reply
I've frequently heard complaints that people in some CS subfields in particular will just add complicated math because they have to rather than because it helps. It sucks because the authors don't have that great a grasp on the math anyway, the readers don't care about the math -- they just care about the intuition and process, and communication suffers overall.
My best papers had at most like two equations, and they were just there to clarify models for fits and the like in case the reader wasn't familiar with terms like "Arrhenius relation". So, you know, to actually make reading it less burdensome rather than just making it look more sophisticated.
[+] [-] johnbender|9 years ago|reply
There are two high level criteria for publication: novelty and difficulty (this is in my field of Programming Languages and Systems so keep that in mind).
The novelty requirement is important and I trust that you satisfied it but (as you pointed out in a child comment) you may not have met the difficulty requirement and the reviewer did their "best" to articulate that in a way that isn't the all together ridiculous "not hard enough".
Naturally we might wonder why "difficulty" is a requirement at all. Shouldn't the importance or impact of the work be the thing that matters regardless of how difficult it was to achieve? The problem is that it's _extremely_ hard to know what work will be impactful and so reviewers, who have to reject something like 90% of submissions, use the heuristic of "difficulty" to estimate.
This is a problem to be sure but I think it would be a problem in other settings as well.
[+] [-] bsder|9 years ago|reply
Ayup. It's too annoying to translate the academic speak, but you can shortcut it.
For example, anything CS nowadays (I include crypto in this) must be 1) publicly available on the web, 2) come with running code that runs some basic tests, and 3) be limited to a single compilable file. Failure in any of the criteria means I move on. If the source code involves anything like "configure" it's an immediate fail. If the code passes all three of those criteria, it's probably more useful to read the code rather than the paper.
I used to love ISSCC for the reason that they used to demand both A) a die photograph of a chip and B) actual oscilloscope traces. You can't hide when you have to make test equipment produce data. Sadly, they got rid of that requirement in the late 90's, and the information content of the conference suffered correspondingly.
[+] [-] tormeh|9 years ago|reply
Academese exists because it works.
[+] [-] ttd|9 years ago|reply
Length restrictions can be a bummer, but if you have a publishable result that you simply can't squeeze into 10-20 pages (depending on the venue), typically you split it in to two or more publications. This has the added advantage of ensuring that each published unit is a smaller, tighter piece of work. I don't think it's just pedantry.
[+] [-] ideonexus|9 years ago|reply
https://en.wikipedia.org/wiki/Carl_Sagan
I used to see this with my peers in computer programming in the 1990s. There was a lot of anger and jealousy when everyday normal people started putting up websites. Several of my CS friends were of the opinion that this was almost polluting the WWW with bad code.
[+] [-] chestervonwinch|9 years ago|reply
Why didn't you place the additional detail in an appendix, so as to not detract from the main points?
[+] [-] plinkplonk|9 years ago|reply
Curious. Which paper is this?
[+] [-] Tunabrain|9 years ago|reply
[+] [-] Fiahil|9 years ago|reply
I don't get why people would try to publish in high-tier venue. To me it seems much more about polishing one's ego instead of improving the research quality.
[+] [-] unknown|9 years ago|reply
[deleted]
[+] [-] btown|9 years ago|reply
The above quote is from Ted Chiang's short story "The Evolution of Human Science," originally published in Nature as "Catching crumbs from the table" [0]. It's a brilliant depiction of this very problem: when new developments contribute to an increasing gap between those who can make new developments, and those attempting to understand the state of the art, the entire process of scientific inquiry becomes less efficient. In fact, the scenario depicted is one where the majority of researchers become "distillers," to use the language of the original post.
While Chiang posits a science-fiction reason for the divide, "normal" research/technical debt is insidious as well. Without incentives to reduce debt, the knowledge gap widens until only a handful of experts can make significant contributions. It's a problem that needs to be tackled head-on in both research and engineering. I'd love to see more initiatives like Distill.
[0] http://www.nature.com/nature/journal/v405/n6786/full/405517a... - a highly recommended companion piece to the original post.
[+] [-] cs702|9 years ago|reply
Tibshirani's "glossary" of ML and Statistics terms is a canonical example: http://statweb.stanford.edu/~tibs/stat315a/glossary.pdf
[+] [-] specialist|9 years ago|reply
Expansion while a problem space is explored, drunken sailor style. Contraction and consolidation as best fit solutions are identified and adopted.
Technical debt due to entropy, obsolescence, communication lag (diffusion of innovation), pride, etc.
---
Oh. This reminds me. TODO: read up on facilities management, see how they deal with this. Stuff like scheduling capex and funding maintenance.
[+] [-] cs702|9 years ago|reply
I can't count how many times I've invested meaningful time and effort to grok the key ideas and intuition of a new AI/DL/ML paper, only to feel that those ideas and intuitions could have been explained much better, less formally, with a couple of napkin diagrams.
Alas, authors normally have no incentive (or time, for that matter!) to publish nice concise explanations of their intuitions with easy-to-follow diagrams and clear notation... so the mountain of research debt continues to grow to the detriment of everyone.
I LOVE what Olah, Carter et al are trying to do here.
[+] [-] akyu|9 years ago|reply
[+] [-] sdenton4|9 years ago|reply
[+] [-] tedmiston|9 years ago|reply
This is a big part of how I don't understand why some type of annotation standard hasn't taken off for research papers. Everyone does the same duplicative, time-consuming work of turning a paper into knowledge in their own head, so many wheels are reinvented. Where is the GitHub for research ideas?
[+] [-] pcrh|9 years ago|reply
People studying topics ranging from Biblical Studies to History to Literature often do not create new source material, unlike in STEM. Yet there is a large degree of effort taken to "distill" existing facts through new lenses, producing novel concepts and interpretations. These efforts can transform our understanding of many areas of human endeavour.
[+] [-] colah3|9 years ago|reply
[+] [-] adamsea|9 years ago|reply
In other words, improve the educational resources for complex subjects.
In our age where we're blessed with cheap printing of books and the possibility of creating complex interactive media, I think the question of designing user-friendly, powerful, and beautiful educational resources is a huge opportunity and pressing question.
Not just for people seeking to achieve a research-level understanding of a complex subject, but for all subjects and all people.
Consider the social value of beautiful, well-designed and nontrivial educational material for mathematics or basic science being widely available for all classes of people at all ages.
I'd argue that when news organizations use infographics or interactive journalism at its best, they are also performing this educational function.
Sorry for the long post, but to summarize, I think it's useful to recognize research debt as a specific case of the art and practice of creating media for education.
[+] [-] dluan|9 years ago|reply
The goal of a practicing scientist is very much at odds with someone whose job is to translate science into larger audiences. I've had very well-intentioned rational research scientists tell me with a straight face that "my job is to produce science results, not to communicate it. that's someone else's job", usually with the attitude that it's less respected or somehow self-aggrandizing. "The best science will be self-evident" attitude that all researchers secretly aspire for, not realizing that 99% of impactful science has had effort spent to promote, frame, or distribute it.
This weird stereotype is somehow beaten into scientists from the very beginning, and I haven't been able to figure out where this comes from. Obviously, yes, it's a lack of tools and accessibility into letting scientists also become distillers themselves. But the motivations and incentives at the center of the whole system is what's making this whole imbalance. I think there are parts of our research system that actually say "No, you cannot and should not distill your science".
Ultimately, for me, it gets back to funding. If review articles and outreach weighed just as much as citation count in tenure and grant committees, then maybe this could start to change. Yet, these committees still don't value open access, and look how tough that battle has been.
Also - this solution is really great and commendable, but I don't see how this works outside of ML/CS where research outputs are more like software development - gists, snippets, prototypes that are immediately shared, pushed, forked. More science fields like ecology, synthetic biology, anthropology, will look like this, but it will take a few human generations.
[+] [-] hprotagonist|9 years ago|reply
Science journalists are fairly poor distillers of knowledge, actually.
What's needed is more like a way for senior researchers to write more and better review papers that lay out and summarize all of the issues around a particular sub-field, for active researchers in that field.
[+] [-] bloaf|9 years ago|reply
[+] [-] tominous|9 years ago|reply
"Nimmo received his first assignment at the age of twenty-five, after he had completed his apprenticeship and been out in the field for less than three months. It came in the shape of a clotted manuscript whose language would impart no glimmering of understanding to any reader, however qualified, without careful study and some inspired guesswork. Nimmo took it apart and put it together again (after five long and exasperating interviews with the authors, who were biophysicists), making the language taut and meaningful and smoothing the style to a pleasant gloss."
In the story Nimmo has less prestige than a "real researcher" but the role pays well and he is in high demand.
[+] [-] cing|9 years ago|reply
The problem is just that the pace of research in the authors field is too fast at the moment. What's the hurry? Over time, the citation graph will reveal the most significant work, and the community will naturally distill that research for maximum effect. The danger is that beautiful distillation of an extremely "niche topic" will not change the fact that it has limited scope and may even limit abstraction. Of course, I say danger with tounge-in-cheek...
[+] [-] colah3|9 years ago|reply
I think we can distill things massively better -- create massively better explanations and ways of thinking about ideas -- if only we invested more in it. It isn't just when the distillation happens, it's the quality.
That's a really bold claim and all I can do is point to the occasional example where someone put effort into distilling and did something wonderful. My intuition is that these aren't one-off miracles, but are the expected result when the community takes this really seriously.
The problem is that distillation is requires focused energy. Reviews and textbooks give us a bit, but it's diffuse. I think we can do much better, and I hope we'll be able to demonstrate that with Distill.
[+] [-] PaulHoule|9 years ago|reply
[+] [-] colah3|9 years ago|reply
Fixing that is basically the goal of Distill: build an ecosystem where this kind of work is supported and rewarded.
[+] [-] eddotman|9 years ago|reply
I do think that the ML / CS / etc. community is actually more open than other academic fields, and so this is definitely the right subfield to start in. Putting open access preprints online is not common practice in all disciplines, although it really should be.
I wonder if it makes sense for Distill to also publish on fields outside of pure ML - e.g. as applied to specific problems in other domains. I work in materials informatics, and I suspect that research in such fields (ML + applied sciences) might benefit quite a bit from having key results 'distilled' in this format.
[+] [-] colah3|9 years ago|reply
But I think the right approach is to start with a narrow topic and do really well there. I guess startup people would say that we're focusing on a single vertical. :)
We haven't done very good line drawing for journal scope yet, and I'm not sure how we'll handle cross-disciplinary work.
[+] [-] CogitoCogito|9 years ago|reply
[+] [-] colah3|9 years ago|reply
I think it can be hard to empathize with what it's like to be a beginner. I learned about the pi definition debate a few years after I learned trigonometry -- there were a bunch of essays prior to the tauday one -- and it seemed tremendously better. I was also still in high school at that point, and I saw students struggling daily with these ideas and it seemed like part of the pi definition was the stumbling block.
So, of course the pi vs 2 pi thing looks trivial to a mature mathematician: we aren't the people paying the cost.
[+] [-] mncharity|9 years ago|reply
This struck me as odd as well, but here's a possible justification.
The ramshackleness of people's understanding, the degree to which it's compromised by misconceptions, grows surprisingly rapidly as one moves away from their specific research focus. A rule of thumb is a post-doc in a subfield, looks elsewhere in the field like a graduate or undergraduate student, and in other fields like an undergraduate or pre-college student. Because that's when they last wrestled with the subject. And science education research shows the wrestling isn't going very well.
It can be startling to be told by first-tier (non-astronomy) physical sciences grad students "the Sun doesn't have a color - it's lots of different colors - it's rainbow colored". Or to encounter first-tier medical school grad students, with no idea how big a red blood cell is, beyond "really really small".
With science and engineering education working so badly, I feel uncomfortable dismissing even "trivial" improvements to widespread abstractions, without looking for education research. I don't feel I can reliably judge how badly it might be screwing us.
[+] [-] mballantyne|9 years ago|reply
[+] [-] amelius|9 years ago|reply
Such a forum could make it much easier to decipher published work, and to fill in details which were missing. Also, errors in publications become clear more quickly.
[+] [-] unboxed_type|9 years ago|reply
[+] [-] haddr|9 years ago|reply
[+] [-] killjoywashere|9 years ago|reply
[+] [-] erikb|9 years ago|reply
[+] [-] RangerScience|9 years ago|reply
This seems like the better way to do it (my way is taking way to many words) - something like better science reporting.
My concern is the act of writing - Medium has a real nice web editor. What can I use to write an article using this HTML/CSS/JS without literally writing the HTML?
[+] [-] elvinyung|9 years ago|reply
I genuinely think it's really unfortunate that academic research are often so hard to approach without getting used to it. Often the concepts underneath are very cool and useful, and aren't actually that complicated. There is much to benefit from bridging the gap between academia and the wider community.
[+] [-] thanatropism|9 years ago|reply
So what are you doing, generating images by hand?
[+] [-] Tyr42|9 years ago|reply
[+] [-] sacheendra|9 years ago|reply
[+] [-] Drup|9 years ago|reply
And it's great, it means I can toy with features and consider "what could I do if it was in a real language ?". Also, we can observe idioms and usage that gets developed when some advanced feature start being used by "normal" programmers.
Computer science, in general, does have the advantage that the distance to applications is quite often much shorter than math, which forces part of this distillation process to proceed a bit quicker. On the other hand, the formalization is highly non-uniform, due to how young it is as a science.
[+] [-] fmap|9 years ago|reply
As far as I can tell, progress towards eliminating "research debt" takes two forms: on the one hand there is a place for good exposition, typically in the form of textbooks, and on the other hand concepts get better understood over time and people come up with simpler explanations. In both cases the results can already be published...
Is the situation in machine learning really so bad that nobody is going to publish simpler explanations of known results?