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Re:Troubling trends in machine learning scholarship (2018) by godelski on Sunday December 01, 2019 @09:47PM (#21679463)
I'm not so sure that it is hustling, though I'm sure that exists. I'm a firm believer of Hanlon's Razor [0], and we shouldn't rush to attribute malice.

I do think there is a problem that our breadth of knowledge, as humans, is far larger than what one person can understand. There's famous examples of revolutions in science being claimed as mundane results. Topologists said Nash was just applying topology to economics and it was nothing new (to them). Mathematicians saw Einstein's results as unsurprising because of the tensor analysis. (Some of these are over exaggerated and there's definitely a post hoc superiority complex in play). But if we just take this at face value, is any of this bad? I would argue no, because it still takes someone to connect the dots between fields and push studies to think in those ways.

But one thing is for sure, as we gain more knowledge it is more likely that someone else independently discovers something that was already discovered. It is also more likely that some of these are rediscoveries of ideas that were not useful at the time.

I think there is a way to solve this though, but I'm not sure we can (yet). We need some good way to check research in a cross-disciplinary manner. Not only that, but in a highly technical way.

[0] https://en.wikipedia.org/wiki/Hanlon%27s_razor

Re:Troubling trends in machine learning scholarship (2018) by ramraj07 on Sunday December 01, 2019 @10:24PM (#21679579)
Regarding hanlons razor, I'm not making any accusations of malice. It could be malice, or it could be stupidity. Either way the results are the same. Frankly my opinion is that the line between the two is often quite blurred.

Regarding "undermining" novel innovation, you're right for sure. In this example, however, it's not undermining, I'm alleging they're not even doing compressive sensing, and that a competent unbiased reviewer would have caught it.

Re:Troubling trends in machine learning scholarship (2018) by godelski on Monday December 02, 2019 @02:51AM (#21680639)
> I'm not making any accusations of malice.

Sorry then, I read "hustling" as with intent. It definitely has negative connotation. Since it many times is seen as an act of fraud. Though strictly by definition this could be over zealousness and not malice, but it definitely has that connotation in vernacular use.

Re:Troubling trends in machine learning scholarship (2018) by BeetleB on Monday December 02, 2019 @12:23AM (#21680024)
> I'm not so sure that it is hustling, though I'm sure that exists. I'm a firm believer of Hanlon's Razor [0], and we shouldn't rush to attribute malice.

From what I saw in academia, it definitely was partial hustling. The thing is that it became so prevalent in some disciplines that the new generation now views this as "research" and not hustling.

Academics are no less immune to social proof. For them, academics is what academics do. And if most of them do this, then this is academics. The notion that it is problematic is waved away.

Re:Troubling trends in machine learning scholarship (2018) by btrettel on Sunday December 01, 2019 @10:12PM (#21679533)
> There's famous examples of revolutions in science being claimed as mundane results. Topologists said Nash was just applying topology to economics and it was nothing new (to them). Mathematicians saw Einstein's results as unsurprising because of the tensor analysis. (Some of these are over exaggerated and there's definitely a post hoc superiority complex in play).

Do you have any references for these examples?

Re:Troubling trends in machine learning scholarship (2018) by godelski on Monday December 02, 2019 @02:49AM (#21680634)
I could not find any through a quick search. Maybe it is convoluted search terms. I got a degree in physics and was pretty close to the math department. This is just stuff that I've heard from both departments and seen in a few documentaries. I had seen a direct source on Nash (IIRC someone upset about the Nobel), but that was years ago and I can't find it easily.

I did try to give a bunch of side notes to downplay and justify it in ways that may be conceited, but also reasonably human. I don't know if these stories I heard were a single person (I'd be surprised if there weren't at least one person!) or a larger group. I wouldn't be surprised if a ridiculous voice got amplified (can't think of any times that happened in modern times.....)

Re:Troubling trends in machine learning scholarship (2018) by scottlocklin on Monday December 02, 2019 @07:14AM (#21681741)
Of course, hustler types don't look at a career in science and think "how can I game this system" -probably not even in a high dollar field like ML (FWIIW I am OP).

But the reality is the present incentive system in the "sciences" is such that bullshit, hustling, trend following, sucking up to the associate dean, marketing chops and chasing fashion (to say nothing of politics and witch hunts) are rewarded, and honest diligent research isn't. This is one of the reasons actual breakthroughs (such as deep learning) are incredibly rare in the modern day, compared to, say, the way stuff worked in the 50s.

Re:Troubling trends in machine learning scholarship (2018) by godelski on Monday December 02, 2019 @12:45PM (#21684720)
> This is one of the reasons actual breakthroughs (such as deep learning) are incredibly rare in the modern day, compared to, say, the way stuff worked in the 50s.

I'd argue that the more we learn the harder it is to find major breakthroughs. Looking at Math and Physics as a model, I think this is clear, that major breakthroughs become more sparse as time passes. Luckily we have more eyes looking at things now, which helps a lot.

Re:Troubling trends in machine learning scholarship (2018) by scottlocklin on Monday December 02, 2019 @04:29PM (#21687158)
There are so many huge things to know about in physics ... so many gaping lacunae, I can't accept this. Sure nobody's thinking about them, instead preferring to fool around with non falsifiable piffle like noodle theory and "quantum information theory:" that's part of the problem.

Similarly in technology, there is much to do, but tinkering with hardware, craftsmanship; the things that worked in industrial labs in the old days: they're not done any more. Even making some simple piece of junk aluminum part, people waste time fooling around with solid designer and FEA instead of just handing a piece of graph paper to a former Navy machinist. There are entire books written in the post WW-2 era about fast development when people actually used to develop things quickly. Nobody does it. Well, the Russians and Chinese do, but they also develop big technology a lot faster than we do in the west.

I have no idea what's wrong with machine learning research; probably gratuitous abuse of grad students, people jumping after fashion, and lately brain drain.

Re:Troubling trends in machine learning scholarship (2018) by TeMPOraL on Monday December 02, 2019 @04:59AM (#21681156)
I'm usually a fan of Hanlon's razor, but I don't think it cuts cleanly when there are well-known and clearly visible incentive structures that strongly promote hustling. Attributing malice here seems closer to saying "water flows downhill" instead of "water flows randomly, and it so happened it randomly picked this direction".

EDIT: I also don't buy the dominance of stupidity for another reason: if you're being stupid, you'll make mistakes. But you can't obfuscate and bullshit without intent to do so.

Re:Troubling trends in machine learning scholarship (2018) by trophycase on Monday December 02, 2019 @01:57AM (#21680426)
I see Hanlon's Razor get throw around a lot, especially on HN. But does it really have any basis in reality?
Re:Troubling trends in machine learning scholarship (2018) by godelski on Monday December 02, 2019 @02:42AM (#21680604)
I think so. If you really try to understand how people become evil you will often find that they do it with good intentions. There's the extremely old adage that I'm sure has earlier origin than the following well known one (anyone know an earlier example?). "The path to Hell is paved with good intentions."

I think we'd agree that there are a large amount of people that do not do great things, momentarily or as a way of being. But how many people see themselves as bad? Very few. You can find tons of psych studies on this. Where people doing evil things justify it for many different reasons. "Just this one time", "for the greater good", "I have no other choice", "because _they_ are cheating", "the system is rigged against me", "fight fire with fire", etc. We all know these things. We've done them ourselves (to some extent or another).

I think a good example is politics. I think a lot of westerners like democracy (vague term). But how many imagine that if we were dictator for a day how we could just fix everything? Besides being naive and overestimating our intellectual prowess, it goes against the fundamental idea of a democracy. I'd argue that a lot of authoritarians see themselves in this way (there's good evidence to support this). That it is for the greater good. I'm sure you can think of at least two examples today that think that they have to control their countries because the people they rule over are not smart/civilized enough to know what is best for themselves.

Is this malice? I think that depends on the perspective. And that's really what Hanlon's Razor is about, perspective. Understanding the mind of the actor.

Re:Troubling trends in machine learning scholarship (2018) by circlefavshape on Monday December 02, 2019 @05:27AM (#21681281)
Hanlon's Razor is not a physical law, it's a heuristic, and a very useful one for stopping you thinking everyone is out to get you
Re:Troubling trends in machine learning scholarship (2018) by pretendscholar on Monday December 02, 2019 @01:04AM (#21680182)
Sounds like mathematicians need to collaborate more then.
Re:Troubling trends in machine learning scholarship (2018) by godelski on Monday December 02, 2019 @02:28AM (#21680552)
I for one would welcome this. I'm not sure if they push themselves into a corner or it is this idea that math is scary that pushes them into a corner. Probably some combination. But they definitely have some things that have a lot of practicality that is yet to be untapped.
Re:Troubling trends in machine learning scholarship (2018) by denzil_correa on Monday December 02, 2019 @06:27AM (#21681535)
> There's famous examples of revolutions in science being claimed as mundane results. Topologists said Nash was just applying topology to economics and it was nothing new (to them). Mathematicians saw Einstein's results as unsurprising because of the tensor analysis. (Some of these are over exaggerated and there's definitely a post hoc superiority complex in play).

Relevant XKCD "Fields of Purity" - https://www.xkcd.com/435/