One explained to me here that the problem is they're mainly rewarded by funding agencies for how many papers they publish. That's why quality went down in general. Then, since funding agencies aren't rewarding code, they're either not encouraged to get it right or actively discouraged from doing it to focus on next paper.
This sad situation is both bad for open source and science. Bad for science since inaccurate results that probably aren't even reproducible aren't the knowledge advancements we need. Funding agencies like NSF need to change their policies to address the root cause here. Universities will follow where the incentives lead them.
Then some other academic can get credit for publishing a rigorous paper describing the actual algorithm, explaining why the original paper is wrong (without speculating on the motive, just the facts), and why the approach taken in the code is in fact better. Bonus points for describing a further improvements, with working code.
In machine learning, you don't get credit for publishing rigorous papers. You get credits for publishing papers that show improved performance:
One big challenge the community faces is that if you want to get a paper published in machine learning now it's got to have a table in it, with all these different data sets across the top, and all these different methods along the side, and your method has to look like the best one. If it doesn’t look like that, it’s hard to get published. I don't think that's encouraging people to think about radically new ideas.
Now if you send in a paper that has a radically new idea, there's no chance in hell it will get accepted, because it's going to get some junior reviewer who doesn't understand it. Or it’s going to get a senior reviewer who's trying to review too many papers and doesn't understand it first time round and assumes it must be nonsense. Anything that makes the brain hurt is not going to get accepted. And I think that's really bad.
I guess people look at statistical machine learning and deep learning, see all the formulae and hear all the calculus terminology and think - "oh, wow, that's a really rigorous field! Look at all the formalisms!".
But it's not. It's an extremely, almost exclusively, empirical field. The mathiness and the formulae are just unfortunate attempts to pass off the whole endeavour as something that it's not- some kind of careful science that uncovers deep truths about intelligence and cognition. In truth, it's all just about beating other peoples' systems in very specific benchmarks.
If it wasn't for this culture of pretensions to higher science, machine learning papers would most likely be written with much more clarity than they are now and mistakes like the one described in the above article would be rare.
I've been quite disappointed to see how many papers from "cutting edge" research groups are chasing small improvements in well-known benchmarks by finding new techniques that happen to work, while there is a lot less effort put into finding out why. I guess Geoff Hinton's explanation about what gets published today explains it.
Yet there are approximately 0 papers doing that. Flawed or not not, the original paper still beats the socks off your approach on novelty. And (perceived) novelty is the lifeblood of the acedemic.
There is a phenomenon in academia I call the "pissing on a lamppost" phenomenon. A well-known researcher mentions his thoughts on (topic) in the penultimate paragraph of a paper on something else. When you talk to them, they say "oh, I don't actually know how to do that, but I think it should work!" So some sucker like me goes & figures out how it actually works, but... BigName has already discussed it so it's not a new result (see citation for penultimate paragraph) so we are not interested in publishing it :)
You can get around it if you show BigName is wrong but if they've been vague enough that's hard.
This sad situation is both bad for open source and science. Bad for science since inaccurate results that probably aren't even reproducible aren't the knowledge advancements we need. Funding agencies like NSF need to change their policies to address the root cause here. Universities will follow where the incentives lead them.