
Troubling trends in machine learning scholarship (2018) - scottlocklin
https://arxiv.org/abs/1807.03341
======
ramraj07
This is not just isolated to machine learning, but most technical fields (at
least one other to my knowledge).

I used to work in microscopy image analysis and the papers often would
obfuscate the fact that they were not exactly doing anything new by using what
looks like fancy math and some trendy names.

One of the most outrageous examples is this "high profile" paper that says it
does compressive sensing with superresolution microscopy -
[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3477591/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3477591/)
except I don't think they do; the math when you remove the bullshit sounds
more like deconvolution than anything else (and the results are only as good).
Yet, it got reviewed and accepted by Nature Methods, and is cited by 360
papers already. Why? Apparently no one in this field knows what compressive
sensing really means. At least one professor in the field when I confronted
him, just said he doesn't have time to go through compressive sensing
literature first before evaluating this paper.

What's the root cause? Frankly I'd argue the majority of professors nowadays
aren't smart in innovation but smart in hustling. Because hustlers are who
become professors in today's academic climate. They are able to publish good
papers still if the field isn't mature, but if the field is saturated, you
have to be really smart to make meaningful progress, and these hustlers are
not. So they just try to find some way to wrap meaningless progress in fancy
math and shove it in papers. The papers also go to reviewers who are similar
hustlers (not every paper can be reviewed by Hinton ) so they either don't
notice the problem or they do but let it slide because it's just their
colleague (yay for journals asking for "suggested reviewers" to the authors
itself).

~~~
godelski
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](https://en.wikipedia.org/wiki/Hanlon%27s_razor)

~~~
trophycase
I see Hanlon's Razor get throw around a lot, especially on HN. But does it
really have any basis in reality?

~~~
godelski
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.

------
joe_the_user
_" In this paper, we focus on the following four patterns that appear to us to
be trending in ML scholarship: (i) failure to distinguish between explanation
and speculation; (ii) failure to identify the sources of empirical gains,
e.g., emphasizing unnecessary modifications to neural architectures when gains
actually stem from hyper-parameter tuning; (iii) mathiness: the use of
mathematics that obfuscates or impresses rather than clarifies, e.g., by
confusing technical and non-technical concepts; and (iv) misuse of language,
e.g., by choosing terms of art with colloquial connotations or by overloading
established technical terms. "_

To their credit, the authors actually own-up to doing this themselves in
various papers. It seems like a way to describe the situation is that neural
nets have become such computational monsters that talking about them exactly
becomes very difficult with the language opaque and ambiguous.

~~~
gmueckl
I'd say a lack of a proper fundamental understanding of trained neural
networks is the main cause. People throw NNs at any problem they can think of,
get good results and when they want to publish, they come up with an
explanation that is more esoteric than founded in solid theory because the
monster they generated is so inscrutable.

~~~
recursive
The stuff is what the stuff is, brother.
[https://youtu.be/ajGX7odA87k?t=931](https://youtu.be/ajGX7odA87k?t=931)

~~~
gmueckl
Thanks! This is a great talk.

------
thanks1banks2
> "...overloading established technical terms. "

Subtle. ;)

------
cosmic_ape
previous discussion here:
[https://news.ycombinator.com/item?id=17497273](https://news.ycombinator.com/item?id=17497273)

