
Machine Learning - tchalla
https://xkcd.com/1838/
======
Houshalter
Reminds me of this horrifying stack exchange post:
[https://stats.stackexchange.com/questions/185507/what-
happen...](https://stats.stackexchange.com/questions/185507/what-happens-if-
the-explanatory-and-response-variables-are-sorted-independently)

~~~
jmkni
ELI5?

~~~
TeMPOraL
The manager of the person who asked the question thinks that if you take data
in form of pairs (X, Y), split them up, sort independently and combine again,
you'll get better results. In fact, such operation _obviously_ destroys the
relationship X had to Y, so the result is meaningless.

~~~
jmkni
Ha, gotcha! Maybe if you do it enough times...in the cloud?

~~~
ardivekar
No no, results stored on The Blockchain.

~~~
pavel_lishin
A chatbot can read them back to us.

------
Sophira
Tom Scott yesterday made a video for laypeople on the topic of 'black box'
machine learning and how it can be difficult to get it to behave as you want,
too.[0]

It's an interesting watch - I'd recommend it if you're interested in learning
about it.

(Heck, I'd recommend the channel. Tom does some great videos on a number of
different topics.)

[0]
[https://www.youtube.com/watch?v=BSpAWkQLlgM](https://www.youtube.com/watch?v=BSpAWkQLlgM)

~~~
flinty
Isn't he the guy from the hello internet podcast?

~~~
cec
No, HI is Brady Haran and CGP Grey, though I suspect Brady may have
collaborated with Tom.

------
jey
"Just stir the pile until [the answers] start looking right" is actually a
pretty decent description of gradient descent.

~~~
dmitriy_ko
With gradient descent you are walking in the direction of negative gradient.
Stirring the pile implies that you are walking randomly from point to point.

~~~
TeMPOraL
No, by stirring you're just making the pile to move, and things descend down
the gradient of gravity. :).

~~~
steanne
The little things moreso. Brazil nut effect!

------
rcarmo
This is _so_ on point considering a lot of my customer interactions. Until I
go in and go over establishing a process framework that goes through the
models in use and sets criteria for their evaluation (even something as simple
as ROC), a lot of ML work in companies is mostly tinkering with things
(sometimes with wrong theoretical underpinnings) until it performs adequately.

The hype is only real if you systematically work it into measurable process,
not virtuoso jamming.

~~~
Kholo
Stone age tools as Chomsky calls them. The problem with simple to use tools,
is the simplicity masks two key facts from the user - whether aptitude for the
tool exists & amount of effort and time to mastery.

------
canjobear
That's some nice snarkiness about how modern machine learning works. But let's
not forget that this apparently "dumb" approach has beaten out much more
intelligent seeming systems on many tasks. To me, this means we shouldn't be
so confident that we know what an intelligent system looks like. Maybe
effectiveness in AI doesn't have much to do with human interpretability, and
even less to do with whether humans find the approach intellectually
satisfying.

~~~
kbutler
Is it survivorship bias? ML attempts that fail are cancelled and never heard
from again, those that succeed are publicized.

The same approach works with pig entrails: a bunch of people make predictions,
the ones that fail go away, the ones that happen to succeed a few times "must
work".

Or in stock market terms, "past performance is no guarantee of future
results."

~~~
canjobear
In a way you're exactly correct and that's exactly why machine learning works.
The models specify a huge range of possible input-output mappings and then do
a dumb search to find the ones that work; the mappings that don't succeed are
discarded. It turns out, surprisingly, this is the best approach we have come
up with.

> Or in stock market terms, "past performance is no guarantee of future
> results."

Past performance isn't a guarantee, but under mild conditions it is very
strong evidence that there will be future results.

------
abainbridge
True that. But you could replace "Machine Learning System" with "code base"
and "linear algebra" with "C++" and it would be an accurate description of
most corporate software development I've seen.

~~~
H4CK3RM4N
I think this is one of Munroe's general digs at corporate culture, and not
specifically aimed at machine learning, that's just the current buzzword.

~~~
TeMPOraL
It feels like a dig at how people do machine learning these days though. My
impression is that people just take TensorFlow and a random neural net, and
keep throwing gigabytes of data at it until they feel the results look like
they should...

~~~
TheOtherHobbes
Random Neural Nets should totally be a real thing.

I mean more than they already are.

~~~
mpweiher
At University, we did neural nets via genetic algorithms. Does that count?

~~~
majewsky
Bingo. :)

------
lngnmn
I like a different analogy. In old times the oracles and shamans danced in
masks wielding amulets of power and magical weapons. Modern day shamans dance
with datasets and clusters, but the main principle is the same. If my
performance produced a desired effect (correlation does not imply causation),
that is, obviously, due to my undoubted magic powers. If it fail, which is
usually the case, that is because the data was not big enough and there was
not enough money given to perform big-enough sacrifice to please the gods.

We actually might learn a lot from Tibetan tantric practitioners. It seems
that Wall street guys and economists did.

~~~
sgt101
My advice : if you see a pile of sticks growing in the car park, make a swift
exit.

------
thriftwy
As famous russo-ukrainian sentence goes, "is it a defeat or a victory?"

~~~
kozak
I don't know what is the Russian equivalent of this saying, but the Ukrainian
version that got very popular recent years has an interesting culturological
twist. The word that is used as an antonym of "victory" does not translate to
English as "defeat", but instead it literally means "betrayal". This is
because in Ukrainian history we almost never lost by being defeated, and
almost every time our defeats were results of betrayal. And same thing applies
to the ongoing war: we don't really fear a military defeat as much as a
political betrayal (either internal or external).

~~~
dagenleg
> in Ukrainian history we almost never lost by being defeated

Nice to see Ukrainians reinventing German Stab-in-the-back mythology from
interwar period.

~~~
kozak
It's much more complex than that.

~~~
dagenleg
After making such a statement it would be proper to elaborate on what you
meant there. Don't get me wrong, I am not yearning for a debate on Ukrainian
historiography here, I just believe that borderline chauvinistic statements
like that should be checked.

~~~
kozak
I get your point. It would be chauvinistic if I said that we are so strong
that we can't be taken by force (which is impossible), while actually I'm just
saying that we are too corrupt (so that every time a pivotal point comes,
someone gets bribed and betrays). And also, I'm speaking of the last 300
years, and not really the _entire_ history.

------
MarkMMullin
I'm waiting for the followon - I stirred it up, I got good results, and now I
can't seem to reproduce them again :-(

------
lottin
There is this famous paper "I Just Ran Two Million Regressions". Any half
decent statistician will tell you what's wrong with that type of approach.

~~~
joe_the_user
Could one describe machine learning as _" combine_ enough _bad statistical
tests that the cries of 'it seems to work' from managers drown out all the
'it's wrong' cries from the statisticians"_

------
itissid
So I am curious, like to a software engineer who has never learnt much of
stats how difficult has it been for them using these things in the field to
solve problems with, if they are any doing so? Like do they have a problem
because they don't have a grounding in stats or do these things pretty much
"work" assuming the models are coped to fit the problems.

------
rufusroflpunch
Not hot dog.

------
YeGoblynQueenne
Seems to be releated to this recent one:

[https://xkcd.com/1831/](https://xkcd.com/1831/)

Something tells me Rundall Munroe had a run-in with some over-eager data
scientists recently.

------
jakozaur
So far Deep Learning has made this Xkcd obsolete:
[https://xkcd.com/1425/](https://xkcd.com/1425/)

Check whether a photo is of a bird.

~~~
seren
Interesting, it shows how things can move quicker than expected, even when
judged by enlightened people.

Is there a way to know the date (1425) was released ?

~~~
spatulan
24th September 2014

~~~
bryanrasmussen
Programmers often have Scotty syndrome and pad their time estimates to look
like geniuses when they solve ahead of schedule.

~~~
afarrell
> to look like geniuses when they solve ahead of schedule

No, it is because estimating software tasks is difficult, the penalty for
underestimating is that people think you are dishonest/flakey, and there isn't
anywhere to get an education in how to do it well. The default advice given to
junior engineers is therefore: "take your intuition and triple it." I hate
that this is the state of the industry. My interactions around estimation over
the past 5 years since uni have literally made me feel nauseated and near
fainting on multiple occasions. I would love for Joel or Klamezius or Uncle
Bob or someone else to fix it and produce a good course on how to create
estimates.

~~~
UK-AL
There isn't a way. It's an issue that blights everyone in the industry.

Probably the best your going get is the book "Software Estimation:
Demystifying the Black Art "

Even applying those techniques you get it wrong.

Most experienced software companies have adopted agile, and accept reductions
in scope to meet deadlines as something that happens.

~~~
bragh
Agreed, agile seems the only way, but does indeed require experienced
managers. A lecturer once pointed out that business/normal people would always
expect some kind of point estimate, they are never satisfied with some kind of
distribution or interval. Personally, I would say that this is even more sad
than that: the point estimates are always taken at the extreme values, which
ever suits the person wanting the estimate more, never the average value.

Of course, all this leads to bad blood between techies and business side: how
long will it take? -> probably about 3 weeks, but this requires using a
library we haven't used before, so in the worst case even 2 months -> what? so
long? get it done in 4 days, this is required the next week -> no, that's not
really possible -> make it happen -> it happens and it either sucks when it's
delivered at all, so the deadline gets extended anyway to iron out all the
bugs or it causes lots of problems in the future.

~~~
seren
For very long projects, I have seen much delay because of feature creep.

"OK you have implemented it as requested, but finally the customer does not
like it, it needs to be slightly different. Can you do it quickly?"

Sometimes it is easy to adapt, sometimes next to impossible.

------
pasta
It's funny that this also seems to work for humans.

When the outcome is bad/not what humanity wants, we get/give a negative
response and hope the outcome next time will be better.

------
ganwar
The second part quite accurately describes most of the generalization
techniques. Especially when it comes to deep learning.

------
stared
For webcomics and ML, see this footnote:
[http://p.migdal.pl/2017/04/30/teaching-deep-
learning.html#fn...](http://p.migdal.pl/2017/04/30/teaching-deep-
learning.html#fn:webcomics)

"It made a few episodes of webcomics obsolete: xkcd: Tasks (totally, by Park
or Bird?), xkcd: Game AI) (partially, by AlphaGo), PHD Comics: If TV Science
was more like REAL Science (not exactly, but still it’s cool, by LapSRN)"

------
topologie
Do you think it's connected in any way to [https://www.wired.com/2013/02/big-
data-means-big-errors-peop...](https://www.wired.com/2013/02/big-data-means-
big-errors-people/) ???

If not, what is your interpretation of the XKCD cartoon?

------
jacquesm
This thread is hilarious.

ML is powering most or even all self driving car efforts underway, powers
online translation services, numerous vision projects and speech recognition
besides winning competitions meeting or exceeding human performance on the
same data.

I'm just as allergic to those that hype some technology as I am to those that
will snarkily discard something with arguments that have already been laid to
rest, in some cases multiple years ago.

Xkcd is fun, but isn't necessarily prescient nor does it have to be accurate,
when this cartoon was published the writing was already on the wall and that's
3 years ago. It's fine to be skeptical about new technology but before you
start criticizing it make sure that you have at least a rough idea of where
things stand lest you end up looking foolish.

Sure, ML is abused and if we're not careful we will see another AI winter
because of silly hype and ascribing near magical properties to ML. But at the
same time snark, condescension and a-priori dismissal of what is most likely
the biggest landslide in computing since the smartphone is - especially on a
site that deals with both hacking _and_ novelty - something that I would not
expect.

Compared to the HN love for the next JS framework or language fad this
attitude is surprising to say the least.

------
21
Can we load all XKCDs into a neural net (LSTM, or something), and train it to
"dream" new ones?

Like the neural nets which generated non-sense, but realistic looking C++
code.

------
vsbosvubo
Couldn't a DNN be trained to inspect other DNNs and generate human-readable
explanations of how they work ? In the fewest words, with maximum poetry

~~~
YeGoblynQueenne
Brilliant idea! Why not try it out yourself and see how it goes?

~~~
vsbosvubo
don't know anything about DNNs!

