
Machine-generated husband chatter - visakanv
https://medium.com/@sharanvkaur/visakanv-rnn-machine-generated-husband-chatter-c2b431bf0ac2
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Artlav
Ah, this brings back memories. I tried something like that about 12 years ago,
with a homebrewn half-hardcoded, half-neural thing and a large set of Jules
Verne books.

The giddy excitement of being on the verge of unlocking the power of language
slowly turned into confusion and then decayed into disappointment as i slowly
realized that the task was maybe a bucket of orders of magnitude harder than i
thought. I think it produced some coherent phrases every now and then, but
mostly it was a random words generator.

...I miss these amplified feelings of cluelessly diving into an impossible
project...

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visakanv
> The giddy excitement of being on the verge of unlocking the power of
> language slowly turned into confusion and then decayed into disappointment
> as i slowly realized that the task was maybe a bucket of orders of magnitude
> harder than i thought

This was very wonderfully articulated! Do you have a blog or something?

~~~
Artlav
Well, Orbital Designs [http://orbides.org](http://orbides.org) certainly
qualifies as "something", but i'm not sure how much of a "blog" it really is
since all of the content is indexed. :)

~~~
visakanv
I like it! Very nice.

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donquichotte
Strangely enough, I find the output of Markov Chain - type text generators
much more readable and coherent than the output of those RNNs. A Marov Chain
text generator is also easy to implement and understand.

~~~
visakanv
Interesting. Why do you think that is? (More readable, that is)

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aninhumer
If I were going to guess (not the parent), it's because human parsing is quite
localised, and Markov models tend to optimise well for localised coherence.

Their failure mode is long sentences which don't go anywhere (or mean
anything), but that's less grating than a blatant parse failure, especially if
you're skim reading.

~~~
visakanv
That actually makes a lot of sense, great point.

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diyseguy
I came up with similar results by just tracking word probabilities and then
regenerating text based on those probabilities.

Feeding it Edgar Allen Poe produced the best results:

twelve vibrations of purple air. vortex of its absolute hurricane. monck
flying footsteps of the picture all the police are concerned. silence on a
year in an identity. materiality escaping the city was their white spot of
printing apparatus all things only one supposes it were its mere duration that
it in baltimore monument to its luxuriant nature asserts her person whom i not
hesitate about five hundred paces to have not the principia of idiotic thought
of curvature or metamorphosis these friends were in an earthquake and the then
i felt my chair so rigidly of interest could not summon courage

Here's the TED version I just generated using the input.txt from one of the
linked articles: [https://github.com/samim23/TED-
RNN/](https://github.com/samim23/TED-RNN/)

educationists made around in our modern medicine. gq did make perfect mankind
which the blue pill every week had infinite instant messaging that sucks when
your food day that previous background is about being foreign interventions.
psychopharmaceutical arsenal. attached to tail but a distribution units in our
traditional estimates that men engineers who negotiate my whole province of
long awkward to call it came back go deep conversations you grow fast when
there been around the long lever systems.

entertaining anyway, and occasionally amazing

~~~
kristjansson
Holy shit, that's awesome. Do you have the code used to generate this
available somewhere?

~~~
diyseguy
Thanks for noticing! :) I will tidy it up and put it out there soon. I'll let
you know after I've done that. :)

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hypfer
I've also did this a few months ago with a dump of all my telegram messages. I
thought it could be a nice chatbot. I was wrong. :-(

~~~
visakanv
Haha yeah it's a little disappointing (yet interesting) how it comes close but
misses the mark. I wonder how far away we are to having machine-generated
output that's tough to distinguish from the input you put into it.

~~~
simonh
The Obama one is much better, but the thing is none of these are even remotely
close. They sort of seem close on the surface if you simply don't engage any
critical parts of your brain, or try to extract any meaning, but if you do the
semblance of meaning that's inherent in the words melts away into nothing.

To generate output that contained meaning that you could actually engage with,
you'd have to first generate the meaning and then turn that into text. That's
a fundamentally different process to what is going on here. This is also why
'Turing Test' competitions involving chat bots are never going to give us
artificial intelligence.

This sort of text generation is like a magician's trick where he appears to
saw the woman in half and put her together again. I don't care how good the
magician is at the trick, he's not going to be any help at all to a surgeon
that's actually cutting someone in half and then stitching them back together
again.

~~~
visakanv
> This sort of text generation is like a magician's trick where he appears to
> saw the woman in half and put her together again.

That's a really good analogy!

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greydius
Does that include starting sentences with "so"? Is anyone else annoyed by
this?

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thiagocsf
Trained a neural net to write like me: poorly.

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visakanv
Hahaha! Yeah, maybe that's part of what makes the output distinctive. Do you
have any good recommendations for a person looking to improve their shoddy
writing?

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xaedes
I really want to try this on small refactoring git commits to reproduce a
simple ml-based code optimization engine.

~~~
visakanv
You should totally do it! Would love to see what happens.

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jorgescx
Reminds me somehow of SCIgen
([https://pdos.csail.mit.edu/archive/scigen/](https://pdos.csail.mit.edu/archive/scigen/)),
a funny 'scientific paper generator'.

~~~
wolfgang42
In a similar vein, [http://git-man-page-generator.lokaltog.net/](http://git-
man-page-generator.lokaltog.net/) creates plausible-sounding man pages for git
internals.

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janwillemb
The title is from the perspective of the submitter (being the husband in the
original title), it should be "visakanv-RNN — machine-generated husband
chatter". Which is a lot less catchy.

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ada1981
Wasn't expecting the image at the end, wtf?

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coldpie
I believe all technical articles are now required to include at least one
Impact font meme. I'll see if I can dig up the RFC.

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Asparagirl
She should call this the HusBot.

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sharanvkaur
this is clearly way better than visakanv-rnn. thanks!

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Asparagirl
:-)

