
Inceptionism:  Going Deeper into Neural Networks - neurologic
http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html
======
davedx
Worth reading the comments too.

One from Vincent Vanhoucke: "This is the most fun we've had in the office in a
while. We've even made some of those 'Inceptionistic' art pieces into giant
posters. Beyond the eye candy, there is actually something deeply interesting
in this line of work: neural networks have a bad reputation for being strange
black boxes that that are opaque to inspection. I have never understood those
charges: any other model (GMM, SVM, Random Forests) of any sufficient
complexity for a real task is completely opaque for very fundamental reasons:
their non-linear structure makes it hard to project back the function they
represent into their input space and make sense of it. Not so with backprop,
as this blog post shows eloquently: you can query the model and ask what it
believes it is seeing or 'wants' to see simply by following gradients. This
'guided hallucination' technique is very powerful and the gorgeous
visualizations it generates are very evocative of what's really going on in
the network.﻿"

~~~
rndn
Perhaps the argument should be steelmanned in that we should generally avoid
using algorithms which are so complex that they aren't glass boxes. I doubt
the idea to "simply follow gradients" can prove neural networks to be glass
boxes because the output of that is still too complex. And we are clearly onto
something here. If we can generate artificially hallucinated pictures today,
it is not unreasonable to assume that computers will be able to hallucinate
entire action sequences (including motor programs and all kinds of modalities)
in a decade or two. Combining such a hallucination technique with
reinforcement learning might be a key to general intelligence. I think it is
highly unethical that there is almost no democratic control over what is being
developed at Google, Facebook et al. in secrecy. The most recent XKCD comic is
quite relevant: [http://xkcd.com/1539/](http://xkcd.com/1539/)

~~~
sangnoir
> I think it is highly unethical that there is almost no democratic control
> over what is being developed at Google, Facebook et al. in secrecy. The most
> recent XKCD comic is quite relevant:
> [http://xkcd.com/1539/](http://xkcd.com/1539/)

I consider myself to be very Left of center, but, I can't imagine what form of
'democratic control' you think is necessary over the research that Google and
Facebook does.

I do not fault Google or Facebook for planning on time-scales longer than most
governments. Governments ought to be doing this level of long-term planning,
but are not (at least publicly)

~~~
rndn
I'm just questioning whether an autopilot with a profit maximization heuristic
is the best tool to guide technological progress. With democratic control I
don't necessarily mean our current democratic systems but any kind of
decentralization of decision making by voting. Yeah, I know that's vague, but
given what appears be at stake it seems unreasonable not to consider
alternatives.

~~~
lt
I'm reading Rationality: From AI to Zombies, and it goes through exactly this
argument. Here's the original post:

[http://lesswrong.com/lw/jb/applause_lights/](http://lesswrong.com/lw/jb/applause_lights/)

~~~
rndn
Fine, my suggestion to solve this problem democratically was an applause
light. It was an unfinished thought and a call for action. I agree that this
didn't convey any new information, I just wanted to express my distrust
towards these kinds of appeasement statements from people who are working on
these technologies. Being able to peak at different layers of a CNN doesn't
recover NNs from the fact that they are in many regards opaque to us (and will
possibly always be due to their complexity). Statements of the sort "I have
never understood those charges" makes it sound like they are pretty much
ignorant of the potential risks associated with not knowing exactly what your
program does (they can perhaps be with regards to current technology, but I
could imagine that more advanced systems can potentially arrive sooner than
overall anticipated).

------
philipn
The reason they look so 'fractal-like' (e.g. trippy!) is because they actually
_are_ fractals!

In the same way a normal fractal is a recursive application of some drawing
function, this is a recursive application of different generation or
"recognition -> generation" drawing functions built on top of the CNN.

So I believe that, given a random noise image, these networks don't generate
the crazy trippy fractal patterns directly. Instead, that happens by feeding
the generated image back to the network over and over again (with e.g. zooming
in between).

Think of it a bit like a Rorschach test. But instead of ink blots, we'd use
random noise and an artificial neural network. And instead of switching to the
next Rorschach card after someone thinks they see a pattern, you continuously
move the ink blot around until it looks more and more like the image the
person thinks they see.

But because we're dealing with ink, and we're just randomly scattering it
around, you'd start to see more and more of your original guess, or other
recognized patterns, throughout the different parts of the scattered ink.
Repeat this over and over again and you have these amazing fractals!

~~~
DanBC
> The reason they look so 'fractal-like' (e.g. trippy!) is because they
> actually are fractals!

Do they exhibit self-similarity at different zoom levels?

~~~
fixermark
I believe they do (in the sense that if you take one of these images, zoom it
in, and run it through the algorithm again, it'll take the micro-features of
the animals it hallucinated and hallucinate more animals on top of them).

------
meemoo
Tweak image urls for bigger images:

Ibis:
[http://3.bp.blogspot.com/-4Uj3hPFupok/VYIT6s_c9OI/AAAAAAAAAl...](http://3.bp.blogspot.com/-4Uj3hPFupok/VYIT6s_c9OI/AAAAAAAAAlc/_yGdbbsmGiw/s6400/ibis.png)
Seurat:
[http://4.bp.blogspot.com/-PK_bEYY91cw/VYIVBYw63uI/AAAAAAAAAl...](http://4.bp.blogspot.com/-PK_bEYY91cw/VYIVBYw63uI/AAAAAAAAAlo/iUsA4leua10/s6400/seurat-
layout.png) Clouds: [http://4.bp.blogspot.com/-FPDgxlc-
WPU/VYIV1bK50HI/AAAAAAAAAl...](http://4.bp.blogspot.com/-FPDgxlc-
WPU/VYIV1bK50HI/AAAAAAAAAlw/YIwOPjoulcs/s6400/skyarrow.png) Buildings:
[http://1.bp.blogspot.com/-XZ0i0zXOhQk/VYIXdyIL9kI/AAAAAAAAAm...](http://1.bp.blogspot.com/-XZ0i0zXOhQk/VYIXdyIL9kI/AAAAAAAAAmQ/UbA6j41w28o/s6400/building-
dreams.png)

I'd love to experiment with this and video. I predict a nerdy music video
soon, and a pop video appropriation soon after.

~~~
cing
As linked in the last figure caption, there's a Google Photos gallery with
high-resolution downloadable versions:
[https://goo.gl/photos/fFcivHZ2CDhqCkZdA](https://goo.gl/photos/fFcivHZ2CDhqCkZdA)

~~~
anon012012
There's a video, among them

[https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliat...](https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliattX4OUCj_8EP65_cTVnBmS1jnYgsGQAieQUc1VQWdgQ/photo/AF1QipOlM1yfMIV0guS4bV9OHIvPmdZcCngCUqpMiS9U?key=aVBxWjhwSzg2RjJWLWRuVFBBZEN1d205bUdEMnhB)

------
moyix
This appears to be the source of the mysterious image that showed up on
Reddit's /r/machinelearning the other day too:

[https://www.reddit.com/r/MachineLearning/comments/3a1ebc/ima...](https://www.reddit.com/r/MachineLearning/comments/3a1ebc/image_generated_by_a_convolutional_network/)

~~~
userbinator
It reminds me of that parrot image that was said to crash human brains, only
even more intense. I certainly experienced some effect, as while looking at it
and trying to figure out what exactly it was, I felt my head heating up ---
probably increased blood flow.

~~~
teraflop
In case anyone hasn't read the story this is referring to: "BLIT" by David
Langford.
[http://www.infinityplus.co.uk/stories/blit.htm](http://www.infinityplus.co.uk/stories/blit.htm)

~~~
Houshalter
There's also a few sequels:

[http://ansible.uk/writing/c-b-faq.html](http://ansible.uk/writing/c-b-
faq.html)

[http://www.lightspeedmagazine.com/fiction/different-kinds-
of...](http://www.lightspeedmagazine.com/fiction/different-kinds-of-darkness/)

And _What Happened at Cambridge IV_ , which I can't find online.

~~~
JonnieCache
Here's _What Happened at Cambridge IV:_

[https://books.google.co.uk/books?id=5d9hHvD-T7gC&lpg=PA264&o...](https://books.google.co.uk/books?id=5d9hHvD-T7gC&lpg=PA264&ots=4iPFt60kwj&dq=What%20Happened%20at%20Cambridge%20IV&pg=PA264#v=onepage&q=What%20Happened%20at%20Cambridge%20IV&f=false)

This last story makes the ML images even more disturbing!

Highly recommend these stories. They'd make a great black mirror episode.

~~~
david-given
Google Books previews appears to block pages randomly per user --- I don't see
the entire text, unfortunately.

------
murbard2
Two remarks

1) Captain obvious says: the "tripiness" of these images is hardly
coincidental, these networks are inspired by the visual cortex.

2) They had to put a prior on the low level pixels to get some sort of image
out. This is because the system is trained as a discriminative classifier, and
it never needed to learn this structure, since it was always present in the
training set. This also means that the algorithm is going to be ignoring all
sort of structures which are relevant to generation, but not relevant for
discrimination, like the precise count and positioning of body parts for
instance.

This makes for some cool nightmarish animals, but fully generative training
could yield even more impressive results.

------
ghoul2
This is brilliant! I did something similar when I was trying to learn about
neural networks a long long time ago. The results were fascinating.

I was writing a neural network trainer - to recognize simple 2D images. This
was on a 300MHz desktop PC(!) so the network had to be pretty small. Which
implied that the input images were just compositions of simple geometric
shapes - a circle within a rectangle, two circles intersecting, etc.

When I tried "recalling" the learnt image after every few X epochs of
training, I noticed the neural network was "inventing" more complex curves to
better fit the image. Initially, only random dots would show up. Then it would
have invented straight lines and would try to compose the target image out of
one and more straight lines.

What was absolute fun to watch was, at some point, it would stop trying to
compose a circle with multiple lines and just invent the circle. And then
proceed to deform the circle as needed.

During different runs, I could even see how it got stuck into various local
minima. To compose a rectangle, mostly the net would create four lines - but
having the lines terminate was obviously difficult. As an alternative,
sometimes the net would instead try a circle, which it would gradually
elongate, straighten out the circumference, slowly to look more and more like
a rectangle.

I was only an undergrad then, and was mostly doing this for fun - I do believe
I should have written it up then. I do not even have the code anymore.

But good to know googlers do the same kinda goofy stuff :-)

------
pault
I would love to see what would come out of a network trained to recognize
pornographic images using this technique. :)

~~~
userbinator
I think doing the same with videos would be far more interesting... and
probably creepier.

------
gradys
Does anyone have a good sense of what exactly they mean here:

>Instead of exactly prescribing which feature we want the network to amplify,
we can also let the network make that decision. In this case we simply feed
the network an arbitrary image or photo and let the network analyze the
picture. We then pick a layer and ask the network to enhance whatever it
detected. Each layer of the network deals with features at a different level
of abstraction, so the complexity of features we generate depends on which
layer we choose to enhance. For example, lower layers tend to produce strokes
or simple ornament-like patterns, because those layers are sensitive to basic
features such as edges and their orientations.

Specifically, what does "we then pick a layer and ask the network to enhance
whatever it detected" mean?

I understand that different layers deal with features at different levels of
abstraction and how that corresponds with the different kinds of
hallucinations shown, but how does it actually work? You choose the output of
one layer, but what does it mean to ask the network to enhance it?

~~~
murbard2
The detection layer will detect very faint random signals. For example, if you
have a unit that's supposed to detect dogs, it might be very faintly activated
if by random chance there is a doggish quality to some part of the image. What
they do is pick up that faint, random, signal and amplify it.

They say: oh you think that cloud is a tiny bit dog-like? Ok, well then find
me a small modification to the image that would make it a little more dog
like, then a little more, and so on.

Think of it as semantic contrast enhancement

~~~
gradys
So in concrete terms, does this mean that we show the network an image, choose
one layer's output vector, and then back-propagate gradients to the image such
that the direction of that vector stays the same, but the magnitude increases?

~~~
murbard2
That is my understanding of the blog post, yes.

That plus a prior on the input pixels to keep it image-like.

------
simonster
The fractal nature of many of the "hallucinated" images is kind of
fascinating. The parallels to psychedelic drug-induced hallucinations are
striking.

~~~
meemoo
I'm reminded of Alex Grey's visionary art
[https://caudallure.files.wordpress.com/2011/09/1217891347094...](https://caudallure.files.wordpress.com/2011/09/1217891347094..).

~~~
raldi
Link's broken.

~~~
teraflop
Fixed:
[https://caudallure.files.wordpress.com/2011/09/1217891347094...](https://caudallure.files.wordpress.com/2011/09/1217891347094.jpg)

~~~
plq
OT, but this is originally a "3D" image.

It can be found in the cover art of "10000 Days", an album from American metal
band Tool. The original box comes with two magnifying lenses like this:

[http://s21.photobucket.com/user/Stonergrunge/media/Mis%20cos...](http://s21.photobucket.com/user/Stonergrunge/media/Mis%20cosas/Colecciones%20varias/TOOL-10000_Days-04.jpg.html)

This (and others in the cover) look stunning through these lenses.

~~~
nitrogen
You can see the 3D effect of this subset of the image here:
[https://imgur.com/04yBHN4](https://imgur.com/04yBHN4)

------
intjk
I'll repeat what I posted on facebook because I thought it was clever: "Yes,
but only if we tell them to dream about electric sheep."

So, tell the machine to think about bananas, and it will conjure up a mental
image of bananas. Tell it to imagine a fish-dog and it'll do its best. What
happens if/when we have enough storage to supply it a 24/7 video feed (aka
eyes), give a robot some navigational logic (or strap it to someone's head),
and give it the ability to ask questions, say, below some confidence interval
(and us the ability to supply it answers)? What would this represent? What
would come out on the other side? A fraction of a human being? Or perhaps just
an artificial representation of "the human experience".

...what if we fed it books?

~~~
andor
Neural networks are a relatively simple mathematical model. They don't
actually "think" or have a conscience. Neural networks are also regularly fed
books, in order to model some properties of natural language.

Here's a good introduction: [http://colah.github.io/posts/2014-07-NLP-RNNs-
Representation...](http://colah.github.io/posts/2014-07-NLP-RNNs-
Representations/)

~~~
gradys
Neurons are also relatively simple, at least in comparison to the mind. I
don't think the simplicity or complexity of the underlying model has much
bearing on the higher-level properties of the _network_.

Now, this isn't to say that the kinds of neural networks we build today are
conscious, but I don't think that's because they're based on a simple
mathematical model; I think that's because they don't have the network-level
properties that conscious humans do, for example, a self-representation.

------
fizixer
Some comments seem to be appreciating (or getting disgusted by) the aesthetics
but I think the "inceptionism" part should not be ignored:

We're essentially peeking inside a very rudimentary form of consciousness: a
consciousness that is very fragile, very dependent, very underdeveloped, and
full of "genetic errors". Once you have a functioning deep learning neural
network, you have the assembly language of consciousness. Then you start
playing with it (as this paper did), you create a hello world program, you
solve the factorial function recursively, and so on. Somewhere in that
universe of possible programs, is hidden a program (or a set of programs) that
will be able to perform the thinking process a lot more accurately.

~~~
romaniv
_We 're essentially peeking inside a very rudimentary form of consciousness_

Blatant sensationalism. There is absolutely nothing here that would suggest
consciousness. If you have a mask for matching images, you can reverse that
mask and imprint it as an image. What we're seeing here is a more complicated
version of the same process. Heck, look more closely. Some of those "building"
images have obvious chunks of pedestrians embedded, probably because the
algorithm was trained on tourist photos.

Is it interesting? Yes, from algorithmic point of view. Cool as hell. However,
this has nothing to do with consciousness.

If anything, some of those images are just a more elaborate version of a
kaleidoscope. It's not like they run a network and got a drawing. They were
looking for a particular result, did post processing, did pre-processing and
tweaked the intermediate steps (by running them multiple times until the image
looked interesting). Finally, we as viewers do our share of pattern matching,
similar to how we see patterns in Rorschach inkblots. And there are captions
that frame what we see and "guide" us to recognizing the right objects.

~~~
fizixer
> Blatant sensationalism. There is absolutely nothing here that would suggest
> consciousness.

Putting biological consciousness on a pedestal might be blatant sensationalism
itself. By consciousness, I specifically mean the behavioral capacity of
general intelligence, nothing more. If by consciousness you mean subjective
character of experience then yes, there are serious issues with resolving the
mind-body problem. But functionally speaking, our brains exist in a physical
universe, are massively parallel, and do stochastic computations. Deep
learning systems share all three of these traits except that the scale is
about 3-5 order of magnitudes smaller (things like incorporating time,
biological impulses are missing but if someone claims those features are going
to be the dealbreaker then maybe we can have a discussion). And the scale
difference is shrinking at lightning speed.

I'm not claiming DNN's are the end-all-be-all of an upcoming general
electronic intelligence. But they seem to be doing mind-blowing stuff every
few weeks, and it seems we've stumbled upon a radically new aspect of
computation.

~~~
romaniv
You did not address a single specific points I made.

 _I specifically mean the behavioral capacity of general intelligence_

Any image-matching algorithm can be used to generate images. A simple color-
matcher can be used to create very impressive things if plugged into a genetic
algorithm, but no one claims it's conscious or intelligent. None of what you
wrote here points out some fundamental differences between this and other
image-generating techniques used before. You're simply trivializing what it
means to be conscious or intelligent to the point that word is no longer
useful.

I will consider an AI to be "generic" when it is able to apply training from
one domain to an entirely different domain without any manual "mapping" from
humans. For example, being able to decently play checkers after learning chess
and being given a description of checker's rules. Applying training in image
domain to image domain with tons of manual tweaking might be interesting and
useful, but it's hardly qualifies as "mind-blowing".

~~~
bytefactory
> will consider an AI to be "generic" when it is able to apply training from
> one domain to an entirely different domain without any manual "mapping" from
> humans. For example, being able to decently play checkers after learning
> chess and being given a description of checker's rules.

Want to butt in to this discussion, and post this link here for anybody who
hasn't seen/heard about this amazing development from a while ago:
[http://robohub.org/artificial-general-intelligence-that-
play...](http://robohub.org/artificial-general-intelligence-that-plays-atari-
video-games-how-did-deepmind-do-it/)

------
davesque
This is one of the most astounding things I've ever seen. Some of these images
look positively like art. And not just art, but _good_ art.

~~~
calebm
I felt the same. I think the main aspect about these images that makes me like
them is how everything feels connected, which, is what the AI is trying to
find: connections. Honestly, can anyone tell me where I could order large
prints of some of these?

~~~
krebby
Agreed. These are just amazing. Someone linked above to the source images on
Google Photos, but even those aren't especially high-res. Would be awesome if
Google released the originals.

------
anigbrowl
These images are remarkably similar to chemically-enhanced mammalian neural
processing in both form and content. I feel comfortable saying that this is
the Real Deal and Google has made a scientifically and historically
significant discovery here. I'm also getting an intense burst of nostalgia.

------
joeyspn
The level of resemblance with a psychotropics' trip is simply fascinating.
It's definitely _really close_ to how our brain reacts when is flooded with
dopamine + serotonin.

I wonder if the engineers at Google can make the same experiment with audio...
It'll be funny to listen the results.

~~~
tripzilch
Might be interesting, although I've always liked the visuals* of psychedelica
a lot more than the audio effects (which in my experience, mostly tends to
make sounds be perceived really "loud" and "close", rather than
"trippy"\--unless that's what you associate with "trippy" audio, of course).
Dunno if my experience is typical, obviously.

* also the particular mind-altering effects, which are hard to describe

------
guelo
I'm starting to come around to sama's way of thinking on AI. This stuff is
going to be scary powerful in 5-10 years. And it will continue to get more
powerful at an exponential rate.

~~~
johnconner
You are not alone in your fears. Others have been ringing the alarm for some
time. Nick Bostrom's Superintelligence is a good reference.

------
gojomo
Facial-recognition neural nets can also generate creepy spectral faces. For
example:

[https://www.youtube.com/watch?v=XNZIN7Jh3Sg](https://www.youtube.com/watch?v=XNZIN7Jh3Sg)

[https://www.youtube.com/watch?v=ogBPFG6qGLM](https://www.youtube.com/watch?v=ogBPFG6qGLM)

(Or if you want to put them full-screen on infinite loop in a darkened room:
[http://www.infinitelooper.com/?v=XNZIN7Jh3Sg&p=n](http://www.infinitelooper.com/?v=XNZIN7Jh3Sg&p=n)
[http://www.infinitelooper.com/?v=ogBPFG6qGLM&p=n](http://www.infinitelooper.com/?v=ogBPFG6qGLM&p=n)
)

The code for the 1st is available in a Gist linked from its comments; the
creator of the 2nd has a few other videos animating grid 'fantasies' of digit-
recognition neural-nets.

------
IanCal
The one generated after looking at completely random noise on the bottom row,
second from the right:

[http://googleresearch.blogspot.co.uk/2015/06/inceptionism-
go...](http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-
into-neural.html)

Reminds me very heavily of The Starry Night
[https://www.google.com/culturalinstitute/asset-viewer/the-
st...](https://www.google.com/culturalinstitute/asset-viewer/the-starry-
night/bgEuwDxel93-Pg?utm_source=google&utm_medium=kp&hl=en-GB&projectId=art-
project)

Lovely imagery.

I never had much luck with generative networks. I did some work putting RBMs
on a GPU partly because I'd seen Hinton talk showing starting with a low level
description and feeding it forwards, but always ended up with highly unstable
networks myself.

~~~
Lewton
[https://lh3.googleusercontent.com/4jaIlDI1xXGOhxNejib833qA5y...](https://lh3.googleusercontent.com/4jaIlDI1xXGOhxNejib833qA5yVMAvQLaKmmuaQ0PGY=w1200-h800-no)

full resolution image

~~~
IanCal
That's great, thank you.

------
henryl
I'll be the first to say it. It looks like an acid/shroom trip.

~~~
jarboot
Maybe there's something to do with how our brains interpret information
differently when under the influence of psychoactive drugs.

I've been looking at Aldous Huxley's "Doors of Perception" and other
psychonautic works recently and he hypothesizes that these sorts of drugs
filter out the usual signals from the CNS that shut out the parts of
perception that are not important for you to receive for survival.

It might be some great leap of armchair psychology, but I think we're due for
another psychedelic revival, especially considering the new advances in
synthetic psychedelics, legalization of more harmless recreational drugs, new
tests in medical research using MDMA/LSD/Psilocybin, and the cultural shift
away from the 'War on drugs'.

------
frankosaurus
Really cool. You could generate all kinds of interesting art with this.

I can't help but think of people who report seeing faces in their toast.
Humans are biased towards seeing faces in randomness. A neural network trained
on millions of puppy pictures will see dogs in clouds.

~~~
fixermark
That's essentially precisely what's happening here. You can see in the
different pictures where different sets of training data were used---
buildings, faces, animals.

Give the machine millions of reference images to work from and then tell it to
find those images in noise, and it will succeed (because it literally can't
"imagine" anything else for the noise to be).

------
djfm
Now I'm thinking about all those google cars, quietly resting in dark garages,
dreaming about streets.

------
nl
I'd really like to see what an Electric Sheep looks like. Maybe if they did a
collaboration with the Android team?

~~~
pgeorgi
[http://electricsheep.org/](http://electricsheep.org/)?

------
jakozaur
Request for startup: Neural Network on demand artist.

E.g. SaaS that takes your images and use neural network transformations. Can
you make a portrait of my that I look like king.

------
tzs
Understanding what is going on in a neural network (or any other kind of
machine learning mechanism) when it makes a decision can be important in real
world applications.

For example, suppose you are a bank and you have used built a neural network
to decide if credit applications should be approved. The lending laws in the
US require that if you reject someone you tell them why.

Your neural network just gives a yes/no. It doesn't give a reason. What do you
tell the applicant?

I have an idea how to deal with that, but I have no idea if it would satisfy
the law. My approach is to run their application through multiple times,
tweaking various items, until you get one that would be approved. You can then
tell them it was that item that sunk them. For instance, suppose that if you
raise their income by $5k, you get approval. You can tell them they were
rejected for having income that is too low.

~~~
nitrogen
I have an idea for a company related to this concept, but for hiring and job
training.

------
waffl
While I think this is beautiful, conceptually, I really am a bit terrified of
the potential of this in reverse (the neural network for
processing/understanding an image). With Google releasing their 'Photos' app,
this network is about to get a direct pipeline for machine learning imagery to
accelerate everything – my main fear would be the potential for this
technology to be employed by weaponized drones able to scan a scene (with,
eventually, incredibly high resolution cameras and microphones that far
surpass human capability) and identify every single object/person in realtime
(also at a rate that humans are incapable of).

Of course, there is great utility to be had as well, it just scares me to
think about what could be done with this technology, in a mature form, if used
for violent purposes.

~~~
visarga
This will happen for sure. Such super-perceptive computers will oversee our
every movement.

Computers can already understand our emotions in writing, voice and from the
expression on our faces, they can also estimate pose and understand your
movements. They can label thousands of kinds of objects. And they're just
starting.

They can also build neural nets 10x smaller by compressing a larger neural net
while maintaining most of accuracy. That means once a problem such as vision
or speech has been solved with a huge net, it can be transferred in a smaller,
more efficient net.

~~~
aoeuasdf1
> They can also build neural nets 10x smaller by compressing a larger neural
> net while maintaining most of accuracy. That means once a problem such as
> vision or speech has been solved with a huge net, it can be transferred in a
> smaller, more efficient net.

This is known as "dark knowledge". Slides from Geoff Hinton:
[http://www.ttic.edu/dl/dark14.pdf](http://www.ttic.edu/dl/dark14.pdf)

------
dnr
Am I the only one who found those images somewhat disturbing? I wonder if
they're triggering something similar to
[http://www.reddit.com/r/trypophobia](http://www.reddit.com/r/trypophobia)

~~~
Houshalter
This unpublished one is incredibly creepy.
[https://i.imgur.com/6ocuQsZ.jpg](https://i.imgur.com/6ocuQsZ.jpg)

~~~
krebby
This one too:
[https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliat...](https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliattX4OUCj_8EP65_cTVnBmS1jnYgsGQAieQUc1VQWdgQ/photo/AF1QipPVTpDfh2LrPA9ui0CH1Xof_RByCyaa9ce_U60h?key=aVBxWjhwSzg2RjJWLWRuVFBBZEN1d205bUdEMnhB)

------
tomlock
These paintings remind me of Louis Wain's work when he was mentally ill.

Which makes me wonder, are these sophisticated neural nets mentally ill, and
what would a course of therapy for them be like?

~~~
anigbrowl
This sort of 'illness' should be supported, rather than treated.

------
hliyan
Am I the only person who is not entirely happy about the overuse of the pop-
culture term 'inception' for everything that is remotely nested, recursive or
strange-loop-like?

    
    
       In this paper, we will focus on an efficient deep neural network 
       architecture for computer vision, codenamed Inception, which derives 
       its name from the Network in network paper by Lin et al [12]
       in conjunction with the famous “we need to go deeper” internet meme [1]

~~~
davesque
I haven't personally noticed any buzz-wordiness about that term lately. Maybe
I'm just not looking at the same stuff as you.

~~~
fixermark
In other words, you haven't trained your personal neural net with enough
Inception-meme instances to be finding it everywhere in the noise? ;)

------
agumonkey
Do computers dream about fractal antilopes
?[http://i.imgur.com/jZtbz7f.png](http://i.imgur.com/jZtbz7f.png)

------
sitkack
This was recently posted to HN,
[http://tjake.github.io/blog/2013/02/18/resurgence-in-
artific...](http://tjake.github.io/blog/2013/02/18/resurgence-in-artificial-
intelligence/)

Which mentions running the NN in reverse, quote

    
    
        By far the most interesting thing I’ve learned about Deep Belief 
        Networks is their generative properties. Meaning you can look 
        inside the ‘mind’ of a DBN and see what it’s imagining. Since a 
        deep belief networks are two-way like restricted boltzmann 
        machines you can make hidden inputs generate valid visual 
        inputs. Continuing with our handwritten digit example you can 
        start with the label input say a ‘3’ label and activate it then 
        go reverse through the DBN and out the other end will pop out a 
        picture of a ‘3’ based on the features of the inner layers. This 
        is equivalent to our ability to visualize things using words, go 
        ahead imagine a ‘3’, now rotate it.

~~~
cafebeen
It's worth pointing out that a naive bayes or k-nearest-neighbor classifier
can similarly generate examples of valid inputs.

------
anigbrowl
I understand the theory behind neural networks quite well, but am not so clear
on how you feed them with images, eg how do you build a network that can
process megapixel images of random aspect ratios or audio files of predictable
length?

I', trying to get a sense of how much effort would be involved to replicate
these results if Google isn't inclined to share its internal tools, to do a
neural network version of Fractint as it were, which one could train oneself.
I have no clue which of the 30-40 deep learning libraries I found would be
best to start with, or whether my basic instinct (to develop a node-based tool
in ab image/video compositing package) is completely harebrained.

Essentially I'm more interested in experimenting with tools to do this sort of
thing by trying out different connections and coefficients than in writing the
underlying code. Any suggestions?

~~~
xtacy
You could try Torch libraries. There are a few examples on how to (almost)
replicate some of Google's neural network models on Imagenet.

Check
[https://github.com/torch/torch7/wiki/Cheatsheet#demos](https://github.com/torch/torch7/wiki/Cheatsheet#demos).

~~~
tripzilch
Does this work well enough on a modern desktop PC without having access to
Google's computing resources?

~~~
anigbrowl
No idea, but I'm sure their enormous infrastructure helps a lot. Maybe it
could be done with some sort of BOINC-type platform.

------
huskyr
Very cool. I wonder if there's some example code on Github to generate images
like this?

------
kriro
Pretty interesting and beautiful. If I was still at my old job I'd love to try
and see how helpful this is in teaching NN. My first instinct is that it would
be really valuable because they tend to be blackboxy/hard to conceptualize.

------
bearzoo
They are doing nothing but starting with random noise, and then learning a
representation of an image that will maximize the probability in the output
layer (by suggesting to the network that this noise should have actually been
recognized as a banana or what have you) and back propagating changes into the
input layer. Essentially, this has been happening since 2003 in the natural
language processing world where we learn 'distributed representations' of
words by starting with random representations of words, and learning them by
context by back propagating changes into the input layer. Very cool though.

------
Animats
This is fascinating. And important. We need better ways to see what neural
nets are doing. At least for visual processing, we now have some.

This might be usable on music. Train a net to recognize a type of music, then
run it backwards to see what comes out.

Run on the neural nets that do face popout (face/non face, not face
recognition), some generic face should emerge. Run on nets for text
recognition, letter forms should emerge. Run on financial data vs results ...
optimal business strategies?

But calling it "inceptionism" is silly. (Could be worse, as with "polyfill",
though.)

~~~
userbinator
_Run on the neural nets that do face popout (face /non face, not face
recognition), some generic face should emerge_

[https://en.wikipedia.org/wiki/Eigenface](https://en.wikipedia.org/wiki/Eigenface)

------
darkFunction
I don't understand what kind of NN they used on the painting and the photo of
the antelopes(?). What was it pre-trained to recognise?

EDIT: in clarification, to pick out abstract features of an image, it must
obviously be trained on many images. I'm curious about how it picked out
seemingly unique characteristics of the painting, and what images it was
trained on to get there.

------
m-i-l
This story has been picked up by The Guardian:
[http://www.theguardian.com/technology/2015/jun/18/google-
ima...](http://www.theguardian.com/technology/2015/jun/18/google-image-
recognition-neural-network-androids-dream-electric-sheep)

------
mkj
Has anyone seen an explanation for the why the images end up with that colour
palette?

~~~
TheLoneWolfling
Probably an artifact of the color space used.

------
mraison
Really nice. I'd be interested in seeing a more in-depth scientific
description of how these images were actually generated. Are there any other
publications related to this work?

~~~
kriro
There's four papers linked in the article. The last three (see below) were
pretty good, haven't read the first.

[http://arxiv.org/pdf/1412.0035v1.pdf](http://arxiv.org/pdf/1412.0035v1.pdf)

[http://arxiv.org/pdf/1506.02753.pdf](http://arxiv.org/pdf/1506.02753.pdf)

[http://arxiv.org/pdf/1312.6034v2.pdf](http://arxiv.org/pdf/1312.6034v2.pdf)

------
imh
It would be interesting to know what happens if instead of tweaking it to
better match a banana, they tweaked it to better match a banana and NOT match
everything else.

------
antirez
Are the coefficients of the neurons inside the layers to "trigger" just
multiplied by some constant? Not cited in the original article apparently.

------
patcon
Wow. Funny how those images look like dreamscapes when the trained neural nets
process random noise...

Kinda make me contemplate more own conscious experience :)

------
spot
a really early version of this:
[http://draves.org/fuse/](http://draves.org/fuse/) published as open source in
the early 90s. not NN but does have the same image matching/searching.

~~~
drcode
Fascinating link, but that was arguably different: In the case of the Google
links, the neural network was built for other uses, and the "image fusion" is
only a side effect... It is a sort of "proof" that some really interesting
things are happening behind the scenes.

In the older approaches, the image fusion was the primary intent of the
system. Still very cool, but much less impressive IMHO.

------
stared
I am curious what do they see if feed with a screenshot of their own code.

------
jastr
If anyone wants to send this to their non-dev friends, here's the write-up I
sent to mine!

[https://medium.com/@stripenight/seeing-how-computers-
might-t...](https://medium.com/@stripenight/seeing-how-computers-might-
think-e8ea3d1de081)

\---

tldr: To figure out how computers "think", Google asked one of its artificial
intelligence algorithms to look at clouds and draw the things it saw!

There's this complex Artificial Intelligence algorithm called a neural network
(
[https://en.wikipedia.org/wiki/Artificial_neural_network](https://en.wikipedia.org/wiki/Artificial_neural_network)
). It's essentially code which tries to simulate the neurons in a brain.

Over the last few years, there have been some really cool results, like using
neural networks to read people's handwriting, or to figure what objects are in
a picture.

To start your neural network, you give it a bunch of pictures of dogs, and
tell it that those pictures contain dogs. Then you give it pictures of
airplanes, and say those are airplanes, etc. Like a child learning for the
first time, the neural network updates its neurons to recognize what makes up
a dog or an airplane.

Afterwords, you can give it a picture and ask if the pic contains a dog or an
airplane.

The problem is that WE DON'T NOW HOW IT KNOWS! It could be using the shape of
a dog, or the color, or the distance between it's legs. We don't know! We just
can't see what the neurons are doing. Like a brain, we don't quite know how it
recognize things.

Google had a big neural network to figure out what's in an image, and they
wanted to know what it did. So, they gave the neural net a picture, but
stopped the neural net at different points, before it could finish deciding.
When, they stopped it, they asked it to "enhance" what is just recognized. Eg.
if it just saw the outline of a dog, the net would return the picture with the
outline a bit thicker. Or, if it saw the colors similar to a banana, it would
return the picture with those colors looking more like a banana's colors.

This seems like a simple idea, but it's actually really complex, and really
insightful! Amazing images here -
[https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliat...](https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliattX4OUCj_8EP65_cTVnBmS1jnYgsGQAieQUc1VQWdgQ?key=aVBxWjhwSzg2RjJWLWRuVFBBZEN1d205bUdEMnhB)

Original article - [http://googleresearch.blogspot.com/2015/06/inceptionism-
goin...](http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-
into-neural.html)

