Inceptionism is a bizarre phenomenon that
has recently popped up in the internet world.
Created by Google in 2014, it consists of
dream-like hallucinogenic images developed
by a computer vision program called DeepDream.
The program uses an advanced AI system known
as a convolutional neural network.
But that’s not exactly a term we hear very
often.
So what exactly is DeepDream and how does
artificial intelligence come into play?
When you hear the word “dream”, you’d
be inclined to think about those vivid experiences
we manifest while we’re asleep.
However DeepDream in this context, refers
to a program designed to categorize images.
It’s the way this machine learns.
A convolutional neural network is a specific
type of AI made up of computing systems based
on the biological neural networks found in
animal brains.
That is to say, they’re modeled to learn
in a way similar to how we learn.
The name “deep dream” comes from the concept
of deep learning.
It’s the process a neural network goes through
to actively analyze things.
And though it has very little to do with how
our own biological neurons work, we might
gain a better understanding by looking at
how we learn.
Two educational psychologists by the names
of Ference Marton and Roger Säljöl suggest
that we humans have two different approaches
to learning: a surface approach and a deep approach.
In humans, the concept of surface learning
is how we memorize parts of information that
we might be questioned about later.
In contrast, our process for deep learning
involves actively searching for the meaning
of information.
In regards to machines, deep learning is more
like a series of fine-tuning based on a set
of probabilities and conditions.
An algorithm.
And we see the practical uses of this sort
of programming, throughout the internet.
It’s how the internet detects content that
might be pornographic.
Or how a YouTube video might be demonetized
for using certain keywords or songs.
Another example: you might have noticed the
presence of an artificial intelligence and
how it “learns” on Facebook, where AI
automatically tags uploaded pictures with
the names of the people in them.
With regards to DeepDream, artificial neural
networks are programmed to analyze specific
patterns within images.
They learn to recognize and identify similar
patterns in other images.
And in cases where they’re being trained
to detect specific objects, they’re exposed
to millions of images containing the desired
object.
What DeepDream does is take this a step further,
by repeatedly analyzing the details in an
image over and over again in multiple layers,
drastically enhancing the patterns it detects
each time, until we get something like this:
So how does an AI make new visual connections
to specific objects from basic patterns?
Well, you might’ve heard of certain songs
that when played backwards or in slow motion,
have random noises or strange messages from
obscure voices.
Perhaps you thought you saw Jesus in your
morning toast.
Or perhaps you were looking at a forest one
day and thought you saw a face lurking in
the patterns of light and shadow.
Pareidolia is the psychological phenomenon
where we look at an image or we hear a sound,
and our minds interpret a familiar pattern
when there isn’t one.
Skeptics often attribute pareidolia for why
some of us might see ghosts or even shadow people.
It’s derived from the Greek words para,
meaning “beside or instead of”
(in thiscase meaning something wrong), and eidolon,
which is the noun for image, form and shape.
You could say that DeepDream uses algorithmic
pareidolia to create these bizarre, dream-like images.
It’s designed to detect faces and other
patterns in images, and automatically classifies
those images within its algorithm.
And as you can see, this makes for some rather
unsettling sights to behold- especially since
the AI often seems to detect target patterns
within photos that aren’t really there.
This all seems rather complicated though.
Why is it that these artificial visualizers
tend to create objects in images
that weren’t originally there?
According to a Google research blog posted
by its own software engineers, when an image
is given to the neural network:
“We ask the network, “whatever you see
there, I want more of it!”
This creates a feedback loop: if a cloud looks
a little bit like a bird, the network will
make it look more like a bird.
This in turn will make the network recognize
the bird even more strongly on the next pass
and so forth, until a highly detailed bird
appears, seemingly out of nowhere.”
This “over-interpretation” is similar
to how we as children might watch the clouds
and interpret random shapes from them.
Since DeepDream was trained mostly on animal
images, it naturally translates shapes into animals.
It's also why you might see it change a blank
horizon into a city skyline, probably made of eyes.
The common presence of eyes in all animals
might explain why the DeepDream seems to be
so fixated on seeing them in almost any pattern.
Neural networks seem to grant us a new channel
for both creating and understanding abstract art.
When I look at the vibrant, alien-like compositions
and landscapes of what these artificial minds
create- I’m reminded of the mysterious unknown
prevalent in Lovecraftian lore.
The study into neural networks seems to have
only just begun, but it does appear to grant
us insight into what our AI has learned about
our world- as well as what that could mean
for AI advancing in the future.
And from this, we may even be able to draw
parallels into understanding how our own organic
minds visualize the universe around us.
I would like to explore the powerful psychological
effects these images might have on us, but
that’s a video for another day.
If you’d like to learn more about this topic,
I’ve left some neat sources in the description
below.
And if you’d like to see what odd images
the DeepDream engine sees in your photos,
you can visit the DeepDream generator which
I’ve left a link to in the description below.
So what do you think of DeepDream?
Let me know in the comments below.
And as always, thanks for watching.
Wanna see more? Click here to checkout the official Darkology playlist!
