# User:Alexander Roidl/algorithmicthesis

Set of experiments on generating the thesis topic by a set of rules / with a game.

## Method: Print | What: Collection | Action: transform

There is a huge collection of images on http://image-net.org/ It is an attempt to classify and categories images into a huge database

I picked a random set of images:

Clouds & sky | 1399 pictures

A visible mass of water or ice particles suspended at a considerable altitude

Interestingly the set also features some cloudy explosions.

As a first attempt to transform I overprinted explosion images with those of clouds.

But I wondered if transformation can also happen on a more cognitive level. By putting this images into a certain context. So I decided to run it through an algorithm, an image detection software. As I result I figured out that the software would predict quite similar values.

This is interesting as in my personal (human) opinion these images feature radical different objects.

So what would the machine see if I labeled the images myself.

It didn’t change its opinion a lot, but still the image detection wouldn’t output the same result

PREDICTED CONCEPT  PROBABILITY  nature 0.989  landscape 0.988  sky 0.987  desert 0.962  travel 0.952  outdoors 0.942  no person 0.933  summer 0.923  volcano 0.922  sun 0.917  sand 0.916  tree 0.913  soil 0.909  cloud 0.907  fair weather 0.902  hill 0.893  hot 0.891  mountain 0.884  heat 0.875  eruption 0.863

PREDICTED CONCEPT  PROBABILITY  nature 0.993  landscape 0.991  sky 0.990  travel 0.954  desert 0.951  summer 0.948  outdoors 0.943  sand 0.940  soil 0.939  cloud 0.935  volcano 0.935  tree 0.926  sun 0.921  fair weather 0.916  mountain 0.915  hill 0.911  rock 0.910  no person 0.899  eruption 0.885  lava 0.882

### Thoughts and questions on print, collection, transform

What do I see when I look at something?

Why is an explosion so different from a cloud and why are both connected for a machine. What do they have in common (outdoors, no person, dramatic)

Also these labels like no person, remind me little bit of fortune tellers that tell you things that most probably fit to every person, but are still relatable to your personal situation. That there is no person in this image is something I wouldn’t have thought of when looking at this image.

Thinking about: who classifies those images? How does the algorithm classify the images?

> So this took somewhat far from printing :D

## Method: Program | What: Data | Action: generate

So how do you generate Data?

Program that generates random data:

```from random import randint

f= open("file.txt","w+")

for i in range(100000):
f.write("{}".format(randint(0,255)))

f.close()
```

This generates random numbers between 0 and 255 and appends it to a file. To make it readable I extend .txt, so it is readable by any text-editor. You can generate GB of data with this scripts and crash your computer – so quite powerful lines of code.

But what does this mean? To visualize it I mapped all these values to pixels, so this random data looks like the following:

Outputting it as a gif:

Compress:

Crop & resize:

## Method: Write | What: Algorithm & Image | Action: question

The algorithmic image & unalgorithmic imagination

Think of a flower, any flower.

Think of a bird, a bird flying in the sky.

What you might have in your head is an image. But still – is it an image? Only you can see it, but as soon as you try to see it, it changes. It is not a single image, it is a series. Is it moving? What materiality does it have? If it is not an image, is it a thought or a chain of thoughts?

Imagine a face, but not of a person you know. A new person that might exist, but probably doesn’t.

When I think of a bird, I most probably think of a very different one than you do. But still we would be able to agree upon an image showing a bird. Why are we trying to use an algorithm for everything? What would be un-algorithmic? Does imagination follow a certain pattern?

(to be continued)

Images generated based on the text with: http://t2i.cvalenzuelab.com/