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:
