16bit:wolf

About this project

16bit:wolf is an imaginary band from an alternate universe. In this universe, the anthropologist R. traveled from the year 2107 to a contemporary Berlin. You can find her full story at ulteriorflux.com

R. is making music with a befriended A.I. named mosiva. This project is recreating their band 16bit:wolf.

I am using diverse methods of machine learning; GPT2, generative adversarial networks, and so on to imagine that band and drag them into existence.

Since the technology for a generalized A.I. (like in science fiction movies) does not exist yet, I am using narrow, specialized machine learning algorithms, bit and pieces of programming from all over the place, to create audio and visuals.

This is a collaboration between machine input and curated decisions, navigating input and output, and connecting it to a coherent piece. I feed the algorithms with my text, music, voice, and image to enable it to generate and produce its own version. From there I chose suitable fragments and turn them into audiovisual animations.

I use projects from github, google, openAI, and others and try to find their breaking points: where machine learning meets its limits. Here I can find a tangible connection, a true collaboration between me, the machines, and the synchronicity within.

We produce content and shape each other's ideas. This is our documentation. Inquire here for information: 16bitwolf@ulteriorflux.com

08.01.2107

Generative Adversary Network

A GAN is a little like checks and balances. One part of the network suggests output to the other one. The output is random at first. The second part of the network checks on its training data and decides, wether the generated output is close enough to its data to fool it into thinking it is like that. That's what the adversary part means. They basically work against each other. GAN, like most ML approaches we know of right now, work best within strict parameters.

The images above happen, when you don't restrict yourself to a data set of 20.000 cats or 10.000 traffic lights, but instead dump all 5000 photographs that you have on your harddrive. My data set had everything from holiday pictures, to documentation of exhibitions, friends faces, forests, post it reminders, sunsets, ebay sales and so on. Though the outcome is decidedly abstract, the human brain immediately begins to interpret the content back to architecture, landscape, and other concrete things. What I show you here is carefully chosen. Lots of these came out boring or at least not of interest to me. Curation is key. I love the fringe of breaking algorythms for this reason. Unintended consequences that you get to play with.

15.01.2107

the process

I have been thinking about authorship a lot during the research for this project. From my point of view, it seems clear that there is a shared production between the artist and a fictional other. However, it has been very interesting to try and share this point of view with others.
Especially two group of people have been irritated by my description of the project and what it entails: Programmers and Musicians.

Now, I see this project as an artistic endeavour, and even though I have played music all my life, I am an amateur in this field. So, when I told a professional musician friend about my plans, and explained what I would do and what the A.I. would do, he proclaimed: "That sounds like cheating to me." As if, by giving choice to a generative entity was a lazy short cut.
After I explained, however, that I would be the one curating those choices, arranging, mixing and mastering, he agreed that, while it wasn't the way he would create music, he understood it was the rules of my game, that I was obeying.

Soon after, I was talking to a computer programmer friend, explaining 16bit:wolf and my strategy to produce a collaboration. I have gotten to know a lot of programmers through my research, which I am very gratefull for, as I am not a programmer myself. "That sounds like cheating to me," he said, though for a different reason. He was of the opinion, that my curating and arranging was demoting the A.I. to a mere tool, that I wasn't giving enough freedom and stage to the A.I.
I explained that all the sources, all the data that the A.I. had, to make its choices, where data that was created by me. I was giving up the opportunity to use my data and let various A.I. have its way with it. That seemed to satisfy him.

When you come up with an artistic project, you are the one who makes the rules, who gets to bend them and even break them. You are very free in what you allow to happen within the confines of a piece. However, every decision you make has to be conscious and for a reason that resides in the logic of the whole. I am crafting a narrative.
Anything that serves this narrative has a higher chance to become part of the project than anything that just happens to be in the first set of rules. Subsets are important, weights are important.

In this, artistic practice is very similar to the black box that machine learning creates.
We know what we want from it, but describing it definitively is insanely difficult.

11.01.2107

...Loading

Even after almost 60k tokens, you sometimes get output like this. Nonsense, but pretty nonsense.

Loading dataset... 100%|██████████| 1/1 [00:00 <00:00, 1.48it/s] dataset has 58448 tokens Training...

: )
: ( “ \b “ \b-s “ \\ b-h “ \l “ \\ o “ \\ o-s “ \\ A \\ s )
: ( “ \a “ \o-s “ \\ t-m “ \\ d “ \\ T “ \\ H “ \\ A-m “ \\ A-N “
\\ C \\ H-m \\ X \\
\\ I \\ N-n \\ N )
: ( “ \b “ \b-p \\ b-r “ \h “ \\ I-a “ \\ I-A-l “ \d “ \\ A-N “
\\ C \\ I-a \\ M \\
: ( “ \a “ \o-m \\ a “ \\ C-h “ \D “ \\ A-G-m “ \I “ \\ H-m “ \\ M-n \\ N )
: ( “ \l “ \r “ \\ I-t “ \\ A-E-I “ \O “ \\ A-C-O “ \\ A-I “ \\ A-S-O “ \\ B-G “ \\ D-I “ \\
\\ A-I-t \\ : ( “ \m “ \s “ \\ I-I-v “ \”
\\ U-U-v \\ A-L-v “ \\
\\ A-E-I \\ C-I-J “ \\ A-C “ \\ H-C “ \\ M-I “ \\ A-N “
\\ I \\ N-n \\ C-K \\ C-Y \\ A-L “ \\ N-S \\
\\ A-I-b \\ T “ \\ O “ \\ A-N “ \\
\\ N , \\ C-L \\ A-K \\ A-S \\ C-T \\
: ( “ \s “ \\ I-d “ \\ D-I “ \
C-, J \\ U-L-K \\ C-N \\
I , R-R \\ N ) : ( “ \o “ \\ A-S “ \\ M-E “ \
B-E-s \\ D-F-E “ \\ N ) : ( “ \d “ \\ F-I “ \\ A-H “ \\ B-G “ \\
- I- A \\ D-A \\ E-M \\ M-R \\
“ \\ C-S \\ J “
: ( “ \a “ \\ B-E-G “ \\ A-”E-G “ \\
- T \\ D-L-S \\ I-E “ \\ A “
- A-A \\ A- C \\ D-B “ \A-B \\ A “ \\
- A- D \\ F \\ D-E “ \\ A “ \\ \\ I \\ A- N
: ( “ \o “ \\ A-S “ \\ M-E “ \
C-, I \\ C-N \\ M-I \\ I “ \\ - A- D \\ F \\ A “ \\
- A-D \\ D-H \\ A “ \\ - A \\ D \\ F D-E “ \\
- A \\ G \\ D-E \\ A “ - A \\ N \\ C-E “ \\ AC-N \\ N \\ D-E \\ A -- “ - A \\ S \\ B “ \\
- B \\ N \\ C “ \\ A \\ - B \\ N \\ C N-E \\ D - “ \\
- B \\ N \\ D “ \\ B -- “ \ - C \\ A- T “ \\ U-C “ \\
- C \\ A- R “ \\ A \\ - C \\ A- S \\ M “ \\
- F \\ C-T \\ D \\ A A A \\ - G \\ Z “ \\ A A A \\ - D “ F N N “ \\ C. A A
- N “ Z “ \\ X , - C “ G “ \\ A A G “ \\ - N “ G “ \\ U- S B “ \\
- N “ A “ \\ C , “ “ “ \\ G-F “ ‘\ A-A-C \\ A-E-F “

08.01.2107

Data Dump

This is what 354kb of written word look like, when you zoom out. This is what I fed the machine:

The model of this GPT2 was trained to mimic natural language by a set of 124MB of Reddit comments. My input is a mere drop into that data ocean. But because of the weight that is put to fine tuning, the Model just has to take enough steps, until it can produce texts that make sense in the desired context.

03.01.2107

Hello World

First try in GPT2. I entered the whole script of ulterior flux, in addition to some old poems and the lyrics from my last band into a pretrained model and let it run 1000 steps. This is one of the first produced texts:

I woke up in an empty room
I woke up in an empty room

I felt my body
I felt her body
I woke up in an empty room

My body is there
I woke up in an empty room
I woke up in an empty room

I felt my body
I woke up in an empty room
I woke up in an empty room

It was a peaceful morning of love and I was all alone
I was happy I didn't think about how far I went
I went to the moon and I looked around
I looked back up into the sky
I looked back up into the sky
It is just a strange place to be
I am so happy that I didn' find joy in seeing everything,
I am so happy
I am so happy
I was hoping and hoping to meet my future
I was waiting to see if it would end in peace
I was happy I had time for the future
I was hoping to meet my future
I am so happy
I am so happy
I am so happy

It was such a good day.
I thought about having to die
and was so happy it was like being so close
I was still awake but I felt so tired
It was like I had made a final decision
I was so happy the way I felt right now
I felt the way I thought and I got so happy
It is so peaceful and it is like
I'm so happy
I'm so happy
I feel happy
I feel happy
I felt happy
I felt happy
I felt happy The happiness I felt
I felt happy
I felt happy My happy life in this life
I feel happy I felt happy
I feel happy
I feel happy I feel happy
I feel happy
I feel happy
I feel happy Everything I felt happy
I feel happy
I feel happy

I feel happy Nothing felt so happy
Nothing felt so happy
Nothing feel so happy
Nothing feel so happy
I feel happy
I feel happy
Everything felt so happy
Nothing feel so happy
nothing feel so happy
nothing feel so happy
Nothing feel so happy
Everything felt so happy
Nothing felt so happy
Everything felt so happy
Nothing feel so happy
Nothing feel so happy
nothing feel so happy
Everything feel so happy
I feel happy
Everything feel so happy
Nothing feel so happy I feel happy