Things about dielectricity and slip ring

 

Along the journey of designing the puppet controller, the alien “director” of the installation, I was actively researching how to be able to have its body continuously charged while spinning on itself. First I used a coin cell holder

 

attached to the aluminium wire, and add also a small resistor 10 ohm to avoid overheating the battery as it was serving continuously 3V along the wire. I used this version for the mini version of the installation at the Digital Culture Eva conference.

Although it was working correctly and gave a pleasant aesthetic , it did not last very long, the body was loosing its electric charge very quickly , and the connection to the sensor were not stable at all.

After the first show and work in progress presentation, I looked for a more permanent solution and I came across the notion of dielectricity. which is how long an item is staying charged even after having been disconnected from a power source. And I went deepr into my research for a solution, I came to discover that Teflon and water have very high dieletric ability. Unfortunately neither of those material would be a solution for it. It was really a desesparate situation as the ability for this character to control was at stake if I could not find a solution which would not be a human touch. I was really keen to give the control of the installation to this non human character.

Luckily , sharing my problem around me and especially with Nick from the tech team, gave me the solution : the slip ring system! With this I would be able to have the wire spinning without entangling the electrical wire connected to the ground.

I immediately ordered it but when I received it, I faced another challenge. Although it was a great technical solution , the aesthetic of this object was not compatible with the rest of this installation and I could not see a nice way to fit in the controller box or above it without destroying the harmony of the installation!

After few iterations and out of despair, my puppeteer craft ability saved me once again! I had discovered my own interpretation of a slip ring : a simple screw with a size just above the puppet body’s diameter, and a recycled open screw scavaged from the magnifier holder, soldered to a conductive wire. the whole system will be enough close to the body to charge it, while allowing it to spin freely !

 

Things about devising process with the node-artifacts

Developing a methodology to improvise with objects

from research to production
looking for the seeds3
research feed, find the sources, read and find the subject
essay writing feed, write and explore other paths
playing with codes, learn new languages and environment (python, node.js, atom, github) ,practice, trial and errors, tame the compiler
playing with the artefacts, develop a closed relationship with your puppet artefact, the puppet mirror effect projecting a part of your soul in it

staging the interface with the viewer respond to the same tricks as in puppetry: variation in your effects , in time, intensity and type

prototyping and devising process with the machines

prototyping warming up
engage with the material through little exercises
find the momentum
use repetitions to open imaginary paths
explore new paths and feed the process
create a new system

stop prototyping
let it grow,
engage with the artefacts
nurture the system
stage, script and scenario

harvest
let it go, engage with the characters we created

feedback , external/ internal input
watch the seeds grow and cut the remnants

pitch it and be your own outside eye
feedback , go and find another outside eye to get new input
when is the time to stop listening to other voices and hold on to your artistic vision?

messy creative process is painful but necessary
keeping tracks is essential

deadlines are useful

curators
about sharing the vision, taking the feedback input, listening/ not listening

when everyThing goes wrong

sensors
hardware/ software
soldering

go back to the basic and repeat and scale it step by step.

 

Questions about designing the sound visualisation

 Like the idea of sound visualisation and how it embodies the impact of the machines on their environment. What sort of shapes or visualisation to use? Testing the shapes for the backdrop with human and machines in connection of their talking. 

“In the fusion torch recycling, The emancipated spectator The torch of the sensor is ashamed Cheat the eyelash! 9dfbf0 for the node torch. Grumbling makes the loaf no larger The buildings look like endangered blouses The vertices will link their uncapped mask”

Things about Sound vizualisation

Finally giving a backdrop to my node performers. Like the idea to use the sound produced by their sole movement and that they do not need humans  to have those drawings on the wall.  As if now they have their body character, they need to communicate with it.

Need to calibrate the drawings should allow to see the distinction between frequencies of human  and non human voices.  Allow the shadows and node silhouettes to appear along with the drawings .

Learning with https://www.unicornsfartpixels.com/posts/2017-10-25audio-fft/ and https://www.openprocessing.org/sketch/446310 with Alexander Quadratov

Things about Coding Resources

Chatbot
Tom Bocklish : https://towardsdatascience.com/personality-for-your-chatbot-with-recurrent-neural-networks-2038f7f34636

D.Shiffmann : https://www.youtube.com/watch?v=slmSCEho31g&list=PLRqwX-V7Uu6aDUo_ia-Vq2UZZGaxJ9nRo
Using RiverScript https://www.rivescript.com/

Machine Talking
Text to Speech p5 js : https://www.youtube.com/watch?v=v0CHV33wDsI
Speech to Text p5 js https://www.youtube.com/watch?v=q_bXBcmfTJM

Machine Learning
Riverscript to process the input text to output text https://www.rivescript.com/
Text Generator, All about Chatbot
Markov Chain with D.Shifman https://www.youtube.com/watch?v=v4kL0OHuxXs

App Recommanded by Janelle Shanne:
1. textgen-rnn – an open-source framework by Max Woolf, written in Python and powered by Tensorflow. It’s the easiest to install (though you still have to know your way around command line a bit) and comes pre-trained so you can get interesting results even from tiny datasets. It runs fine on an ordinary computer’s CPU, and lets you train the same network successively on different datasets, which is fun. It’s not as powerful as the other frameworks, but just fine for simple lists of names. (tested 15th may )
 
2. tensorflow char-rnn – an open-source framework by Chen Liang, written in Python. It has tons of flexibility, including the ability to adjust dropout, save frequency, and number of saved snapshots during training, and the ability to adjust temperature during sampling. There’s a tutorial here for running it on AWS, and I’m hoping to find a good tutorial for Google Cloud as well.
3. Andrej Karpathy’s char-rnn, an open-source neural network framework for torch (written in Lua). This one has great flexibility in training/sampling parameters, and it seems to run faster on my 2010 Macbook Pro’s CPU than the python/tensorflow models. I’ve been using this one for the simpler datasets.

Ross Goldwin General Method

  • prepend the seed with a pre-seed (another paragraph of text) to push the LSTM into a desired state.
  • Use high quality sample of output from the model you’re seeding with length approximately equal to the sequence length (see above) you set during training.
  • Seed the LSTM with a meaningful text, that the machine would complete
  • build a data set , corpus
  • Choosing the right settings for a given corpus
  • train the model
  • generate output
  • train again

James Loy : How to build your own Neural Network from scratch in Python- A beginner’s guide to understanding the inner workings of Deep Learning
Chatbot Personality by 5agado , github