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Nicolas Schöffer /
9 SepNicolas Schöffer (1912-1992) was a Hungarian-born French artist. He can be considered as the father of cybernetic art. His career touched on painting, kinetic sculpture, architecture, urbanism, film, TV, and music. Indeed he collaborated on music with Pierre Henry. All of the artistic actions of Schöffer were done in the pursuit of a dynamism in art.
This interest in artistic dynamism was originally initiated by the Cubo-Futurists and then intensified and solidified by the Russian Constructivism artists, such as Naum Gabo, Anton Pevsner, Moholy-Nagy and Ludwig Hirschfeld-Mack. All these artists were concerned with opening up the static three-dimensional sculptural form to a fourth dimension of time and motion. And this was the intention of Schöffer as well.
Schöffer however, coming well after, benefited from cybernetic theories (theories of feedback systems (interactivity) primarily based on the ideas of Norbert Wiener (1894–1964)) in that they suggested to him artistic processes in terms of the organization of the system manifesting it (e.g., the circular causality of feedback-loops). For Schöffer, this enabled cybernetics to elucidate complex artistic relationships from within the work itself.
His CYSP 1 (1956) is considered the first cybernetic sculpture in art history in that it made use of electronic computations as developed by the Philips Company. The sculpture is set on a base mounted on four rollers, which contains the mechanism and the electronic brain. The plates are operated by small motors located under their axis. Photo-electric cells and a microphone built into the sculpture catch all the variations in the fields of color, light intensity and sound intensity. All these changes occasion reactions on the part of the sculpture.
Consequently his kinetic sculptural compositions were able to parallel the work of Warren McCulloch and his adaptation of cybernetics in formulating a creative epistemology concerned with the self-communication within an observer’s psyche and between the psyche and the surrounding environment. This is the primary usefulness of cybernetics in studying the supposed subject/object polarity in terms of artistic experience.
Here is an excerpt of his work:
Galerie 47 – 20th century furniture and decorative arts
Sculpture: Nicolas Schöffer 1968
Music:Pierre Henry “spatiodynamisme I ” 1963
Nicolas Schöffer – Cyspe – 1959
Sydney Opera House / Living Mural
21 JulGlobal animation project coordinated by Universal Everything.
Animation Sequences:
01, Block: Universal Everything –
02, Burst: Patch d. Keyes – http://patchdkeyes.co.uk/
03, Fill: Drew Tyndell – http://drewtyndell.com/
04, Chase: Tymote – http://tymote.jp/
05, Multiply: Nicolas Ménard – http://nicolasmenard.com/
06, Climb: Parallel Teeth – http://parallelteeth.com/
07, Rise: KClogg – http://kclogg.tumblr.com/
08, Twist: Matt Scharenboich – http://mattscharenbroich.com/
09, Wind: Váscolo – http://vascolo.com.ar/
10, Swarm: Universal Everything
11, Calm: Zutto – http://zuttoworld.com/
12, Float: Cindy Suen – http://cindysuen.tumblr.com/
13, Power: Masanobu Hiraoka – http://vimeo.com/user6065152
14, Slide: Ori Toor – http://oritoor.com/
15, Spin: Ori Toor
16, Wave: Váscolo
17, Construct: DXMIQ – http://dxmiq.com/
18, Growth: DXMIQ
19, Flow: Ruff Mercy – http://ruffmercy.com/
20, Scatter: Caleb Wood – http://vimeo.com/calebwood
21, Shrink: Bee Grandinetti – http://behance.net/grandinetti
22, Noise: Takcom – http://takafumitsuchiya.com/
23, Attack: MixCode – http://mixcode.tv
24, Boom: nöbl – http://nobl.tv
25, Bounce: Matt Abbiss – http://abbiss.co/
26, Ricochet: Matt Frodsham – http://mattfrodsham.com/
27, Splash: Guille Comin – http://guillermocomin.com/
28, Bang: Universal Everything
29, Embrace: Matt Frodsham
30, Melt: Caleb Wood
31, Stripe: Matt Abbiss
Journey through the layers of the mind // Memo Akten
8 JulA visualisation of what’s happening inside the mind of an artificial neural network.
In non-technical speak:
An artificial neural network can be thought of as analogous to a brain (immensely, immensely, immensely simplified. nothing like a brain really). It consists of layers of neurons and connections between neurons. Information is stored in this network as ‘weights’ (strengths) of connections between neurons. Low layers (i.e. closer to the input, e.g. ‘eyes’) store (and recognise) low level abstract features (corners, edges, orientations etc.) and higher layers store (and recognise) higher level features. This is analogous to how information is stored in the mammalian cerebral cortex (e.g. our brain).
Here a neural network has been ‘trained’ on millions of images – i.e. the images have been fed into the network, and the network has ‘learnt’ about them (establishes weights / strengths for each neuron).
Then when the network is fed a new unknown image (e.g. me), it tries to make sense of (i.e. recognise) this new image in context of what it already knows, i.e. what it’s already been trained on.
This can be thought of as asking the network “Based on what you’ve seen / what you know, what do you think this is?”, and is analogous to you recognising objects in clouds or ink / rorschach tests etc.
The effect is further exaggerated by encouraging the algorithm to generate an image of what it ‘thinks’ it is seeing, and feeding that image back into the input. Then it’s asked to reevaluate, creating a positive feedback loop, reinforcing the biased misinterpretation.
This is like asking you to draw what you think you see in the clouds, and then asking you to look at your drawing and draw what you think you are seeing in your drawing etc,
That last sentence was actually not fully accurate. It would be accurate, if instead of asking you to draw what you think you saw in the clouds, we scanned your brain, looked at a particular group of neurons, reconstructed an image based on the firing patterns of those neurons, based on the in-between representational states in your brain, and gave *that* image to you to look at. Then you would try to make sense of (i.e. recognise) *that* image, and the whole process will be repeated.
We aren’t actually asking the system what it thinks the image is, we’re extracting the image from somewhere inside the network. From any one of the layers. Since different layers store different levels of abstraction and detail, picking different layers to generate the ‘internal picture’ hi-lights different features.
All based on the google research by Alexander Mordvintsev, Software Engineer, Christopher Olah, Software Engineering Intern and Mike Tyka, Software Engineer
Hattie Stewart – Oxymoron (Damien Hirst Projections)
7 JulAndy Baker animated Hattie’s illustrations.
Cerco De Chapa Con Hojas Secas (2010)
4 Jul
A fine Data-Mosh-Trip, ‘Metal Fence with Dry Leaves’ from Spanish video artist Mateo Amaral. Translates: “Jungle wind, ocean waves extending as much as they can reach the inner senses and much more too. A network is stirred by sounds of leaves, the network separates things. DMT circulating in the brain.”
