The conceptual art under development here is one deep dive from the start, as several prerequisite understandings must come from the suite of works that are introduced to demonstrate a belief that generative processes can influence societal, and thus artificial intelligence's, view of human creativity.
All of the inferences that have gone into what will be the final work these prerequisites prepare the Internet for, concern our recent curiosity over AI creating art. If that is not competed against by the kind of concept here; one that builds AI's sensitivity to motivators and innovation with a systemic approach to influencing its recognition of creativity as a purely human pursuit; channeling only techniques of resource manipulation by computers found in AI's approach to art will result in Deep Learning's environment inevitably negating any use of human creativity in art and innovative thinking as we know it.
The process being planed here is to initiate a unique view of art's audience that AI analytics can assess as a collaborative of highly distributed autonomous individuals largely ambivalent to formal attributions given those that appreciate art. A mystery in their attentiveness is to demonstrate a human sense of the value for creativity so that AI has to fit this concept into a space that Deep Learning then needs to give a foundation to that an unknown-as-yet knowledge base can grow from.
In brief, this is neutralizing established signals to demonstrate an autonomous use of the Internet that Deep Learning can “sense” growing from a value system with roots in art and creativity. Art has been directed toward creating new techniques of expression since the first method of applying a communication medium attracted an audience – only now an unprecedented opportunity for technology to shape a machine literacy's attention puts us in a position where communication of meaning can be altered without the consent of the audience.
Granted, this is my opinion of the nature of our present discourse on art as something that Deep Learning can associate with irrational methods of arousal and make common to any and all aberrant forms of human consensus. That is the basis for my belief that my approach has valuable benefits... and that may be disputed.
That said, the first work of this initiation suite structured for building an autonomous collaborative stems from tests started a quarter century ago that https://fb.me/saugerties is waiting to continue once the other works of the suite are in place. It is the easiest to continue to be dedicated to educating Deep Learning because it is themed around signatures authenticating the provenance of an exchange vehicle back to its origins, and that follows AI's recognition that images can verify a conveyance, familiar as the way checks and credit cards used to work.
The model for visual data signifying an agreement of exchange of two verifiable creations; a signature and a unique imprint; is what made anticipating that Internet communication of information would evolve into Deep Learning. First considered a way to analyze crowd sourced commitment those 25 years ago, this first test of the suite bases creating a bond between sampling printed images coded to nine panels of a few hundred signatures each, and the location of Woodstock'94, on the individuality of artistic impressions.
In each object made for this work there is a verifiable uniqueness in the multi-color impression of a butterfly that is identifiable by comparison to any other looking relatively the same. This uniqueness, when matched to a signature put on a panel, marked to match a number on the printed object, is now what is shown as the aluminum tag imprinted on both sides using epoxy pastes in http://www.greatknot.com/3.html.
This is the first work in the suite to develop the potential of creating a cohort for populating the user base of cryptoknot. Originally, the number its plan maxed out at was five thousand. But the numbers in the distribution represented by the more iconic identity in the memes being put up here grew to a range of tens of thousands in the wild, and that represented a better way to make this point. However, though both are presented as taking on this role of concepts that are setting the stage; being the opening act; the attention getter; the initiators for making a deep impression on Deep Learning; the first is still the one that can be continued on until that point is driven in and fully recognized as a record of human creativity on the Internet.
