23/09 – 30/09

Feedback from Proposal:

  • Reference back the origins.
  • Reproduction of reproduction. (author and ownership from reproduction).
  • Art in the age of mechanical reproduction.
  • Bringing back the fantastical within objects.
  • Concepts of ownership and how this has different value within physical & digital spaces.
  • Value from material is lost within digital space.
  • well articulated
  • rich area: playing on this digital forms + manufacture of them + process chain
  • Start! Get going
  • One point: abstract and summary -> bring some of this process into it
  • Can you reference certain methods/stages in the history of production?
  • The work of art in the age of mechanical reproduction – Walter Benjamin (MUST read)
  • Is it about the author or the owner?
  • what are you shifting?
  • Playfulness? In crafts and noble material or un-noble? (wood vs cast)
  • Thingyverse?
  • Open-source – You get bits of code so who owns it?
  • Is the value the same with different materials?
  • Authorship, ownership, originality, value

This website allows unrestricted access to levels from many of the most popular nintendo games of the past few decades. The way textures, levels and environments can be explored and distorted in new ways allows for many new insights into the digital environments. It also allows me to question the idea of ownership. These were levels available in games created for profit but have now been made in essence open source. Ownership of digital content will remain complex as non tangibility remains a key factor of their existence. Scrolling textures and bump maps are examples of how depth can be faked within these digital environments. I think going forward I want to explore digital reproduction to begin with, moving through levels of depth within this.

These videos showcase the simulation of complex real world behaviour of material and animals within a digital environment. I find this fascinating as these types of simulation would not have been deemed remotely possible only a decade ago, yet now, machines are at the stage where they can almost entirely train themselves. The only limit to these types of simulations is the processing power which the computers possess. But as networks grow, and processing power increases, this expands exponentially.