Convergence: What Are We Converging To?

Convergence: What Are We Converging To?

Posted 10 December 2014 by Ben Bogart
Self-Organized Landscape #4, 2009 (Architectural Study from Video) Self-Organized Landscape #4, 2009

This is the 8th in a series of posts from guest bloggers at Convergence: an International Summit on Art and Technology at The Banff Centre, from November 27-29, 2014

  • Part 1: It's Time to #ConvergeBanff
  • Part 2: Anthropomorphized Futures and Relationships With Technology
  • Part 3: Interview with Cindy Poremba
  • Part 4: Art, Technology, Process and Tool
  • Part 5: Craft, Collaboration and Computing in Art
  • Part 6: Human 2446271, Bear 71
  • Part 7: Questions Along the Art and Technology Highway

      One technical conception of convergence is the point when a learning system arrives at a stable state where further learning would no longer improve the system's ability. Under this constrained conception, convergence seems to imply a stagnation of development where a static framework unifies all knowledge. If applied to disciplinary knowledge, then convergence implies some degree of collapse of those systems of knowledge under one unified umbrella.

      I, for one, am more interested in bridging and knowledge sharing between disciplines, rather than convergence. This is because I don't subscribe to a unification of knowledge under one system. We are constantly changing what we know and how we know it, where knowledge, and the metrics we use to validate it, are continuously refined. Knowledge construction is a context dependent enquiry that can never be universally complete. When I ask what we are converging to, I am asking what the unifying framework for development is, and in which direction it points. The key here is not so much convergence, but the underlying notion of development itself – the notion that we can improve, optimize and unify all aspects of human endeavour according to a global framework (faster, cheaper, better). In the context of the Convergence event, a central manifestation of development is technological innovation.

      The notion of innovation is inseparable from the metrics we use to measure it. In medicine, we can consider innovation as the decrease of mortality. While a notion of innovation may seem obvious, it still embeds implicit values. Even in medicine, the notion of living for longer seems an obvious goal, but there is always the question of a longer life at what level of function or ability. Is living pain-free for a short time better than suffering for much longer with chronic pain? In a world of ever increasing population and life-spans, is living longer really a sustainable choice?

      Innovation in technology seems driven by an engineering process of incremental (local) development. We can easily point to an increase of display resolution or processor clock speed, or a decrease in computer size as being better, but do those metrics really give us any meaningful value beyond economic viability? What resolution is high enough? As we seem to drive media innovation towards a perfect representation of reality, how much do we reflect on the implications of pointing to a future where simulations and reality have converged and are indistinguishable? I would argue that perception is constructive, and that as media representations appear closer to reality, it's as much their novelty as their resemblance to reality that drives our interest in them. Once the novelty is normalized, the previous hyper-real media can still appear dated because our sensitivity and attention change how we perceive them. As representations and simulations get closer to reality, the more we expect of them.

      As cognitive creatures, we humans are quite good at learning the structure and regularities in the sensory world. We use these regularities to construct internal models that we can use to predict outcomes. Many people have had the experience of acting without conscious will, be it driving home from work or completing some other overly familiar set of actions. Once we learn skills that can be executed with little (or no) intentional control, our minds can run on autopilot. The problem with our ever present ability to learn is that we too easily take for granted the status quo. The speed of the computer, or resolution of a display, becomes normalized and therefore also invisible. We are ever destined to learn and internalize those technologies we use, but what do these rays of incremental 'innovation' point to? The global direction is an emergent result of small local changes.

      While it may not be possible to trace how these local changes point to global frameworks, we can uncover implicit values by reflecting on the metrics used to drive development. We need critical skills to articulate and reformulated these values and how they are reflected in development. Can we innovate ourselves out of the problems caused by our innovation, or should we step back and see how innovation (and cultural values) can be constrained and critiqued through cultural and social solutions? We should not let local incremental change blindly drive innovation, but be critical of our metrics and make sure they align with our values. It is only through knowledge of our values, implicit in local choices, and metrics that we have any chance of seeing where development points to, let alone steer it in the direction we want it to go.


      Ben Bogart

      About the Author: Ben Bogart

      Ben Bogart is a generative artist primarily working in installation and print whose practice is located at the intersection of art and science. His installations create content live in response to their sensed environment. Physical modelling, chaos, feedback systems, evolutionary algorithms, computer vision, and machine learning have been used to inform and engage in his creative process. Ben holds a Ph.D. in Interactive Arts and Technology from Simon Fraser University. In his Ph.D. research, he proposes an Integrative Theory of cognitive and neurobiological mechanisms of perception, mental imagery, mind-wandering, and dreaming. This cognitive framework is manifest in a computational model and site-specific generative art installation: Dreaming Machine #3.

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