This very rich, conversational thought piece asks if we, as participant designers within a complex adaptive ecology, can envision and act on a better paradigm than the ones that propel us toward mono-currency and monoculture.
We should learn from our history of applying over-reductionist science to society and try to, as Wiener says, “cease to kiss the whip that lashes us.” While it is one of the key drivers of science—to elegantly explain the complex and reduce confusion to understanding—we must also remember what Albert Einstein said, “Everything should be made as simple as possible, but no simpler.” We need to embrace the unknowability—the irreducibility—of the real world that artists, biologists and those who work in the messy world of liberal arts and humanities are familiar with.
In order to effectively respond to the significant scientific challenges of our times, I believe we must view the world as many interconnected, complex, self-adaptive systems across scales and dimensions that are unknowable and largely inseparable from the observer and the designer. In other words, we are participants in multiple evolutionary systems with different fitness landscapes at different scales, from our microbes to our individual identities to society and our species. Individuals themselves are systems composed of systems of systems, such as the cells in our bodies that behave more like system-level designers than we do.
See the study here in Frontiers of Human Neuroscience, 15 June 2017. From the abstract:
“Dancing seems a promising intervention for both improving balance and brain structure in the elderly. It combines aerobic fitness, sensorimotor skills and cognitive demands while at the same time the risk of injuries is low. […] Hence, dancing constitutes a promising candidate in counteracting the age-related decline in physical and mental abilities.”
Google and others are developing neural networks that learn to recognize and imitate patterns present in works of art, including music. The path to autonomous creativity is unclear. Current systems can imitate existing artworks, but cannot generate truly original works. Human prompting and configuration are required.
Google’s Magenta project’s neural network learned from 4,500 pieces of music before creating the following simple tune (drum track overlaid by a human):
Click Play button to listen->
Is it conceivable that AI may one day be able to synthesize new made-to-order creations by blending features from a catalog of existing works and styles? Imagine being able to specify, “Write me a new musical composition reminiscent of Rhapsody in Blue, but in the style of Lynyrd Skynyrd.
There is already at least one human who could instantly play Rhapsody in Blue in Skynyrd style, but even he does not (to my knowledge) create entirely original pieces.