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On Nonscalability: The Living World Is Not Amenable to Precision-Nested Scales / Anna Lowenhaupt Tsing
Nonscalability theory makes it possible to see how scalability uses articulations with nonscalable forms even as it denies or erases them. Entrepreneurs have already taken great advan- tage of this feature of the contemporary political economy. So have the plants and animals we call weeds and pests, and indeed the great variety of life that thrives with human disturbance. Yet scholars lag behind, holding on to the aesthetic pleasures of scalable precision even when it projects only our fantasies.
The Environment is Not A System / Tega Brain
Firstly, at the center of a data driven approach is the assumption that the past is indicative of the future. Big data has led to developments in machine learning which describes new statistical approaches that derive models from pattern in large quantities of data without any knowledge of underlying structures, whether these are language, environmental processes or genomics.
The research group, which is headed by Neri Oxman, has attempted to apply the intrinsic intelligence of natural ecologies to the way that we design and fabricate the built environment.
Part of MIT Media Lab, the group seeks to enhance the relationship between natural and man-made environments by integrating computational design with biofabrication.
When presented with more than two food sources, P. polycephalum apparently solves a more complicated transportation problem. With more than two sources, the amoeba also produces efficient networks. In a 2010 paper, oatflakes were dispersed to represent Tokyo and 36 surrounding towns. P. polycephalum created a network similar to the existing train system, and "with comparable efficiency, fault tolerance, and cost".
on sickness / Xiaowei Wang
And while dreams of quantum computing tied with neural networks are seductive, such stances overlook the extreme intelligence our cells possess.
More than a technological fix though, I hanker for a cure or the fulfilled promise of cyborg-ness. We simultaneously know so much and so little about the brain and about seeing. Mainly, what we know is that the human brain is not only extremely complex, but that the brain works alongside part of the body in fantastically interesting ways: the importance of microbes in feeling, the role of the liver in vision. And so on. It’s made me internalize the knowledge that no amount of linear algebra or fast processors could achieve really seeing.
It is a shadow that I have never seen before, but I understand the depth of the image in a physical way. When the wind blows and I see the shadows wiggle and change, I almost feel the gust against my face. I am reminded of Oliver Sacks’s distinction between information and knowledge. Computer vision has the power to sort and classify information, to aid humans in building knowledge, but I would not call computer vision a form of visual knowledge building.
Tar Pool Lab / Carl Cheng
Carl Cheng (United States, born 1942) leverages technology to “model nature, its processes and effects for a future environment that may be completely made by humans.”
Membrane Computing Systems / George Phaun
Sequential 1-membrane symport/antiport systems (SA's) are equivalent to vector addition systems, contrasting the known result that the parallel versions can define nonrecursive sets.
Nature provides many examples of systems whose basic components are simple, but whose overall behaviour is extremely complex. Mathematical models such as cellular automata (e.g. ) seem to capture many essential features of such systems, and provide some understanding of the basic mechanisms by which complexity is produced for example in turbulent fluid flow. But now one must use this understanding to design systems whose complex behaviour can be controlled and directed to particular tasks. From complex systems science, one must now develop complex systems engineering.
Perhaps no one states this belief more clearly than inventor and futurist Ray Kurzweil in his 2005 book The Singularity Is Near: “The ongoing acceleration of technology is the implication and inevitable result of what I call the law of accelerating returns, which describes the acceleration of the pace of and the exponential growth of the products of an evolutionary process.”
To claim that these devices are the result of some kind of ever-improving natural process not only misunderstands how evolution works, but it also suggests that everything from biological weapons to fraudulent startups like Theranos to Juicero (the $400 machine that squeezed juice out of packets) are necessary and natural.