Animals, Anthropology, Biological Computation, Biology, Biometrics, Education, Nature, PDF, Science

Darwinism About Darwinism (Joeri Witteveen)

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Review of Darwinian Populations and Natural Selection by Peter Godfrey-Smith (Oxford Uni Press 2009)

“… devoted to fleshing out what makes a population Darwinian. This is done by scoring a given population on a variety of parameters, such as H, the fidelity of heredity, and V, the abundance of variation. So, instead of saying that a population must have heredity and variation—in the vein of the classical approach—the Darwinian populations framework ranks populations according to how much it possesses of each. The H and V parameters are familiar; they are derived from the classical summaries. The other parameters are less obvious. G-S discusses several important ones, but notes that these do not exhaust the options; other parameters may also be important in judging how Darwinian a population is. The new parameters that are discussed at some length are α, defined as the competitive interaction with respect to reproduction, C, for “continuity” or smoothness of the fitness landscape, and S, the dependence of reproductive differences on “intrinsic character.” The concept of continuity was introduced by Lewontin as the principle that “small changes in a characteristic must result in only small changes in ecological relations” (Lewontin 1978: 169). G-S extends this principle, and turns it into a parameter. One way to understand C is as the smoothness of the fitness landscape. The smoother the fitness landscape, the higher the value C takes for the population under consideration. C is determined by causes of both internal and external nature. Internal influences stem from the organism’s physiology and development. External influences on C are location, and interaction with others. G-S assigns the internal/external difference its own parameter, S, for “intrinsic character.” The higher a population’s score on C and S, the more Darwinian are the individuals it is composed of. C and S not only tell us something about what makes individuals more Darwinian, they also serve as a replacement for another vexed notion in evolutionary theory: drift. Selection is often contrasted with drift; change may be due to selection and/or drift. G-S suggests that the C and S parameters dissolve this dichotomy. What we take to be drift is in fact a combination of low values of C and/or S. So drift and selection are not two distinct factors, but are “distinctions along the gradients of S and C” (p. 61). After having discussed some of the parameters, G-S introduces a spatial framework of three-dimensional “Darwinian spaces” as a tool for further analysis. Along each of the three axes of a Darwinian space, we can put a parameter, on which a score from 0 to 1 can be obtained. For instance, if we put the H, C, and S parameters along the axes and start scoring populations, one that scores close to (0,0,0) is very marginal, and one that sits close to (1,1,1) is a paradigmatic Darwinian population. Scoring somewhere in between will make it a minimal Darwinian population.”

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Algorithm, Analog Computing, Art, Automata, Bacteria, Biological Computation, Biology, Cybernetics, Deep Learning

Beyond design: cybernetics, biological computers and hylozoism (Pickering 2008)

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Analog Computing, Bio hacking, Biological Computation, Biology, Biometrics, Brain, Cybernetics, DNA, Science

Mind-controlled transgene expression by a wireless-powered optogenetic designer cell implant

“Mammalian synthetic biology has significantly advanced the design of gene switches that are responsive to traceless cues such as light, gas and radio waves, complex gene circuits, including oscillators, cancer-killing gene classifiers and programmable biocomputers, as well as prosthetic gene networks that provide treatment strategies for gouty arthritis, diabetes and obesity. Akin to synthetic biology promoting prosthetic gene networks for the treatment of metabolic disorders, cybernetics advances the design of functional man–machine interfaces in which brain–computer interfaces (BCI) process brain waves to control electromechanical prostheses, such as bionic extremities and even wheel chairs. The advent of synthetic optogenetic devices that use power-controlled, light-adjustable therapeutic interventions18 will enable the merging of synthetic biology with cybernetics to allow brain waves to remotely control the transgene expression and cellular behaviour in a wireless manner.”

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AI, Algorithm, Automata, Biological Computation, Code, Cybernetics, Deep Learning, Emergence, Man/Machine, Neural Networks, Robots, Science, Social intelligence, Society

Can a robot be too nice?

“Designing artificial entities perfectly groomed to meet our emotional needs has an obvious appeal, like creating the exact right person for a job from thin air. But it’s also not hard to imagine the problems that might arise in a world where we’re constantly dealing with robots calibrated to treat us, on an interpersonal level, exactly the way we want. We might start to prefer the company of robots to that of other, less perfectly optimized humans. We might react against them, hungry for some of the normal friction of human relations. As Lanier worried, we might start to see the lines blur, and become convinced that machines—which in some ways are vastly inferior to us, and in other ways vastly superior—are actually our equals.”

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Anthropology, Art, Biological Computation, Biology, Biometrics, Brain, Deep Learning, Music, Neural Networks, Psychology, Science, Social intelligence, Society, Sound

The Neuroscience of Improvisation

Charles Limb has been investigating rap. “It’s what kids are doing spontaneously when growing up… and improvisation is a strong theme. It incorporates language and rhythmic music very equally.” Limb has been scanning the brains of rappers the same way he looked at jazz musicians: comparing fMRIs when they recited memorized passages to when they “freestyled,” or improvised in rhyme. Although the study is still in progress, preliminary data suggest “major changes in brain activity when you go from memorized rap to freestyle.” Can studies of improvisation unlock more general secrets of creativity? Limb hopes to do similar investigations of artists as they draw or paint. The moderator ended with an inevitable question about art and science: “It is worth the effort to measure and quantify something abstract and artistic… to demystify what we enjoy the mystery of?” Limb saw nothing “threatening or reductionist” in the work of neuroscientists. “Humans are hardwired to seek art, and there are very few things that engage the brain on the level that music does. To understand the neural basis of creativity teaches us something fundamental about who we are, why we’re here.” Improvisation “shows us what the mind can do,” Marcus added. “The ability of human beings to improvise tells us a lot about the ultimate scope of our capabilities.”

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AI, Algorithm, Automata, Biological Computation, Brain, Code, Cybernetics, Deep Learning, Logic, Man/Machine, Mathematics, Neural Networks, Science, Social intelligence

Neural Networks and Deep Learning

“Will we understand how such intelligent networks work? Perhaps the networks will be opaque to us, with weights and biases we don’t understand, because they’ve been learned automatically. In the early days of AI research people hoped that the effort to build an AI would also help us understand the principles behind intelligence and, maybe, the functioning of the human brain. But perhaps the outcome will be that we end up understanding neither the brain nor how artificial intelligence works!”

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Algorithm, Architecture, Art, Automata, Biological Computation, Chaos, Code, Cybernetics, Drawing machine, History, Interface, Kinetic, Light, Logic, Maker, Man/Machine, Mathematics, Neural Networks, PDF, Social intelligence, Society, Tactical Media

Cybernetic Serendipity the Computer and the Arts – (1968)

Exhibition catalogue. Edited by Jasia Reichardt (Studio International Special Issue, London. 1968)

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