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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AI, Animals, Brain, Emergence, Ethics, philosophy, Religion, Robots, Science, Society

Buddhist perspectives on AI

“From the viewpoint of Buddhism, all life is emergent, entities functioning at a capacity greater than the sum of their parts. There is no special qualifier that separates any form of intelligence from another (note that even consciousness is on the list of things that we aren’t.”. This means that an intelligence inside of a robot body, a computer, or existing on the Internet would be just as worthy of being considered “alive” as a squirrel, a human, or a bacteria. Further, Buddhism accepts the existence of life that does not have a physical body. In the Buddhist mythology, beings that exist in realms without physical bodies are described and treated the same as those with physical bodies. Although this ethic is ascribed to mythical beings, if we begin to see actual beings that exist in “formless realms”, most Buddhists would likely see no problem accepting them as living. In Buddhism, a computer intelligence would be viewed by most as a new form of life, but one equally possessed of the heaps and equally capable of emergent behavior and enlightenment. The Dalai Lama, Thich Nhat Hanh, and several other high profile Buddhist thinkers have already spoken in support of AI as a living being.”

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Animals, Biology, Biometrics, DNA, Economy, Education, Nature, Neural Networks, Science, Social intelligence, Society

Twittering bacteria: on bacteria… social intelligence

“New research suggests that microbial life can be even richer: highly social, intricately networked, and teeming with interactions [47]. Bassler [3] and other researchers have determined that bacteria communicate using molecules comparable to pheromones. By tapping into this cell-to-cell network, microbes are able to collectively track changes in their environment, conspire with their own species, build mutually beneficial alliances with other types of bacteria, gain advantages over competitors, and communicate with their hosts – the sort of collective strategizing typically ascribed to bees, ants, and people, not to bacteria. Eshel Ben-Jacob [6] indicate that bacteria have developed intricate communication capabilities (e.g. quorum-sensing, chemotactic signalling and plasmid exchange) to cooperatively self-organize into highly structured colonies with elevated environmental adaptability, proposing that they maintain linguistic communication. Meaning-based communication permits colonial identity, intentional behavior (e.g. pheromone-based courtship for mating), purposeful alteration of colony structure (e.g. formation of fruiting bodies), decision-making (e.g. to sporulate) and the recognition and identification of other colonies – features we might begin to associate with a bacterial social intelligence.”

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Algorithm, Animals, Automata, Bio hacking, Biology, Biometrics, DNA, Man/Machine, Medicine, Nature, Neural Networks, PDF, phenomenology, Science

The Algorithmic Origins Of Life

“To avoid an infinite regress, in which the blueprint of a self-replicating UC contains the blueprint which contains the blueprint . . . ad infinitum, Von Neumann proposed that in the biological case the blueprint must play a dual role: it should contain instructions – an algorithm – to make a certain kind of machine (e.g. UC – Universal Constructor) but should also be blindly copied as a mere physical structure, without reference to the instructions its contains, and thus reference itself only indirectly. This dual hardware/software role mirrors precisely that played by DNA, where genes act both passively as physical structures to be copied, and are actively read-out as a source of algorithmic instructions. To implement this dualistic role, von Neumann appended a “supervisory unit” to his automata whose task is to supervise which of these two roles the blueprint must play at a given time, thereby ensuring that the blueprint is treated both as an algorithm to be read–out and as a structure to be copied, depending on the context. In this manner, the organization of a von Neumann automaton ensures that instructions remain logically differentiated from their physical representation. To be functional over successive generations, a complete self-replicating automaton must therefore consist of three components: a UC, an (instructional) blueprint, and a supervisory unit.”

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Animals, Architecture, Bio hacking, Biology, Biometrics, Nature, phenomenology, Science, Tactical Media

Urban frogs use drains as mating megaphones

“This is perhaps the first study to show that an animal preferentially uses human-made structures to potentially enhance the sounds of its vocal communication signals,” says Mark Bee, a biologist at the University of Minnesota, Twin Cities, in St Paul. “These males could be taking advantage of the enhanced acoustics in drainage ditches to outdo their competition.”

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Animals, Biology, Biometrics, Brain, Farming, Nature, Neural Networks, Science

Cognitive Maps in Bees

“Experimentally captured and displaced bees often depart from the release site in the compass direction they were bent on before their capture, even though this no longer heads them toward their goal. When they discover their error, however, the bees set off more or less directly toward their goal. This ability to orient toward a goal from an arbitrary point in the familiar environment is evidence that they have an integrated metric map of the experienced environment.”

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Algorithm, Animals, Biology, Biometrics, Brain, Film, Man/Machine, Medicine, Nature, Neural Networks, Science

Filming the World Laboratory Cybernetic History in Das Netz

“But in the brain as McCulloch and Pitts imagine it, computation does not proceed along an infinite linear tape. Instead, complex series of equations are mapped out as pathways through a finite network of neurons. Patterns of electrochemical impulses correspond to the propositions of symbolic logic, expressed in the mathematical terms developed earlier in the century by logical empiricists such as Carnap (with whom Pitts had studied).7 Thus, the very process of thinking in language becomes equivalent to neural computation. In this way, the two scientists arrived at their fundamental breakthrough, stated in the title of their 1943 paper: “A Logical Calculus of Ideas Immanent in Nervous Activity.”8 What they had done was to map out the possible circuits of feedback in the flesh.”

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