IDEAS 3



A NETWORK OF IDEAS



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LINEAR THINKING

I habitually organise my ideas one after the other, from A to B to C etc. In my memory however, IDEAS are not organised in this linear fashion, they form a NETWORK in my ASSOCIATION CORTEX. In order to find them there I need to learn to THINK in a different manner:


NETWORKING


I have tried to organise all the texts in my collection into a NETWORK OF KEYWORDS.

This - I hope - will make it easier for me (and for you) to find clusters in the vast sea ideas. This network of ideas is an ongoing process. The structure will never be finished. New ideas and new keywords will always be added.
 


COGNITION - KOGNITION
PERCEPTION - QUALIA -  METAPHYSICS

KEYWORDS: COGNITION

Complexity


Life is all about insuring information is passed on, or transmitted, while at the same time preventing entropy from corrupting the message. Life has found a way to ensure that entropy keeps increasing but not that the expense of its own survival or the integrity of the information it wants to transmit.

This is the biggest and best tricks that life has learned. Now ALife is helping us to understand just how it does it. It is starting to show that living things do not rely on the properties of chemicals to foster another generation, they depend on information encoded in chemical form. ALife is showing how the dynamics of information can come to dominate over the properties of the material living things are made of.

Mark Ward
VIRTUAL ORGANISMS
Pan Books 1999


DIGITAL BIOLOGY

pg 19

THE UNIVERSE OF IDEAS

The universe of ideas, or memes, is quite different from ours. The memetic universe has very different scaffolding from ours. Objects (memes) in the memetic universes have no positional size - there are no spatial dimensions. There is a dimension of time, however. So memes can exist at certain times, but not at certain locations. Because of this, there can be no movement, no speed, no acceleration, no collisions. But there are some exotic rules that define when memes are created. It seems that as a travel forward along the time axis of this universe we see more and more memes in existence. Perhaps then one rule of this universes is that the number of memes increases as time passes. Other rules also seem to affect this universe. New memes are always very similar to existing memes except for slight variations. Some memes seem to persist for long periods; others come and go quickly. At times a specific property of a meeting will cause many memes to be created soon after, like the population explosion.

Because the memetic universes are different from ours, it is very hard to imagine that it might really exist. If it does, it will be because the theories of memetics are somehow shown to be true. This viewpoint suggests that ideas, concepts and even catchy tunes exist like parasites in our minds. We call them memes, and the ones that we favour become more numerous, while memes we dislike slowly fade away. This is a revolutionary process: the evolution of memes within our combined brains, books and other media. Memetics requires us in our physical world to copy, alter and select memes. At the memes themselves exist in the memetic universe (sometimes called the meme pool ), with its unfamiliar rules that our defined in terms of our own universe. (Bentley10)

Cellular Automata

Stephen Wolfram


Keywords : Conway's Game of Life - unpredictability - a complex world that derives from simple premises - Von Neumann's cellular automaton - emulate any self-replicating machine - act like a universal Turing machine - By picking the right rules Conway believed that he was, in some less complicated way, rerunning a process that takes place in our own universe. At some point (perhaps Planck time) everything was set up, the rules were established and the whole thing was left to run. The same rules determined what happened to new generations of organisms in our universe. - CA - living organisms are physical systems made up of elements operating to a set of rules -some cells in the body act like computers and process information - Ed Fredkin - Stephen Wolfram -

Wolfram declares himself frustrated by the slow progress of biology to reveal how living processes are organized and the way they work. He has strong opinions and is contemptuous of most of biology, seeing it as mere naturalism rather than an investigation into fundamentals. 'What we call life is something that is defined more by its history and heritage than its properties,' he says. The problem with life is that it is hard to know what is important and what is not. Wolfram says that while models of living systems can be built, typically they have not been very successful, usually because they are over-complicated. Wolfram thinks that CAs may be a way to investigate those properties and gain a much better understanding of how life is organized. Wolfram says that by analysing CAs 'one may, on the one hand, develop specific models for particular systems, and, on the other hand, hope to abstract general principles applicable to a wide variety of complex systems.

- one-dimensional CA - CAs can be divided into four different classes. The first sort of rule sets prodoced patterns that quickly died out. The second class found a stable form and reproduced it for ever. A third type produced chaotic patterns that keep growing. The patterns produced are fractals. Patterns that look the same at different scales. The fourth sort produced patterns that never settle down and grow and contract irregularly. - Fractal patterns - Chris Langton - the order goes I, II, IV, III - The most interesting systems on Earth, the living ones, are a tricky mixture of both complexity and chaos - lambda value - critical phase transition - universality - life uses information to maintain itself in the critical phase transition region

Mark Ward
Virtual Organisms
The Startling World of Artificial Life
Pan Books
pg 72


CONSCIOUSNESS - Consciousness Studies

Christopher Altman

The study of consciousness and its origins has experienced radical breakthroughs with the advent of emerging technologies in the field of neuroscience. Once overcast with a veil of mystery, the inner workings of the brain have undergone profound illuminations made possible by rapid advances in the computing industry, coupled with insights gleaned through medical science. New technologies such as MRI scanning have enabled scientists to understand the neurological correlates of mental processes in ever finer detail, allowing them to glimpse the interior of a fully-functioning brain in real-time. However, even with these exponential advances in the field of consciousness, laying a theoretical foundation to explain precisely how it arises currently remains a cryptic and insurmountable task. The most important scientific discovery of our time will be when this problem is resolved.
PROPOSED THEORIES OF CONSCIOUSNESS

The scientific community has generally accepted that consciousness is an emergent system-level feature of neurophysiological processes. Exactly how our individual subjective experiences arise has been a matter of long-standing debate in both scientific and philosophical circles, but there are a number of currently proposed theories that attempt to resolve the mystery of consciousness. Among these theories are (1) consciousness is a feature of synchronized resonance within neurons in the frontal cortex, (2) consciousness results through the mechanism of quantum coherence in neuron microtubules, and (3) consciousness is an emergent property of complex systems.


Fractal Music

Ward76

Universality

Opem systems - flow of energy or Information

"self-similar" systems started to be called fractals.

Fractals

Power Laws

Fractals are distinguished by the fact that they have no characteristic length scale. There are also fractals that have no characteristic time scale, i.e. the pattern of events look self-similar on all time scales, whether you're studying a second, a minute or an hour. The these fractals go by the name of 1/f (pronounced one-over-f) noise.

1/f Noise


Ward96

Cellular Automata

John von Neumann

John Horton Conway

Stephan Wolfram

Class 1 patterns go nowhere: they either disappear quickly or settle into a fixed state.

Class 2 patterns grow to a fixed size and then repeat forever.

Class 3 patterns yield chaotic patterns that look similar but never repeat.

Class 4 patterns produce complex patterns that grow and contract regularly.


Wardal72:

The one-dimensional CA on which Wolfram did his initial work had 256 different rule sets. Wolfram seeded the first line of his CA with a random number of live cells, then tried out the different rule sets to discover the kinds of patterns they produced.

He found that CAs can be divided into four different classes. The first sort of rule sets prodoced patterns that quickly died out. The second class found a stable form and reproduced it for ever. A third type produced chaotic patterns that keep growing. The patterns produced are fractals. Patterns that look the same at different scales. The fourth sort produced patterns that never settle down and grow and contract irregularly. The classes that Wolfram identified do not just apply to CAs, they also seem to have parallels with the behaviours seen in dynamic systems.

Class I CAs swiftly reach what are known as limit points, effectively dead ends. Class II CAs produce patterns that are very similar to the small, discrete cycles like gyres, eddies and waterspouts that sometimes appear in liquids and gases. Class III CAs tend to produce patterns that are chaotic and never settle down; global weather patterns are the classic example of this kind of dynamic system. Class IV CAs are hard to define. They are easy to identify when set against the other classes of CAs but as Chris Langton, another longtime CA fan, says: 'no direct analog for them has been identified among continuous dynamic systems'. They produce complex structures that persist over long periods of time, like life.

Wolfram's survey was empirical rather than analytical but it seems to have revealed some qualitative differences between CAs. His results have been independently corroborated and extended by others in the ALife field. The survey has important implications for anyone who believes that CAs can be used to study complexity or analyse some biological phenomena. Wolfram believes that organisms often unknowingly employ CA type systems to create some of their characteristics. One of the most striking examples of this can be found on the shell of the mollusc Natica enzona. Anyone comparing the patterns on the shells of these molluscs and some of the patterns that form in a CA would be hard pressed to deny some sort of connection. If it is the case that recursive systems are widely used in nature, then starting to classify them could also help us to understand them. At the very least they could be used to model them. It is unlikely, though, that living systems use only one sort of dynamic system. Wolfram is convinced that Class III and IV CAs are most lifelike.

Mark Ward
Virtual Organisms
The Startling World of Artificial LifePan Books
pg 72

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