" My approach to understanding the full implications of Gödel's work is mathematically analogous to the ideas of thermodynamics and Boltzmann and statistical mechanics. You might say, not completely seriously, that what I'm proposing is 'thermodynamical epistemology'!"
- G. Chaitin

"A lady said, 'What's your solution?'
I said, 'There are no solutions. There are only trade-offs.'
She said, 'The people demand solutions!'
- Thomas Sowell

IBM has devised a way to let computers think like vertebrates.

Charles Peck and James Kozloski of IBM's Biometaphorical Computing team say they have created a mathematical model that mimics the behavior of neocortal minicolumns, thin strands of tissue that aggregate impulses from neurons. Further research could one day lead to robots that can "see" like humans and/or make appropriate decisions when bombarded with sensory information.

A research paper on the model is expected to come out this week.

The brain consists of roughly 28 billion cells, Peck explained. The 200 million minicolumns essentially gather sensory data and organize it for higher parts of the brain. The minicolumns also communicate with each other through interconnections. Minicolumns are roughly 1/20 of a millimeter in diameter and extend through the cortex.

The mathematical model created at IBM simulates the behavior of 500,000 minicolumns connected by 400 million connections. With it, "we were able to demonstrate self-organization" and behavior similar to that seen in the real world, Peck said.

The absurd capriciousness underlying such a memory system is best represented by the categorization scheme of an ancient Chinese encyclopedia entitled Celestial Emporium of Benevolent Knowledge, as interpreted by the South American fiction master J. L. Borges.
On those remote pages it is written that animals are divided into (a) those that belong to the Emperor, (b) embalmed ones, (c) those that are trained , (d) suckling pigs, (e) mermaids, (f) fabulous ones, (g) stray dogs, (h) those that are included in this classification, (i) those that tremble as if they were mad, (j) innumerable ones, (k) those drawn with a very fine camel's hair brush, (1) others, (m) those that have just broken a flower vase, (n) those that resemble flies from a distance.

If you want to find alien life-forms, hold off on booking that trip to the moons of Saturn. You may only need to catch a plane to East Lansing, Michigan.

The aliens of East Lansing are not made of carbon and water. They have no DNA. Billions of them are quietly colonizing a cluster of 200computers in the basement of the Plant and Soil Sciences building at Michigan State University. To peer into their world, however, you have to walk a few blocks west on Wilson Road to the engineering department and visit the Digital Evolution Laboratory. Here you'll find a crew of computer scientists, biologists, and even a philosopher or two gazing at computer monitors, watching the evolution of bizarre new life-forms.

These are digital organisms-strings of commands-akin to computer viruses. Each organism can produce tens of thousands of copies of itself within a matter of minutes. Unlike computer viruses, however, they are made up of digital bits that can mutate in much the same way DNA mutates. A software program called Avida allows researchers to track the birth, life, and death of generation after generation of the digital organisms by scanning columns of numbers that pour down a computer screen like waterfalls.

After more than a decade of development, Avida's digital organisms are now getting close to fulfilling the definition of biological life. “More and more of the features that biologists have said were necessary for life we can check off,” says Robert Pennock, a philosopher at Michigan State and a member of the Avida team. “Does this, does that, does this. Metabolism? Maybe not quite yet, but getting pretty close.”

One thing the digital organisms do particularly well is evolve.“ Avida is not a simulation of evolution; it is an instance of it,” Pennock says. “All the core parts of the Darwinian process are there. These things replicate, they mutate, they are competing with one another. The very process of natural selection is happening there. If that's central to the definition of life, then these things count.”

The more we find out about genomes, the more humiliating the news they bring us. The human genome turns out to be profoundly ordinary. We have known for decades that human beings have one fewer chromosome than chimpanzees, which should have been ample warning. We have known for years that grasshoppers have three times as much DNA per cell as we do, deep sea shrimps ten times, salamanders 20 times and African lungfish a staggering 40 times. But we still kidded ourselves until just the last few years that human beings would prove to have more genes, arranged in a more sophisticated way, than most other creatures. How else to explain our exquisite brains?

We have 25,000 genes (or recipes for protein molecules) which is the same as a mouse, just 6,000 more than a microscopic nematode worm and 15,000 fewer than a rice plant. However sophisticated our brains are, it is not reflected in our genes. This has led some to suggest that we have been exaggerating the role of genes in shaping our brains. In fact, it reminds us that recipes are more than lists of ingredients. How those ingredients are cooked is also crucial. And the instructions for cooking up a body are hidden in the genome too - between the genes themselves.

The evolution of specialization in a multi-agent system is studied both by computer simulation and Markov process model. Many individual agents search for and exploit resources to get global optimization in an environment without complete information. With the selection acting on agent specialization at the level of system and under the condition of increasing returns, the division of labor emerges as the results of long-term optimizing evolution. Mathematical analysis gives the optimum division of agents and a Markov chain model is proposed to describe the evolutionary dynamics. The results are in good agreement with that of simulation model.

Key Words: division of labor, evolutionary dynamics, multi-agent system, emergence.
Zengru Di, Jiawei Chen, Yougui Wang, and Zhangang Han
Department of Systems Science, Beijing Normal University, Beijing, 100875, China

Internet download speeds could be improved dramatically by mimicking Darwin's evolution to "breed" the best networking strategies, say computer scientists.

Transferring popular data across the internet repeatedly can be inefficient and costly, so networking companies have developed ways of temporarily storing, or "caching", data at different locations to reduce costs and increase download speeds.

But figuring out where to store data and for how long is a complex problem. One solution might be to have caches "talk" to each other repeatedly, but this is inefficient as it takes up a lot of bandwidth.

To tackle the challenge, Pablo Funes of US company Icosystem and Jürgen Branke and Frederik Theil of the University of Karlsruhe in Germany used "genetic algorithms", which mimic Darwinian evolution, to develop strategies for internet servers to use when caching data. Using a simulation they were able to improve download speeds over existing caching schemes.

In finding an answer to “perhaps the greatest unsolved ecological riddle,” evolutionists propose that diversity is a testament to there being more than one way to make a living.

The riddle: Why are some habitats loaded with many more species than others?

The answer: Nature and evolution respect that there’s more than one way of doing things.

“What we’ve learned,” said Michigan State University scientist Charles Ofria, “is that if there isn’t just one way to succeed, you’ll see diversity.”

In an article published in the July 2 issue of Science, an interdisciplinary team of scientists at MSU, the California Institute of Technology and Keck Graduate Institute (KGI), with the help of powerful computers, has used a kind of artificial life, or ALife, to gain insight into questions of evolution.

Gregory Chaitin has devoted his life to the attempt to understand what mathematics can and cannot achieve, and is a member of the digital philosophy/digital physics movement. Its members believe that the world is built out of digital information, out of 0 and 1 bits, and they view the universe as a giant information-processing machine, a giant digital computer. In this book on the history of ideas, Chaitin traces digital philosophy back to the nearly-forgotten 17th century genius Leibniz. He also tells us how he discovered the celebrated Omega number, which marks the current boundary of what mathematics can achieve. This book is an opportunity to get inside the head of a creative mathematician and see what makes him tick, and opens a window for its readers onto a glittering world of high-altitude thought that few intellectual mountain climbers can ever glimpse.

Abstract:
To analyze the evolutionary emergence of structural complexity in physical processes we introduce a general, but tractable, model of objects that interact to produce new objects. Since the objects--\emph{$epsilon$-machines}--have well defined structural properties, we demonstrate that complexity in the resulting population dynamical system emerges on several distinct organizational scales during evolution--from individuals to nested levels of mutually self-sustaining interaction. The evolution to increased organization is dominated by the spontaneous creation of structural hierarchies and this, in turn, is facilitated by the innovation and maintenance of relatively low-complexity, but general individuals.

Authors: James P. Crutchfield, Olof Gornerup
Comments: 4 pages, 3 figures
Report-no: Santa Fe Institute Working Paper 04-06-020
Subj-class: Adaptation and Self-Organizing Systems

Abstract:
Motivated by a recent article on open problems in artificial life, here I postulate three laws which form a mathematical framework to describe artificial life evolutionary dynamics. They are based on a continuous approximation of population dynamics. Four dynamical elements are required in this formulation: ascendant matrix, transverse matrix, fitness function, and the stochastic drive. The first law states that in the absence of stochastic drive the artificial life always seeks for a local fitness attractor and stay there. It gives the reference point to discuss the general evolutionary dynamics. The second law is explicitly expressed in a unique form of stochastic differential equation with all four dynamical elements. The third law defines the relationship between the focused level of description to its lower and higher ones, and also defines the dichotomy of deterministic and stochastic drives. These laws provide a coherence framework to discuss several current problems, such as emergency and stability. In particular, two quantities are emphasized: the fitness function as the standard for selection and the stochasticity as the source of creativity. Those three laws may appear almost self-evident from a statistical physics point of view. However, their equivalent to a most conventional approach for evolutionary dynamics is shown for the first time by the present author, to the best of his knowledge. The computational advantage of the present formulation in the study of artificial life evolution is also discussed.

Subj-class: Adaptation and Self-Organizing Systems
P. Ao
Departments of Mechanical Engineering and Physics, University of Washington, Seattle, WA 98195, USA

Scale-free networks are everywhere. The can be seen in airline traffic routes, connections between actors in Hollywood, weblog links, sexual relationships, and terrorist networks. So what exactly is a scale-free network? A scale-free network is one that obeys a power law distribution in the number of connections between nodes on the network. Some few nodes exhibit extremely high connectivity (essentially scale-free) while the vast majority are relatively poorly connected. The reason that scale-free networks emerge, as opposed to evenly distributed random networks, is due to these factors:

* Rapid growth confers preference to early entrants. The longer a node has been in place the greater the number of links to it. First mover advantage is very important.
* In an environment of too much information people link to nodes that are easier to find. This preferential linking reinforces itself by making the easier to find nodes even more easy to find.
* The greater the capacity of the hub (bandwidth, work ethic, etc.) the faster its growth.

The Strength and Weaknesses of Scale-Free Networks
The proliferation of scale-free networks and our increasing dependence on them (particularly given their prevalence in energy, transportation, and communications systems) begs the question: how reliable are these networks?

Ecology has always lacked a strong overarching theory like those behind physics or chemistry. But no longer, thanks to the universal throttle inside every living thing, say ecologist James Brown and physicist Geoffrey West

If you want to experience the diversity and complexity of life, walk into any forest anywhere in the world. Most revealing would be a tropical forest in Brazil, New Guinea or the Congo. Look at the birds, butterflies, ants and plants. Listen to the sounds of animals you cannot see. In just half a hectare there may be more than 1000 individuals of 100 different tree species and millions of individuals representing thousands of insect species. Is there any order to this jumble? It is hard to imagine learning the rules to the games all these organisms are playing.

Of course, any ecologist will tell you we can work out a few of the rules. This insect pollinates this tree; that one feeds on its leaves. Some plants are short-lived weedy opportunists; others grow slowly but live for centuries. But almost all such rules apply only to a specific collection of species in a particular place and time. Look elsewhere - sometimes even just the next river basin - and the rules are different. Despite a hundred years of research, ecology has little in the way of universal laws or principles like the laws of gravity and thermodynamics in physics or the Mendelian laws of inheritance in biology. Is ecology really devoid of universal laws?

We think not. The laws are there, just waiting to be discovered. In the past few years we have been leading a unique collaboration of biologists and physicists that has been investigating the forces behind a host of ecological patterns, and we have identified one factor that seems to have dramatic ecological consequences: metabolic rates, the rates at which organisms use energy and materials. Our developing "metabolic theory of ecology" has already provided powerful general explanations for how metabolic rate changes with the body size and temperature of animals, plants and microbes. It also predicts and explains many simple and regular relationships - akin to the overarching laws in other areas of science.