Jared Diamond is a professor of geography at UCLA and author of the popular bestsellers Guns, Germs and Steel: The Fates of Human Societies and Collapse: How Societies Choose to Fail or Succeed. In this lecture, Diamond argues that religion has encompassed at least four independent components and functions that have arisen or disappeared at different stages of development of human societies over the last 10,000 years (the talk is only 40 minutes, the rest are questions and answers).

He asks what the benefit and function of a religion for a society is, and lists four basic functions: explanation, justification of government, conservation of society, motivation of warfare. The first function has been taken over by science, the second function has been taken over by politics and the political system, the third function has been taken over by courts and the legal system, and the fourth has become obsolete. Originally an important function was explanation of nature, culture, society and history. Another function is to justify the existence of chiefs, kings and priests and other centralized decision makers. Religions have the function to teach obedience and moral values (what’s right and what’s wrong). And finally they justify wars against other religions. Therefore religions originally were not only an aspect of society, they enabled a society and higher civilization in the first place. They were able to hold a larger society together, where no longer everyone knows everybody. Religion is an assemble of shared beliefs and values which enable the creation, existence and cohesion of a group by combining the interests of the individual with the interests of the community. A blog post said about religions and people

Many years ago, a missionary doctor told me that most Africans – in the nation he was proud to serve – had little use for communists or communism because the communists said there is no God – so we can do whatever we want. The Africans generally said, “Everyone knows there is a God. Every reasonable person can see that. The world we live in shows it clearly. What people want to know is how to get in touch with him, and get help for our problems.”

Religion prevents that we can do whatever we want, and it tells us how to get in touch with god (which stands here for the group), and how to get help from the group for all kinds of personal problems. As Randall Collins notices in his book Sociological Insight, the basic religious terms are related to group terms:

• priest: teaches rules of a group
• prophet: creates or invents rules for a group
• sin: breaking the rules of a group
• blessing: wish to be included in the group
• curse: wish to be excluded or banned from a group
• heaven: to be included and supported by the whole group
• hell: to be excluded and haunted by the whole group
• salvation: to be saved by the group from punishment and annihilation
• damnation: to be condemned to permanent exclusion from the group
• good: positive for the group (i.e. help a member)
• evil: negative for the group (i.e. hurt a member)
• profane: belongs to the individual
• divine/sacred: belongs to the group
• sacred text: history of the group
• sacrifice: gift from a member to the group, who donates something to the group. In the strongest form this can even be the life of a member: a deliberate loss of an individual (like Apoptosis) to benefit the group
• baptism: creation of a new group membership
• to be baptized / to be reborn: to become a new member of a group

Religion is all about groups and communities. A group is larger, stronger and bigger than a single individual and can of course accomplish much more. Community structure and religion were indeed closely related in ancient cultures: city states with many independent cities had many gods, one for each city, which was worshipped in the temple of the city. In small countries like Israel with only one major city and one temple, there was only one god. A unified empire or strong dictatorship often also tolerates only one religion and one god. In ancient cities, the house of god was temple, town-hall and storeroom. Today, the house of god is a name for a church, and the church is the place where the meetings of the religious group takes place. Basically, the house of god is the house of the group.

If we want to know how society arises and what continues to hold it together, we must take a look at religions. Their evolution is an interesting topic. Religions are the glue which keeps societies together by shared beliefs and common social rituals. They are a set of memes which use groups as survival machines. In all early cultures a religion was the central element that made the emergence and the existence of a society possible. And if we want to know how self-consciousness arises, we have to look at religions, too. The belief in the self (or soul) and the belief in god are very similar. In both cases, an invisible, immortal agent is identified, in the first case it controls the person, in the second case the group, country or society. To be effective, the belief must be enforced by recurrent rituals: private prayers are nothing must concentrated actions for the crowd within, public prayers are social rituals for assembled groups. Social rituals glorifying the founders and the common interests of groups have a central function: they manifest in their synchronicity the ties holding people together, and they enforce them through feedback illusions. By collectively performing activities, the group acts as a single unit.

I found this picture in a blog entry from Rudy Rucker. It shows from top left to bottom right the leading members of the Nazi Party: Joseph Goebbels, Heinrich Himmler, Hermann Göring, and Adolf Hitler.

There is some truth in this poster. They all were big losers before they became leaders. And they all had their difficulties with women:

• Goebbels was a cripple and limped, and he was a failed writer: his novel did not find a publisher and his plays were never staged. He took control of the media as Reich Minister of Propaganda
• Himmler was a cruel, antisocial and uptight chicken farmer with an inclination to mysticism. He took control of the police and security forces.
• Göring was a fat drug addict who effectively took control of the economy as Reich Minister of Economics
• Hilter was an  unemployed, failed and paranoid postcard painter who became the dictator of Nazi Germany

Goebbels was perhaps the smartest of them, but he has physically handicapped. The others were more or less dumb, especially Hitler. He made it in the army to the lowest possible rank, and worked in WW I as an errand-boy. His application for the academy of arts was rejected, and as a postcard painter in Vienna (Wien) he failed miserably, and belonged to the jobless and unemployed. Instead of embracing failure as inevitable he tried to avoid it at all costs, and started to blame others. And instead of listening to others, he started to talk in public beer gardens and revealed everyone that he is a fool.

Although Nazis are the evil guys in Hollywood films, they were not evil. They were just some dumb losers, who happen to command a hierarchy of mindless bureaucrats. Hannah Arendt described this phenomenon by coining the phrase Banality of Evil. Those who dreamed of creating a new superior race were in fact severely handicapped themselves. They came to power by a series of accidents and lucky circumstances. I wrote earlier how losers can become leaders. In this case the lost World War and the economic depression helped a lot. The Germans had lost the first World War badly, and esp. in the following global economic crisis the average German felt like a loser: outraged, angry and frustrated. The leaders of the Nazi party represented the average German after WW I. The angry speeches of Hitler captured this mood perfectly. And he was excellent in blaming others – especially the Jews – for all bad things. This is what Politicians do, there is hardly a better campaign strategy.

Was this a reason why the Germans tolerated a group of losers at their top for so long? Well, maybe, of course there was also a dictatorship in the end. Hitler became a real dictator after the ‘terrorist attack’ on the national parliament, the Reichstag, which burnt completely down. Immediately a ‘dutch communist’ was found guilty and the communists were blamed to threaten national security. Civil liberties were suspended to increase national security. One could describe the situation as a frozen accident. The losers became accidentally the new leaders, and due to an accident they managed to stay there.

Ten years ago, the Matrix film trilogy described a future in which reality perceived by humans is only a simulated reality created by sentient machines. Although the Cyberpunk genre offers room for much more stories about virtual worlds, only a few have appeared which are as brilliant, creative, or influential.

One exception are the Otherland books written by Tad Williams. And based on the successful Otherland novels, there is a new massively multiplayer online game (MMO) coming out soon. Like the Matrix it deals heavily with the interplay of the virtual and the real. In the world of Otherland, the ultimate status symbols of the super-rich are their own personal virtual realities, tailored to their interests and specifications. These private virtual worlds are populated by AI and unwitting humans who don’t know that what they’re experiencing isn’t real.

Maybe stuff for a good film, too? The major elements which made the Matrix interesting are here: the interplay of the virtual and the real, the transitions between one world and another (for instance being trapped into a virtual reality while falling into a coma in another), and elements of mystery, adventure, suspense and wonder. I’m curious about it.

Wolfram’s new kind of science is already 7 years old – not quite new anymore, but still interesting. Has he found a whole new way of looking at processes in our universe? Well, scientists have always tried to find simple explanations for complex phenomena. Yet only a few have considered cellular automata as a good way to find such explanations. And even less have examined cellular automata as deep as Wolfram has. Judge yourself:

Can the Internet become conscious and aware of itself? I dont’ think so. Can a net in general become conscious? Yes, certainly: the neural networks in our head do. So what is the difference? I think when a net becomes conscious of itself, it must match at least the following three conditions (see also here):

* distributed orchestration: even if it is huge, the whole net must be focussed on a single goal: controlling an AI. Although it is a large distributed system, the net must act in real-time as a unified entity, i.e. the whole net must be involved in the same operation, in reacting to a single stimulus in each moment

* embedded encapsulation: the net should not be conscious of itself (i.e. its parts) if it wants to become conscious of itself (i.e. the whole). Imagine an AI in a virtual world controlled by a large net running on a server farm. Any knowledge of the server farm or the operations in it would distract the AI and destroy the illusion of a central self. Life-forms have different degrees of consciousness, but no living animal is conscious of the operations on the single neuron level.

* grounded representation: the net must be complex enough to represent and map a whole world: it must be able to represent each situation described by the continuous set of inputs by some form of abstract structure or language. This language is used to extend the ordinary perceive-reason-act cycle: perceptions turn into beliefs and actions into intentions, until the belief in a single self emerges

P.S. theses conditions concern nets with ‘dumb’ and simple nodes. They are less important if they are applied to nets with complex nodes, i.e. to group of persons, because these nodes are already conscious of themselves. A group of people can develop some sort of consciousness (for example a religious group, a sport club, a party, ..). The “many belief they are one” phenomenon works best if the three conditions are fulfilled. This is the case in common rituals if the group acts as one (distributed orchestration), if the individual vanishes in the mass (embedded encapsulation), and if the group mind focusses on certain symbol and symbolic actions (grounded representation). The difference is that the conditions are not fulfilled normally, only at special occasions which occur regularly.

The EBNF form to describe the syntax of programming languages is based on simple recursive substitution rules. There are different recursive substitution rules, the Chomsky hierarchy describes the four basic types of grammars which can be used to generate languages: unrestricted, context-sensitive, context-free, and regular. Yet all of these recursively enumerable languages can be recognized by a primitive serial Turing Machine. John Backus said “Conventional languages are basically high-level, complex versions of the von Neumann computer.” All programming languages are ways to express primitive serial computations for serial von Neumann computers.

If we deal with parallel, distributed, or agent-oriented computing systems, the situation becomes more interesting, and we must go beyond primitive serial machines. In order to describe this case we would not only need a single Turing Machine, but a number of coupled Turing Machines (type two Turing Machines) similar to Wegner’s interaction machines. Would it be possible to describe a language or pattern for this system by a kind of fractal? Is there a network analog of a recursive function? Is it possible to describe the interplay of liveliness and safety properties in distributed algorithms as a strange attractor? Or would it be possible to distinguish between different forms of organization in coupled Turing Machines by using the concept of emergence?

Eberbach, Goldin and Wegner say that interaction with the environment is the key difference: “Interactive computation involves interaction with an external world, or the environment of the computation, during the computation — rather than before and after it, as in algorithmic computation.” (in “Turing’s ideas and models of computation”, 2003). The interesting thing about the Turing Machine is that it is as simple as possible, and yet can describe nearly all computations and languages we currently use. Therefore it comes close to a “Theory of Everything” in computer science: it describes everything with nearly nothing. Is it already a theory of everything in computer science? Or can we find fundamtental models for other forms of computation as well? Something which captures the essence of cellular automata, neural networks and multi-agent systems, similar to the Interaction Machines from Wegner, Eberbach and Golding. Copeland talks about Hypercomputation. Stepney calls it non-classical computation or non-standard computation. Robin Milner argues that a structural theory of interaction may burst the von Neumann’s bottleneck and says that “we must find an elementary model which does for interaction what Turing’s logical machines do for computation” (see “Turing, Computing and Communication“).

Then he compares “Old Computing”..
Finite Computation – Sequential – Prescription – Instruction

..with “New Computing”
Continuing Interaction – Parallel – Description – Recognition

Is he right? To find an elementary model it is useful to identify the basic abstractions. The basic abstractions of sequential computing are code and data, which are manipulated by read & write (or load & store) operations, the essential operations of Turing Machine and von Neumann architectures:

– data structures: number, set, record, array, list
– control structures: if-then-else, loops (for, while, repeat)
– operations: read & write (or load & store, destroy & create)

Other operations, for example the classic move operation, is a combination of read and write functions. Arithmetic operations (inc, dec, add, sub, etc.) can in principle be implemented by a suitable combination, too. If a high-level program has a valid syntax can be checked with a suitable context-free grammar for the programming language. High level abstractions for sequential computation are procedures and algorithms. According to Abelson and Sussman SCIP’s book, “a procedure is a pattern for the local evolution of a computational process” (“Structure and Interpretation of Computer Programs“, chapter 1.2) Sequential computation means following a precise computational recipe – a procedure, a program, an algorithm or other step-by-step description of what to do.

Interactive, real-time computation is different. It must be fast, which requires parallel or distributed architectures. The basic abstractions of distributed computing are processes and messages, which are manipulated by receive & send operations (or perceive & act), the essential operations of agents and networks.

– data structures: messages
– control structures: events, threats (for consumers and producers)
– operations: receive & send (or perceive & act)

Instead of perceive and act one can also say classify & signal, reduce & map, collect & emit, gather & update, or propose & decide. The repeated application of the first step, perception, classification and reduction leads to abstraction. The repeated application of the second step, action, signaling and mapping, leads to dissemination and broadcasting. The broadcast operation is a combination of send operations. In a hierarchy of layers, the effect of mapping, signaling, and notification can also be considered as the opposite of abstraction, which is implementation or instantiation.

Receive and send functions can also be combined to implement all kinds of reactive behavior and to achieve a broker, dispatcher or transmitter functionality. Certain perceptions are mapped to particular actions. An essential abstraction in distributed computing is consensus, when the processes try to make a consistent decision, and agree on a common data value. Special cases:

* synchrony: agreement on a certain time
* election: agreement on an elected leader
* atomic commit: agreement on the outcome of a transaction
* total order broadcast: agreement on a sequence of messages

Now, how can we check if a high-level distributed program has a valid syntax or correct behavior? It would be fascinating if it is possible to define a fractal pattern for coupled Turing Machines to describe the strands and braids of information spreading through the system. Or is it maybe possible to use liveness and safety properties to define suitable attractors of the system?

Golding and Wegner say that the classical view of computing views “computation as a closed-box transformation of inputs (rational numbers or finite strings) to output” (see also Dina Goldin and Peter Wegner: “The Interactive Nature of Computing – Refuting the Strong Church-Turing Thesis“). The new, interactive view of computing describes computation as an ongoing interactive process. In sequential computing, the syntax of the program is checked with a suitable context-free grammar to ensure the application does not halt or stop. If the syntax is violated, then an exception would be raised and the execution of the program would come to a full stop. An object is called once, and should deliver instantly an result to any function call. An agent is called continuously, because it is embedded in the environment through a perceive-reason-act cycle. It does not need to react to every request, but it should not stop working completely. For distributed computing, even if a single node crashes the rest of the nodes would continue work. Rather than to guarantee that a single node will never stop, it is important for an embedded system to ensure that the whole system will continue to work. The interaction must go on, and the flow of information from sensors to motors (or actuators) must not stop. This can be achieved by a suitable activation or modulation of the whole system. An agent or animal which comes in a situation where absolutely no action or output is generated would be forever trapped and probably die. Therefore it is necessary to guarantee always an optimal level of activity. In the brain, there are a number of diffuse modulatory neurotransmitter systems, which work with Dopamine, Noradrenaline, Serotonin, and Acetylcholine. They are used to modulate and regulate the activity for large populations of neurons. The brainstem and the thalamocortical system guarantee always an optimal level of activity.

Furthermore it is important to ensure consistency of the system: contradictory and inconsistent processes must be avoided, because they could lead to permanent deadlocks. One way to achieve consistency is to make cognitive dissonance unpleasant. Cognitive dissonance was first investigated in Psychology by Leon Festinger in 1957. It is an uncomfortable feeling caused by holding two contradictory or inconsistent ideas simultaneously. On the contrary, cognitive consonance is a comfortable feeling. There is a tendency for individuals to seek consistency among their perceptions, beliefs and opinions: they distort the uncomfortable perception so that it does make sense, for example by reducing the importance of the dissonant beliefs, by adding more consonant beliefs that outweigh the dissonant beliefs, or by changing the dissonant beliefs to make them consistent. Or they focus on the situation and resolve the incongruity by suddenly understanding it: this resolution of incongruity is the kernel of humor and laughter.

Suitable high-level abstractions for interactive computations can perhaps be found in the area of fluid dynamics: wave, flow, modulation, sink, source, channel or conduit. Maybe it is useful to compare the computation of a distributed system to a liquid: waves of activation can spread to the system, stress bubbles can prepare the system for action, flows of information can pass through certain channels from sinks to sources, and the degree of activation and modulation can be compared to the surface level of the liquid, etc.

Es ist nicht das Wissen, sondern das Lernen, nicht das Besitzen, sondern das Erwerben, nicht das Dasein, sondern das Hinkommen, was den größten Genuß gewährt. (It is not knowledge, but the act of learning, not possession but the act of getting there, which grants the greatest enjoyment.) – Carl Friedrich Gauss

According to the American physicist Richard P. Feynman, it is the pleasure of finding things out which makes science worthwhile. It is the reason we do science: the joy that comes from learning something that we don’t knew before – sometimes even something which nobody knew before. Feynman says in his book The Meaning of It All: Thoughts of a Citizen Scientist:

The next aspect of science is its contents, the things that have been found out. This is the yield. This is the gold. This is the excitement, the pay you get for all the disciplined thinking and hard work. The work is not done for the sake of an application. It is done for the excitement of what is found out. Perhaps most of you know this. But to those of you who do not know it, it is almost impossible for me to convey in a lecture this important aspect, this exciting part, the real reason for science.

The pleasure of finding things out is also the reason why children laugh more than grown-ups. They discover constantly new things, while grown-ups rarely encounter completely new things. And maybe it is the reason why humans are the most advanced of mammals. Tom Robbins said in his book “Still Life With Woodpecker” (1980)

“Humans are the most advanced of mammals — although a case could be made for the dolphins – because they seldom grow up. Behavioral traits such as curiosity about the world, flexibility of response, and playfulness are common to practically all young mammals but are usually rapidly lost with the onset of maturity in all but humans. Humanity has advanced, when it has advanced, not because it has been sober, responsible, and cautious, but because it has been playful, rebellious, and immature“

Our brains obviously have been designed to find learning fun, to be playful and curious. When you have an open mind and learn a new thing, your brain is rewarded for this successful adaptation to the environment by a release of dopamine. Dopamine is involved in the reward and motivation systems of the brain. Do only scientists have the pleasure of finding things out? No. They may think they are special, but they are not. Others don’t find new laws, but they find out different things, reasons and rules. Lawyers and advocates find out the reason what really happened, doctors find out the reason for the symptoms of the patient, etc.

Journalists, Lawyers, Thriller-Readers
* find out the reason what really happened: the initiator of the crime

Doctors:
* find out the reason for the symptoms: the disease of the patient

Politicians:
* find out new ways to organize society: the laws for the country

Managers:
* find out new ways to organize people: the rules for the organization

Executives:
* find out new ways to make money: the niche in the economy

Engineers:
* find out new ways to build s.th.: the blueprint for the machine

Scientists:
*  find out new ways to explain s.th.: the laws of nature

Measures of Tipping Points, Robustness, and Path Dependence
Aaron L Bramson
http://arxiv.org/abs/0811.0633

Concept and Definition of Complexity
Russell K. Standish
http://arxiv.org/abs/0805.0685

The Sigma Profile: A Tool to Study Organization and its Evolution at Multiple Scales
Carlos Gershenson
http://arxiv.org/abs/0809.0504

Complex and Adaptive Dynamical Systems: A Primer
Claudius Gros
http://arxiv.org/abs/0807.4838
also available as lecture notes

Exaggeration is one of the most commonly used techniques in satire and comedy: you take something to the extremes until it becomes unreal, distorted and ridiculous. Unreal thoughts are inhibited by common sense, and therefore a good source of incongruity. Another form of inhibition used in comedy are social taboos. Funny scenes are often a violation of common sense or social taboos.

Old songs such as “So Near And Yet So Far” or “All The Things You Are” may be a bit cheesy. Yet the feeling of craving, longing and yearning expressed in them is very human. The longing for love is indeed deeply human, especially if the object of desire is unreachable, just as the desire to find out what the point of living is. In German we have the word “Sehnsucht” for it. In WALL-E, the main character WALL-E (short for Waste Allocation Load Lifter-Earth Class) views old TV recordings with the help of an old VHS tape, although he is just a waste collection robot. Besides his tendency to collect things he finds interesting, it is this longing for love and companionship which makes him human. In the Star Trek films, it is also the longing for being human which makes the cyborg Commander Data more human than the humans themselves. Here are Fred Astaire and Rita Hayworth with So Near Yet So Far:

I’ve a feeling you are
So near and yet so far
You appear like a radiant star
First so near, then again so far

Robert Browning said “a man’s reach should exceed his grasp, or what’s a heaven for?”. It is very human to be expelled from the paradise, to recognize something which is nearly within reach and yet unreachable, to see something which is “so near and yet so far”. If a machine can understand this problem, and enjoy a song like this, then it probably has reached human-level intelligence. Here is Helen Forrest with “All the Things You Are“, a classic love song that has been sung later by Ella Fitzgerald and Barbra Streisand.

Time and again Ive longed for adventure
Something to make my heart beat much faster
What did I long for, I never really knew.
Finding your love, I found my adventure,
Touching your hand my heart beat much faster
All that I want in all of this world is you.

You are the promised kiss of springtime
That makes the lonely winter seem long
You are the breathless hush of evening
That trembles on the brink of a lovely song.
You are the angel glow that lights the star,
The dearest things I know are what you are.
Someday my happy arms will hold you,
And someday Ill know that moment divine
When all the things you are, are mine.