Where Does Life Come From?

Where does life come from? As yet, no one knows, and it’s not likely they will know. The problem lies in the way the human brain works.

“How can a universe of mindless matter produce beings with intrinsic ends, self replicating capabilities, and ‘coded chemistry’?….Living matter possesses an inherent goal or end-centered organization that is nowhere present in the matter that preceded it. “

We’re not talking simply about the workings of the brain, but of life itself. One of the problems with understanding is that we don’t communicate directly between brain and conscious mind. The brain is a “make aware” agent, and the body continues massive communication levels from body to brain on a level simply incomprehensible by the conscious mind, which must process it all symbolically.

For example, the brain registers a thought somehow to the conscious mind, which symbolically then says to itself “I’m hungry” or “I’m sleepy”, or “I’m horny”. The brain can act on the awareness by getting something to eat, etc, but it has merely reacted to inner cues from the body.

An interesting point is that the brain forms symbols to represent inner reality, which are then cues that the brain can recognize over time and react. Symbol processing, however, is not just an action of the brain, but of the body, all the way from genes to the brain and conscious awareness.

One of the signs of life, that there is life, says Richard Cameron, is teleology:

“…that is, it will possess intrinsic ends, goals, or purposes….teleology is essential to the life of living things”.

If genes replicate that is their “meaning”, which means they will exclude everything that is not in accordance with their meaning. if I, at a much higher level, make a claim, it is “meaningful” only if I exclude certain things. If I claim the earth is a globe, that excludes the possibility that it is flat. Each meaning I select excludes other possibilities. For the gene, the “meaning” of its existence is replication, and it will therefore exclude that which violates the meaning of replication.

Let’s look at the “meaning” of a cell. The cells’ fundamentally different structures of information(DNA. RNA) are coordinated by the genetic code. Mathematician David Berlinski points out that:

“By itself, a code is familiar enough, as arbitrary mapping or a system of linkages between two discrete combinatorial objects. The Morse code, to take a familiar example, coordinates dashes and dots with letters of the alphabet. To note that codes are arbitrary is to note the distinction between a code and a purely physical connection between two objects. To note that codes embody mappings is to embed the concept of a code in mathematical language. To note that codes reflect a linkage of some sort is to return is to return the concept of a code to its human users.”

A code is an arbitrary process of linkage or communicaton, but that arbitrary connection excludes some things in favor of others. A code transmits “meaning” if the receiver can read the code.

Carl Woese a leader in origin-of-life studies, writes:

“The coding, mechanistic, and evolutionary facets of the problem now become separate issues.”

Of all we can understand about mechanics and evolution, we have no idea how the coding practice of gene replication came to be, nor how the “meaning” is read by the receptor cell. It is a purpose without purpose.

Paul Davies, however, adds, as stated by Professor Flew:

“…life is more than just complex chemical reactions. The cell is also an information storing, processing and replicating system….a gene is nothing but a set of coded instructions with a precise recipe for manufacturing proteins….they constitute semantic information. In other words, they have a specific meaning. These instructions can be effective only in a molecular environment capable of interpreting the meaning of the genetic code…The problem of how meaningful or semantic information can emerge spontaneously from a collection of mindless molecules subject to blind and purposeless forces presents a deep conceptual challenge”.

Nevertheless, the cell, “computes”, at incredible complexity, all the factors involved in replication , operating from the specific instructions of the gene code within its nucleus. I will explore this operation later, but for now what we’re beginning to see is a process by which life functions according to communication, input/output, and feedback, leading us back to the negative feedback ideas of Norbert Wiener. Storage, replication, and transmission, all acts of computation.

Eschel ben-Jacob, an Israeli researcher into epigenetics and communication among cells, points out that bacteria operate as a “learning machine”, and have five techniques to perform their necessary tasks:

(From Bloom’s Global Brain):

1.Bacterial colonies utilize a conformity enforcer, which, among other things, imposes a common language. Every member of the community then responds to the same chemical signals.
2.Bacillus colonies are riddled with diversity generators. Bacterial clones can morph into a host of variations if necessary. This produces a process in which thousands of diverse bacteria explore territory for food, and if there are groups which fail, the bacteria have
3. Inner Judges, which rule that the particular variation has failed, and dies. This actually provides a statistical model in bacteria acting like a computer to bring life forward successively at the bacterial level
4.Resource shifters, which strip “stranded” bacteria of food and all necessary means of survival, and that is then passed on to the successful variants within the pool of bacteria.
5.Intergroup tournaments, in which two bacterial systems will gab its own territory and wage chemical warfare on the other. These techniques add to speed up collective evolution within the successful groups.

This is all part of complex strategies that simply seek equilibrium between genes, cells, and bacteria, building up to higher levels. None of this, however, is part of our brain’s communication levels, except when the bacteria makes us sick and make us aware that something isn’t right.

At the lower level, as I wrote earlier, viruses act to exchange DNA, which is part of the coded system that forces change on the cells, bacteria , and humans, by exchanging DNA and turning it into RNA for replication within the organism.

The system “reads” the input, converts it to “meaningful” codes of the cell’s DNA, and then begins replicating “mutant” DNA,(output) to which the organism is forced to adapt. At the lowest level this is part of coded symbols. At the brain’s level, it is part of symbols we must know and recognize for further growth and organization.

The most interesting aspect of this “learning machine” that acts collectively, is described by Charles Darwin:

“…a selfish and contentious people will not cohere , and without coherence nothing can be effected. A tribe rich in the above qualities[reason…and foresight…the habit of aiding his fellows…the habit of performing benevolent actions,…social virtues…and social instincts] would spread and be victorious over other tribes. Thus the social moral qualities would tend slowly to advance and be diffused throughout the world”.

This, however, takes us back to the ‘machine-like response in the face of danger” described by Slater, and the “renouncing of the self” described by Hoffer, which is necesary for such linkages of “social” action at the cost of other societies. Bloom writes in Global Brain:

“When groups struggle, the one which boast the most effective organization, strategy, and weapons, wins. Individuals who contribute to their group’s virtuosity will be part of the team which survives.”

What both Bloom and Darwin have described leads to collectivist strategies in which individual sacrifice is the dominant “social” factor. This can be compared to Marxism, communism, socialism, or fascism. If Darwin and Bloom are correct, then socialism, communism, and fascism are the natural tendency when bodies begin to cohere socially. They take on aspects of collective behavior that increasingly resemble the above strategies as power centralizes.

Or as Slater put it, those who were more machine-like triumphed over those less so, and caused a powerful evolutionary tendency in social organization.

But Bloom further adds that such societies also act as learning machines:

“…semi-independent modules which work together to solve a problem. Some complex adaptive systems, like rain forests, are biological. Others like human economies, are social. And the ones computer scientists work with are usually electronic. Neural networks and immune systems are particularly good examples.”

In referring to “human economies” above, Bloom didn’t mention that the “social” functions of those economies would be both “mechanical” and social. That is, they would tend to operate more and more as a machine than as a biological system, as we see in our “mass democracy” systems of today, with communication and decision-making abilities ignored in favor of collective imitative behaviors, like mirror neurons.

This cooperative learning follows an “algorithm” writes Bloom, best expressed by Jesus: “To he who hath it shall be given; from he who hath not even what he hath shall be taken away”.

If this is so, then the biological ‘learning machine” , the “superorganism” will have a natural tendency to make the rich richer, and the poor, poorer. All such systems that propose sacrifice, collective strategies, ideologies requiring “renouncing the self”, will always tend to follow a process in which the few become richer and the many become poorer. The reason for this, as described by both Bloom and Darwin, is that nature will select for those survival strategies that work best for the replication and reproduction of genes. The simple algorithm: that which works, will reproduce.

And that which works, by the above strategies, will not care about individual fate or fortune in the matter.

“From whence come wars and fightings among you? Come they not hence, even of your lusts that war in your members?” (James 4:1)

To a learning machine, individuals are merely part of a statistical analysis of what works best for the group, or the “hive mind”, and while the hive mind can arrive at amazingly good solutions for finite problems, it has nothing to say about choices of individuals for individuals. Alvin Toffler, in the book, Powershift, points out that emerging systems of the new “information age” will tend to use statistical analysis and manipulation to maintain their power, because that is the nature of the “superorganism”. It is a learning machine that seeks to maintain power by whatever means possible. Since its survival is dependent on statistical analysis of what “works”, it will use the symbolism of statistical analysis to “prove’ the “truth” of its words. As a judge told me recently, “Your rights come second to the compelling interest of the state(superorganism).”

For the biological “learning machine”, it seems self evident to follow the route of the “greatest good for the greatest number”, yet as we see from conclusions above, that very tendency, superimposed as a symbol system over the pre-existing process, will tend to grant the “greatest good for the fewest in number”. The reason is that the “learning machine” becomes subject to the symbol system of its participants, evolve “inner circuitry” that responds, not to the environment, as biology once dictated, but to the symbol system of the group apart from the environment.