Nowadays, it’s widely accepted that DNA’s are long strings of coded data transmitted vertically (and recently horizontally) between species. However, it was a revolutionary idea in 1927 when Nikolai Koltsov wrote about a giant hereditary molecule that is composed of two mirror strands that would replicate in a semi-conservative fashion using each strand as a template.

Koltsov was not an unknown scientist. He was a prominent biologist in the Soviet Union. Koltsov has founded Institute of Experimental Biology 1917, ten years before his official and public statements about inheritance, evolution and genetics.

Despite the fact that he was known as a leading scientist and even trustee of the communist party, still it was not easy to stand in front of Lamarckian soft-inheritance. Lamarckian idea about inheritance of acquired characteristics was accepted by communist party as a scientific viewpoint aligned with their ideology. That’s a repetitive behavioral pattern of ideologies that they support scientific theories which support their ideas better than the others, and the most supportive theory is not necessarily the closest to the reality.

At that time, genetics was considered as a bourgeois science and of course, ideas of Koltsov as a leading genetic scientist was labeled as a fascist propaganda!

Although military pressures have had a great contribution to the advancement of the technology, intervention of political leaders in science has been destructive most of the times. Technology helps politics to enhance its tools, but science has not short-term or mid-term results, so it’s mostly judged against political values and ideologies.

The communist party, motivated by Nikolai Lysenko (a leading Lamarckian), condemned findings of Koltsov and considered him as a person promoting fascistic nonsense ideas and defending racist ideology.

Nikolai Koltsov, somehow father of the modern genetics and a leading pioneer of biotechnology, died suddenly and unexpectedly in 1940. His wife committed suicide on the same day and years later, it was revealed that he was poisoned by Soviet Union police.

Modern genetics is one of our most valuable tools in widening our view of the world and helps us to move toward trans-humans. Therefore, Koltsov research shall be considered as one the first steps in the ladder of human transcendence, however, it seems humans themselves, do not welcome these efforts.

The surprising fact is that Koltsov, a scientist that his findings were supporting the idea of evolution, was killed by a follower of Lamarck, the other scientist who has a significant contribution to the development of the evolution!

Reading history (at least the recent history that is well documented) shows that the science has been the victim of the scientists itself. Public opinion is flexible and adapts itself to it’s short term benefits, and people, are not able to fight against science without the support of the other scientists. The ones who have a strong emotional bond with the old theories or have a strong affection for money and are ready to sacrifice science for anyone who signs their paycheck.

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Firstly, let’s begin by describing the distinction between replicate and duplicate. Afterward, we will address the most perplexing aspect: the meanings of reproduction and the differences between reproduction and replication.

Replicate vs. Duplicate

“Replicate” and “duplicate” are very similar in meaning and, in most cases, can be used interchangeably. However, “duplication” carries the nuance of copying, while “replication” is closer to recreating the same thing with a separate identity.

For example, when you make a copy of your license or obtain a second one after losing the original, you then have a duplicate of it in your hand. Similarly, when you read an argument in a book and republish it on social media, you are replicating the original content.

Here are a few examples of using “duplicate” (copy) in sentences:

– How do you duplicate a key? (Make a copy of your key)
– This application takes up too much space because it duplicates all the songs on my hard disk and stores them again in its directory.
– With the ever-increasing volume of data, duplicate detection is an essential feature in dataset management systems.

However, when you read a study and attempt to recreate it precisely in the same way, you are replicating it. It’s termed as replication because it maintains a separate and independent identity despite using the same methods and procedures. You are either confirming or refuting the original study with your replication study. Therefore, in this context, the research is being replicated and not duplicated.

Reproduce vs. Replicate | Examples from the IT & Software World

You can guess the difference if you are working in the information technology and network infrastructure field. There are hardware devices called replicators, whose function is to receive a data packet and deliver it elsewhere. The receiver collects packets in such a way that there’s virtually no way to spot the difference between the primary source and the replicator. Replicators, also available as software, are used for long-range data transmission.

Now let’s talk about reproduction. Again in the same context: software and information technology. You always hear from software developers that they are trying to reproduce a bug. What do they mean by that?

They mean that the user has faced a problem while working with the software. And although they don’t know the reason behind the problem, they play with the code and the interface, hoping that the problem will happen again and they can analyze and solve it.

So when we are talking about reproducing a bug, we are not going to make exactly the same problem again. There can be minor differences, and reproduction would still help detect the bug’s source.

Reproduction, in this sense, means the process of creating a very similar but not exact copy of something.

Having the above example in mind, now you understand why we say humans and animals can reproduce themselves. We don’t use the term replicate, as the children are not precise copies of their parents.

Let me restate the difference between replication and reproduction in other words:

In replication, outputs are exactly the same as the inputs but in reproduction, outputs are just similar to the inputs.

Here you can see a few examples of the word reproduce (process of building a similar copy) in different sentences:

• The concert will be reproduced on compact disc (It’s not the concert, but it’s a concert anyway)
• Let me reproduce that scene in my mind (It wouldn’t be the same scene, but very close to reality)
• Skin cells reproduce too quickly (Make new cells very similar to themselves)

Reproduction and Replication in Biology

Just imagine if every single cell was replicating itself or if all of the species were replicating themselves. There was not any kind of evolution on the planet: No change, No improvement, No growth, No chance for the emergence of the new species, No life!

As I have emphasized in the definition of the living entities:

But is the story as simple as it seems? Unfortunately not!

In the core of every living cell, RNAs just replicate themselves:

If there was not any minor error or mutation in replication at the lower levels, there was no chance for the emergence of reproduction in the higher levels. RNAs and cells are genderless. The concept of the gender just appears in the higher and more complex levels of the hierarchy as a trick to get more distance from replication and get closer to the reproduction.

Considering reproduction vs replication we can say every living entity has to find some way to get closer to the reproduction concept.

This is one of the reasons I am so optimistic about the social networks as a trick for making the cyberspace more alive. Comparing them with the printed content and even digital contents in the websites, social networks are more prone to human and technical errors in quotation and replication, which can move the digital world one step further from the simple mechanical replication process.

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In my approach to the living entities and the articles about living things and cellular organization and predictability and the living things, complexity was considered as the most important aspect of a living entity usually associated with existence of some kind of living spirit by primitive cultures.

My introduction to the concept of emergence and coining of the term inferiomorphism has some roots in my mindset based on the complexity of the systems.

So it might be a good time to bring up the complexity concept seriously and discuss it as clear as possible. I would start with the definition of a complex system and will elaborate on the technical terms in future articles.

Sure my definition, is not the most comprehensive or accurate one. But would be sufficient for a preliminary introduction. In future, we will study each of the following terms in more details.

So let me introduce a very simplified and basic definition of a complex system:

A complex system is a collection (or population) of many different entities with the specific properties and basic behaviors interacting with each other as well as the environment. When the population of the entities grows enough, there would be hard for the observer to predict the behavior of the whole system, even assuming that we know all the basic properties and behavioral rules of the entities. At this stage we have no pre-assumption about autonomy of the entities as we will see that the autonomy is a label for the complex systems beyond our full understanding.

Having the above-mentioned definition in mind, every society can be considered as a complex system made of humans.

Every human can be considered as a complex system made of organs.

Every organ can be considered as a complex system made of cells.

Every solid or liquid material can be considered as a complex system made of molecules and atoms.

In other words, with this approach, biology is nothing more than the behavioral study of a complex chemical system.

In the same way, psychology is nothing more than the behavioral study of a complex biological system.

And sociology is nothing more than the behavioral study of a complex psychological system.

The same framework can be applied to the entities with higher or lower levels of complexity. This is the reason that the complexity theory, accepting our ontological and epistemological limitations, can be considered as a theory of everything.

You can consider the complex systems somewhere in the middle of the chaotic systems and the mechanical ones. In a chaotic system, any small disturbance can lead to a major disruption and the system can not be considered as stable or even as an integrated entity. In a mechanical system, on the other hand, everything is predictable and response of the system to the external stimuli can be predicted with an acceptable precision.

Most of the words I used in the last sentences are inaccurate ones. So in the future articles we will try to define them more clearly. It may take some time, but it’s worth the effort.

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I have coined the term inferiomorphism by myself to emphasize on one of the greatest mistakes we’ve ever made.

When I was talking about the practical definition of emergence, I’ve noted that the reductionist approach to the world is absolutely against the emergence concept.

Here’s the simplest definition of the reductionist approach:

You can divide the whole into the parts and study them, without losing anything important.

Let me rephrase the above definition:

You can sum up the parts and make a whole new entity, without emerging any new characteristics or properties.

You may think of a brick wall as the first example. We put bricks together and build a wall. Are there any new characteristics emerged? You may say yes. Now we have a wall but we had nothing more than a pile of bricks before. Someone else may say we have nothing new. There is still a pile of bricks with a new structure.

Such a discussion will lead nowhere as it’s a debate over the meaning of the words.

But let’s think about another example which dates back to one century ago when Boltzmann and his colleagues were working on the atomic theory and statistical mechanics. The atomic theory and his mindset was too early for his time. So he depreciated himself through long and strong debates with the contemporary philosophers and the governing paradigm. He finally hanged himself as a result of a depression attack  on September 5, 1906. But now we know that his valuable studies can be a vivid example of the emergence concept.

Just imagine the pressure or temperature of a gas. Can you define the temperature or pressure for every single molecule? Sure not. We can’t imagine the concept of pressure and temperature for a single molecule. These characteristics just emerge by the interaction of a large group of molecules with each other.

Pressure and temperature have a fundamental difference with the other physical concepts like mass. As the total mass of a gas is the sum of the masses of its molecules. But pressure and temperature are not sums of the atom properties. Pressure and temperature emerge from the interactions of a large group of atoms and molecules.

From now on, I will frequently use the following terms: agent and superagent.

A superagent is a new entity emerged from the interaction of many similar agents with each other.

So, molecules are agents and the gas is a superagent. Organs are the agent and the humans are superagents. Humans are agents and the society is a superagent. All the firms and government and families are agents and the whole economic system is a superagent.

So the agent and the superagent concepts are completely relative. Every single agent can be a superagent for the lower levels of the hierarchy and vice versa.

The one who has the emergence mindset and a holistic approach to the world, would be able to look for the new characteristics of every superagent and characteristics of the agents in the lower levels of the hierarchy, independently.

Just think about your society and try to describe it in few sentences. Would you call it outgoing? or emotional? or rule conscious?

As you see all of the above-mentioned characteristics are based on our understanding of the humans as agents not the society as a superagent. These descriptions are more similar to the concept of mass, not the concept of temperature.

This is the mistake I would like to call inferiomorphism. Thinking about the superagent with the concepts which are just applicable to the agents.

You can see the result of this misleading mindset in various fields such as religion, science, technology, and philosophy of the digital age.

In the future articles, we will think about the approaches which try to go beyond this limiting mindset. The most complicated challenge would be trying to understand the superagent when we ourselves are the agents.

The post Basic Concepts: Inferiomorphism – when agents impose themselves to the superagent appeared first on webmindset.

Now it’s the time to talk about the concept of emergence with more examples and look for a practical definition of it.

Imitating Michael Porter’s style, let me start with this question: What Emergence Is Not? Or let’s say in other words: how would we see the world if we don’t use the emergence approach:

Centralized approach to the systems

The centralized approach to the world has been popular since centuries ago:

• The whole world has to have a center and sure it was earth.
• The whole human body has to have a commanding center. In the old times, the heart was supposed to be this center and then the brain took this central position.
•  The whole society has a command center. It may be a king in the autocratic societies and the parliament in the democratic ones. But there has to be a center.

The centralized approach has been the governing paradigm for a long time and one of the hardest challenges in understanding of emergence is unlearning this old-fashioned concept.

Reductionistic approach to the systems

Every system can be divided into smaller parts and every part can be studied separately in order to understand the system as a whole.

• To know a watch, break it into the parts.
• To know an organization, take a look at its chart and visit every department.
• To know the human body, try to know the organs and the cells.

Peter Senge states in his highly recommended book The Fifth Discipline, by dividing a cow in two parts you would not have two calves. What lies there would be nothing more than a dead cow. The same metaphor holds true for all the systems. The system as a whole is more than the sum of its parts and this fact is not acknowledged by the reductionist approach.

I have coined the term inferiomorphism to describe misleading results of this mindset.

The objective assumption of the subjective system characteristics

The mass of a brick seems an objective measure. Also the height of a human.  Also the RPM of an engine. But what’s your opinion about being alive? or being autonomous? Can I talk about a living state and a non-living state as clear as the solid/liquid state? Is it correct to consider it as a measurable boolean parameter? Can I talk about the meaning of living independent of the observer? Can I define these characteristics based on the characteristics of the parts?

Sure there are objective characteristics in any system and there are subjective ones too. But the people who are not equipped with the emergence approach are in danger of assuming the subjective characteristics as objective ones. I have mentioned some simple examples about this point in my article titled predictability and the meaning of living. But sure we will talk about it with more details in future.

Understanding the emergence concept will lead us to a new world. The world in which most of the currently accepted facts will not be anything more than misleading assumptions.

We will talk about this concept in future articles.

The post Basic Concepts: Looking for a practical definition of emergence appeared first on webmindset.

The concept of randomness seems obvious at first glance. Random and Randomness are so popular in our daily talks which we never think about their exact definition.

Let me start with a simple question. Which of the following series of the numbers can be called random:

1  0  0  1  0  0  1  0  0  1  0  0 1  0  0  1  0  0  1  0  0  1  0  0 1  0  0  1  0  0  1  0  0  1  0  0

1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1

1  0  1  1  1  1  0  1  1  0  1  0  0  0  0  1  0  1  1  1  0  1  1  0  0  0  1  0  1  0  0  0  1  1  0

Most of us consider the third-row as a random series of bits. The second-row doesn’t seem a random serie and the first-row is something in the middle. Not as random as the third-row and not as ordered as the second row. But sure it has a pattern inside. It’s just a ‘1  0  0’ repeated 12 times.

Now let me tell you that all these 3 rows are the result of consecutive tossing a coin. Anyone familiar with the basics of probability knows that if you toss a coin for 36 times all of the above series have exactly the same chance of appearance. Actually the chance is 0.000000000014551915. So they all can be called a random series of numbers somehow.

In theory, it’s understandable but we all feel that there’s a difference between these 3 series. The first and the second series have some pattern, but the third one seems absolutely patternless.

Here we can define two different concepts of randomness: Product Randomness and Process Randomness. As far as I know, this distinction between the product randomness and the process randomness was done by Gregory Chaitin. Although he didn’t use the same names, but sure he distinguishes the two different approaches to the randomness.

So let’s make a brief definition of the both terms:

Product Randomness is an attribute of a series of events with no visible pattern.  So any patternless series of events can be called as a sample of product randomness.

Process Randomness is an attribute of a series of events resulting from a process with two or more products which all have the same probability of happening.

So considering my question at the start of this article, all three rows can be called random if we consider the process definition of randomness, but only the third one can be called random if we consider the product definition of randomness.

Gregory Chaitin has an amazing definition of product randomness: Any series of numbers can be considered as random if there is no shorter way to communicate them with someone else (or with a computer) than copying the whole series of numbers itself.

So in his point of view the second row is not a random row as you can describe it for a computer in this way:

Print ‘1’ for 36 times

Even the first raw is not random because you can write:

Print ‘1  0  0’ for 12 times

But the third one can be considered random as there’s no way to compress it to a shorter message.

But what if our computer understands other commands too? Let’s suppose that our computer understands DecToBin command for converting decimal numbers to binaries.

then all of the three rows are not random anymore (or they are at the equal randomness level):

First row: DecToBin(39268272420)

Second row: DecToBin(68719476735)

Third row: DecToBin(25451802950)

Here Chaitin has a very simple answer: as far you can make it shorter, just do it. when it’s not absolutely possible then you have a random series in hand!

So this is an understandable yet non-measurable definition of product randomness. Anyway, he is a mathematician and for the mathematicians the most important concern is to prove that there exists an answer. To know the actual answer is not the first priority.

But in the real world we need an exactly quantifiable definition. The whole existing world is a series of events. So considering it as a sample of process randomness means that this nice pattern-full world has the same value as any other pattern-less world which could happen.  On the other hand considering the whole world as a result of a process randomness has its own implications and complications.

The most serious challenge in understanding the world is limitation of our brain as it is hardwired to look for pattern and meaning even in pattern-less and meaningless things.

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In the above photo, you can see various structures made by termites. Actually these are the cities they live in. Every single colony can have millions of members. Considering the structure, scientists believe that the complexity level of these colonies is close to a mid-size city.

But before talking more about termites, would be better to make a definition of the term eusocial insects and eusociality.

Any insect species that are living together can be called social insects. But the term eusociality (originally coined by Suzanne Batra) means the most complicated social system among insects recognised by three basic characteristics:

• Conducting cooperative care of young. Even when are not directly their offspring.
•  A clear division of labor
• Overlapping of generations, helping the experience to be transferred from the olds to the youngs.

There are various eusocial insect species which ants, bees and termites are most studied ones. But termites are more interesting as some of them build their cities not under the ground but over the ground just like our skyscrapers.

Living for about 300 million years on the earth, although they have been breakfast of dinosaurs, their robust society structure gave them the chance to see their extinction and even chances are high that they see extinction of human species. Currently, the total weight of the termites is more than the total weight of the humans on the planet. Every single termite lives for several years. But termite colony lives for even more than one century.

So if we consider survival as the most important desire of all the species (including humans) seems that termites have been more intelligent to find a practical solution for this quest.

Still considering an individual termite, it has one of the simplest minds ever existed on earth. But considering the termite society, there’s an emerged mind more complicated and more productive than human mind. No centralized government. Not any sign of democracy. Not any means of complicated communication tools or message broadcasting services. But making the best decisions:

• Where to build the city?
• When to leave the city?
• How to design the structure of the streets and homes?
• When help the others and when leave them to die in favor of the society?

Yes. We can be proud that we are more conscious than termites. But, to be honest, there’s no proof for that. As the ability to speak is not a proof of conscientiousness. and tool making is not a proof of intelligence if it can not help our society to live longer. Sure if aliens with no human bias come to the earth and observe us for a long period of time they will vote for termites as a more powerful and adaptable entity.

How does it happen? With a very clear and simple structure. They have three different categories with different duties in the society called castes:

• Soldier termites (usually blind and without a developed eye) with strength and armor prepared to defend society against ant attack. They are not even able to feed themselves. They are fed by worker ants through their mouth or anus.
• Worker termites responsible for food storage, nest maintenance and digestion of cellulose for the others.
• Reproductive termites specialized in egg laying. A mature queen may produce 20000 to 30000 eggs a day.

Termite society is the best demonstration of the emergence concept. Simple minded individuals (we call them agents) do a very simple duty (we call it task or interaction) for the society (we call it a system) and the new characteristics emerge. The ones who has no similarity to the basic characteristics of agents.

The most complicated emergence case we ever recognized is the human brain. Individual neurons with simple characteristics combined together and a complicated system emerged which we call it brain.

To be more precise, a complicated system is emerged and called itself brain. As the brain is the only existing entity who named itself.

In future articles, I will try to show you that the brains are not the most complicated emergent system anymore. We have more complicated living digital entities which are not recognised as most intelligent and adaptable living entities of the world yet!

The post Introductory Case: Life of termites as eusocial insects appeared first on webmindset.

Yes, we have the ability to talk. We are able to communicate with words. We are one of the few species who are able to transfer what they have learned to the next generation via something beyond genes and genetics. We are one of the most powerful and creative tool-maker animals ever existed. But does it mean that we are the most successful inhabitant on the planet earth?

To find the most successful species, it’s necessary to define success. May we could call ourselves one of the most complex ones, but complexity is not an advantage on its own.

Survival can be considered as one of the greatest success criteria. The whole concept of complexity is grown around survival. Even if we don’t know the question behind the creation, the answer is crystal clear: We leave the playground in favor of more professional players!

Would not be hard to guess that humans are not necessarily the last surviving entity on the earth. There are many other species with more chance. Ants are one of them.

A history of living for more than 300 million years on the earth and diversification into more than 10000 species are just two indicators showing their success on the planet. Considering they are more resistant than humans on bearing hostile and harsh environmental conditions, it can make their survival possible even after our extinction.

From the ant’s point of view with more than 300 million years of life, appearance and extinction of humans within hundred thousands of years is not anything longer than a thunder sound: loud and frightening but short!

 What have helped them to be one the most successful species as we know they have one of the simplest brains ever known to us? Something we can call the social brain.

Ants and termites and similar insects are eusocial. They live together and despite the fact that every single ant has a very simple mind, the ant society has a complicated adaptive social mind. Every ant works like a neuron and every communication between ants works like a synaptic connection.

Here lies the main concept behind emergence:

A process whereby larger entities, patterns, and regularities arise through interactions among smaller or simpler entities that themselves do not exhibit that properties.

A single ant is not as successful as an individual human. But the ant society is clearly more successful and more adaptable than the human society with a much brighter future on the planet.

The concept of emergence has a tight connection to the concept of living and predictability. Generally, most of the emerged behaviors of the systems are more complex and less predictable than the behavior of their individual components. And here’s the starting point of the delusion. where we started to put tags on the emergent characteristics which we could not analyze. Living, spirit and intuition, to name a few.

Before any further discussion, would be helpful if you take a look to the following articles:

Life of termites as eusocial insects

predictability and the living things.

The post Basic Concepts: An introduction to the concept of emergence appeared first on webmindset.

After you play too much with the spring something extraordinary happens. spring moves away from you with a worm-like movement. The spring pushes himself forward by stretching and contracting its body and hides somewhere behind a book.

Seems it was not a real spring. It was a spring-like living creature! This is a simplified statement of my idea behind the concept of predictability and connection of this concept with being alive.

Taking a look at the literature on mind and free will, you see the most examples and metaphors about predictability  revolves around predictability of physical movements. Same holds true for mine. But soon we will extend it to the other aspects of predictability.

When an object becomes absolutely predictable for us, it’s not considered as a living entity anymore. Physical movements of water are predictable. So it’s not considered a living thing. Movements of a fish are not predictable. So it’s kind of living entity!

The idea of predictability of behavior in living things is easy to grasp yet hard to define. As Dale states four decades ago:

An important feature of the response made to stimuli by living things is that in general the magnitude of the response has no obvious relation to the intensity of the stimulus.

Taking a look at the general literature, seems that without being seriously criticized, we can consider unpredictability and randomness as similar or same concepts. As Patrick Suppes states in his book called Probabilistic Metaphysics:

Phenomena that we cannot predict must be judged random.

In our daily talks, such a definition seems a bit radical. We are not able to predict stock market movement, however, no-one would be happy if you call the market movement as a random walk. Malkiel‘s book Random Walk Down Wall Street, caught the attention of the great stock market players and individual investors, but investors are still asking expensive consultants to help them with selecting an above average portfolio based on past performance of them.

The same story holds true for destiny of a marriage or result of a surgery and many other life incidents.

But taking a second look at the randomness definition shows that we have missed the context of the statement.

Are we trying to predict a single behavior or the pattern of behavior over time? If we flip a coin for 1000 times would be easy to predict that it will land head-side-up for about 500 times. But can you have any prediction with the same certainty if I have just one chance to toss the coin? Sure not.

Therefore, we must clearly define our scope of the study and boundary of the system before making any judgment about predictability.

So, let me state an oversimplified definition of unpredictability at this stage:

An entity can be considered unpredictable if giving the same history of states and unlimited time to experiment, still be impossible to predict the response to the same stimulus  within a predefined tolerance.

This is the place where the real story begins. There are some obvious and many hidden assumptions in the above definition. Not only about scope and the boundary, but also about observer and stimuli. Back to Wittgenstein’s ruler metaphor mentioned before, here’s the challenge. We are unreliable rulers trying to measure the table. But let’s try to do so.

I have continued this topic with a brief look at the concept of randomness.

The post Basic Concepts: Predictability and the living things appeared first on webmindset.

We know many characteristics of living things. Are all of them necessary condition for an object be considered as a living entity?

(I use the verb consider intentionally. Just to emphasize on the subjectivity of this concept).

Before going any further, let me remind you of biological organisation. It’s always supposed that biological hierarchy starts from atoms and macromolecules and continues with cells, tissues, organs and organisms.

It’s crystal clear that all the entities we consider now as “Living Entities” are composed of biological cells. I mean amoebas, plants, animals and humans have one thing in common: they are all a simple or  a complex structure made of biological cells.

But is it really a necessary condition for a living entity to be made of biological cells? I don’t think so.

It’s just for about four centuries which we are familiar with cell concept. Just before Robert Hooke and his well-known microscope, we have had no idea about cells or cell theory. But it’s for thousands of years which we are able to recognise between dead than alive conditions. Even plants were considered alive since centuries ago before we know anything about cells.

Sure advances of technology and new inventions, helped us to see many other living things we were not able to see or analyze them before. But seems that correlating concept of “biological cells” and “living entities” is kind of overgeneralization.

So I mean, if we are going to define the living state, we can forget parts of our present knowledge and go back to years before Hooke. Even if I do not know that whether your body is built of cells or not, still I can firmly differentiate between you and your sculpture!

Is there any factor which we can consider as a principal and necessary condition for any object to be considered alive?

If you ask my opinion, I would say:

Yes! There is! This is the good part of the news.

But the dividing line is a bit blurred! This is the bad part of the news.

From my point of view, unpredictability seems to be more profound characteristic of living entities. But how can we distinguish between predictable and unpredictable entities? Is it a yes-no question? Sure not.

In the next articles, we will touch concepts of emergence, complexity and chaos. Just as an effort to have some understanding of the concept of predictability.

You can read the rest of the story in my article titled predictability and the living things.

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