Transforming from Human to Artificial Intelligence

Everything is a repetition of its smallest part.

SUMMARY

In this study, the birth of the 3rd wave artificial intelligence algorithm, which will be equipped with decision-making abilities with humanoid feelings that exhibit learning behavior like the human brain, is described. In the study, with the philosophy of "Everything is the repetition of the smallest part", the most primitive and smallest known structure of the brain and the cells that enable us to learn is explained; The historical development of intelligence, wisdom and cognition processes are mentioned, and the development processes of artificial intelligence and its stages are exemplified. The numerical brain structure, which can learn with small data on its own by teaching only the rules,  and which can perform learning by matching with images, sounds or similar sensors, and the birth of the algorithm that creates this structure is explained.

Keywords: Artificial intelligence, algorithm, brain, learning.

1. INTRODUCTION

The decision-maker, by its very nature, chooses what is comfortable and simple. If the decision-maker chooses the difficult and the risky, he is in the determination and determination of the effort to be himself. In this way, it can be said that there is nothing but change, uncertainties, problems, and risk confusion. Risk is strategic in meaning. When the decision-maker takes the risk, the decisions made, and the consequences of those decisions can evolve into advantages or disadvantages.  However, it depends on the fact that the risks taken or to be taken by the decision maker in the decision-making process can lead to new advantages, and that the environments in which the decision is made change in a situation that is open to change and can be constantly renewed.

As a result of the changes in hardware with the advancement of technology, the first was manual machines, energy-consuming motor machines with the presence of electricity and automatic machines that followed them. In general, these automatic machines, which perform a single function or a specific function, have been developed into machines that perform multiple different functions with the technological changes in recent years and it has been tried to add intelligence to these machines and these studies are continuing.  (2) (Aysever, 2001)

In parallel with the change in machines, there have been developments in software systems. While the first software systems performed simple arithmetic operations, the resulting data was stored and transferred. Office automation software has been developed from developing software to facilitate our fast and complex life with developing technology. With the data obtained from office automations, management information systems and decision support systems have been developed. Likewise, this can be given as an example of intelligent, unmanned military vehicles in the defense industry, such as intelligent robot systems in factories. When it comes to the game world, we encounter universes with numerical twin structures and virtual, intelligent players who play games on their own in these universes.

Looking at the current approaches in today's machines and software, after manual control, human-controlled machines, traditional machines, and then automated machines have followed. Today, with automation systems consisting of automatic machines, many conveniences are provided in enterprises, factories or in our daily lives. All these systems work on specific software and perform specific jobs.  (5)(Euphrates and Euphrates, 2017)

These changes in technology have brought about social transformation. *0)(Yalçınkaya, 2010). The agricultural society that processes the land has been ruled by the agricultural lord, the mechanized industrial society has been ruled by the bosses, and with the development of information, the digital society has been managed by digital investors. Today, with the awareness and hunger for knowledge, and innovation, an innovative society is created and it is questioned who will manage this society (7)(Ozdemir, 2014)

With the rapid developments in information and communication technologies, systems that know the future by predicting the future will develop, the developing and changing social organizational structure will changes with it, and the smart organization of the future is moving towards the smart organization. (3) (From the beginning, 2003) Professor Stephan Hawking's warning that artificial intelligence could bring about the end of humanity raises different questions in minds.  (6)(Köroglu, 2017)

2. ARTIFICIAL INTELLIGENCE

We can define artificial intelligence as a branch of science that develops algorithms that try to bring certain features of human intelligence to the computer. It is aimed to develop artificial intelligence systems that can produce solutions to problems by exhibiting intelligent behaviors. According to scientist John McCarthy, who first coined the term artificial intelligence, artificial intelligence; is the branch of science and engineering that intelligence computer programs.

Edward Fredkin (Computer Scientist at MIT) says in a BBC interview: "There have been three important events in the past: The first is the formation of the universe, the second is the formation of the beginning of life, and the third is the emergence of artificial intelligence." (1)(Act. Acar, 2007)

To understand artificial intelligence, one must first understand the similarities and differences between the computer and the human brain: 

The human brain has an average mass of one and a half kilograms and has the ability to memory, process and reprogram 500 to 800 units of data per second in a life span of 65 years. That's about 3,600 bits of information per minute, 2,160,000 per hour, and 51,840,000 bits per day. 

As a scientist who researches on the brain, Dr. V.  When Grey Walter's work is examined, it is concluded that "It takes more than 300 trillion dollars to build a computer or machine that looks like a human brain."  He says that in today's technology, more than 1 trillion watts of electrical energy would be needed for a machine that works in this way. 

Throughout our lives, we receive information with electrical signals from all our sensory organs, send information and store this information. In addition, there is no consensus on how much of the concept of intelligence can be measured and what it means. As the common idea of the expressions used, we can define intelligence as the brain's ability to receive information and analyze it correctly and quickly. Since it is an open-ended and abstract expression such as consciousness, soul and subconscious, a general expression of intelligence could not be made. 

The concept of intelligence, on the other hand, is measured by the ability to create cause and effect relationships, understand information, process information, and derive information from knowledge.  Your intelligent behavior; learning, monitoring, problem-solving, reasoning, planning, making decisions, controlling, and diagnosing.

2.1 The formation of the universe 

Hesiod's 'Theogonia', which has an important place in the Greek philosophy of thought, gives clues to the formation of the universe. The most important thing is Khaos, one of the phenomena that make up the universe, according to Hesiod, "Khaos is the head of everything." (* 1) (Akderin, 2014). A word derived from the Greek word "Chaos" is "to open up, to yawn, to split" or "to yawn and open up to give birth to something."  (4) (Dürüşken, 2014) "Khaos means 'abyss, stretching slit, opening, emptiness' and expresses that everything has been thrown into the world system from an unknown cliff or void." (* 2)( (Werner, 2011).

Khaos, the chasm that stretches and opens to give birth to something, first  forms Gaia.  Gaia is Mother Earth with great space, where every being emerges as her immortal home. Mother Earth is the earth. Khaos, which is itself a formless and yawning void, separates the earth from its complex and disordered state, and the universe thus begins to form.  He creates Eros, the third main element after  Gaia  . Eros means "love, love" in Greek.  The exact equivalent used by Hesiod is "desire". (*3) (Erhat, 2014)

Gaia gives birth to Ouranos spontaneously with Eros, that is, with Eros’ desire. Ouranos, which means "starry sky”, is the sky from which Gaia was born in a way that will envelop and cover herself from all sides. Thus, the formation of Ouranos and  Kosmos,  which was born with the formation  of Gaia and Eros from Khaos, begins to take place completely.

Kosmos  means "to arrange, to arrange, to tidy up," derived from the verb kosmo. In Greek thought, Kosmos means "harmony, beauty, intelligibility and explain ability". 

2.2 The Beginning of Life and Evolution

The development of the brain, which governs the most primitive and most advanced vertebrates, is 600 million years old. To understand the evolution of the mind and the evolution of man, one must look at the evolution of the nervous system in living things and the evolution of vertebrate animals within it. Figure-1 In unicellular cells, cells sensitive to feeling in euglena  or paramecium or amoebae behave like a kind of nerve cell. 

Figure 1 Paramecium and Euglana

Living things; are divided into two basic groups vertebrates and invertebrates. Invertebrates do not have a nervous system. It has simple nerve cells that are sensitive to stimuli such as touch and light. For example, although bivalvia, which is found in mussels we know, is stimulated in members of the class of two crustaceans, they activate the muscular system of nerve cells and close their open shells.  In the Vertebrate class, as in Figure-2, there are living things ranging from fish with nervous systems to mammals according to the order of evolution.  Among these creatures, it is seen that the most developed and the most complex living thing is the human brain. 

It is necessary to know the mammalian brain simply:

Temporal lobe: It is the place of the hearing. 

Cerebrum: It holds our memories and controls the different signals we receive from the outside world.

Brain root: This is where all tasks are sent.

Cerebellum: It is the place of movements related to our muscles, such as running, for example. 

Ossipital lobe: Imaging works can be examined here. 

Frontal lobe: Speech control is in these sections. 

Parietal lobe: It is our center of touch and feel.

Pons: It is the location of the heart and respiratory functions.  (8)(Sakınç, 2015).

Figure 2 Vertebrate and Mammalian Brain Structure

However, in the modern age today, virtual minds, and high-speed recording systems that do not disappear, how they will affect the evolution of the human brain is one of the questions that are wondered today.

2.2.1 Pineal gland

The nervous system in mammals in vertebrates is closely related to brain development and the formation of intelligence. The Epithalamus, which forms part of the intermediate brain or forebrain, is the brain region where the biological clock and time concept are adjusted, hormone release and emotions are managed. 

The pineal gland, which is in this region, is located buried in the middle of the brain in all mammals.  The pineal gland: is a sensitive biological clock that converts the periodic neural changes produced by the light in the environment into hormonal information, the cellular structures in the organism during these hours are the structures of time measurement.  (Richards & Gumz 2012). As a result of types of research, it has now been determined that the pineal gland is a fourth neuroendocrine converter. That is, the pineal gland is the gland that converts from the nerve into a hormonal outlet (Wurtman & Julius, 1965).  When the sun rises, the production of the pineal gland stops, and in the dark, the melatonin it secretes begins to be produced. As darkness increases, its secretion increases.

In some cultures, and as in the Sumerians, the pineal gland was symbolized in the form of a pinecone. Hindu statues of God often hold a pinecone forward. Christian popes used pine cone handles for their mace. In Freemasonry, it is seen that the pineal gland is symbolized with the cone. 

2.3 Neuron and Brain Scientific Studies

  An Italian doctor named Camillo Golgi, while examining brain tissue with a microscope, poured the dye prepared by him on the brain tissue, making the axons, branched dendrites, and neuron cells easily visible. (1909) Using the highest technology today, Henry Markam tries to simulate a real human brain by initiating the Blue Brain Project and inserting all sections of human and animal brains into supercomputers, as in Figure 3. In doing so, he tried to remove his digital twin by increasing the concentration of calcium by transmitting electrical impulses to a neuron, examining all the nerves and connections of the dead brain. 

Figure 3 Brain simulation image from the Blue Brain Project

Blue Brain is the name given to the world's first virtual brain. This means a machine that can function as a human brain. He has the goal of creating an artificial brain that can think, react, make decisions, and keep everything in its memory. To date, similar studies have been conducted on worms and mice. 

2.4 The Emergence of Artificial Intelligence

In his essay "Can machines think?", Turing states that thought and machine are terminally decided. Instead of the word "thinking", he introduces the game "Imitation Game" by naming it with another equivalent question.  (Lame, 2017)

In 1951, he introduced a test called "The Imitation Game", which led Alan Turing to reveal what machine thinking is. In its first version, this game did not include machine intelligence. The person in 2 different rooms wants someone in a 3rd room to deceive him and the other to convince him. In later versions, the computer replaces the person who wants to be tricked and finds out whether it is a human or a computer. In this way, it has been useful in the development of natural thinking methods by forming the basis of many problems related to artificial intelligence. 

2.4.1 Artificial Intelligence Foundation and History

If the purpose of artificial intelligence is to gather under 3 main headings:

• To make computers more intelligent, 

• Understanding intelligence, 

• Making computers useful for their benefit.

Many behaviors of humans or animals reveal their intelligence or definition of intelligent behavior. Examples include:

• Learning and understanding from past and previous experiences

 • Making sense of complex and inverse situations 

• Ability to give a quick answer instantly

• Ability to compare in finding solutions to problems

 • Ability to make sense of and use data 

• Ability to overcome different situations     

 substances.

The basis or history of artificial intelligence can be classified in different ways, according to different periods. 

Prehistoric Period: Thousands of years ago, the attempt of Daedelus, the creator of the wind, to create an artificial man in Greek mythology can be cited as an example. However, the year 1884 can be cited as an important turning point for artificial intelligence. Charles Babbage, who experimented on mechanical machines, showed that these machines could not behave as intelligently as  human. But in 1950, a scientist named Shannon proposed that computers could play chess.

Birth: At the conference of scientists in 1956, held in Dartmouth, United States  , artificial intelligence was born. In this conference, A.  NewellJ., McCarthy, M.  Minsky, H.  Simon and C. Shannon proposed to investigate the possibility of computer programs creating artificial intelligence. Thus, the term 'Artificial Intelligence' was used. 

The first artificial intelligence programs (Chess program, logic theories program, Logic Theorist; both theories are Simon and Newell theories) and LISP (artificial intelligence programming language) were created in this process. 

The information about 'intelligent machines' in human history dates to ancient times. Theoretical and practical developments on automata in a scientific plan, on the other hand, the first foundations of artificial intelligence were mathematical studies and logic studies.

One of the first is mentioned by Babbage (the "Analytical" Machine in 1842) and A. Turing (the Universal Machine in 1936) about cybernetics (Wiener) in the brain's interpretation of the data obtained. 

One of the main factors in the birth of artificial intelligence is the emergence of the computer. In this way, studies were carried out by considering whether we could combine computers with intelligence, and Alan Turing created a test that revealed the decision of whether computers are intelligent or not.

The Dartmouth Conference can also be called the beginning of a new era in artificial intelligence. This conference, organized by Dartmouth College, mentioned artificial intelligence (AI) for the first time and accepted the participants as the pioneers of artificial intelligence. 

One of the important achievements of this period is software used to distinguish similar geometric shapes. The successful results of this period marked the beginning of very early and unrealistic period of anticipation about the creation of intelligent computers. 

Dark Period (1965-1970): The fact that the hardware and software inadequacies in this process were very few and that software was produced in this way revealed such a period. The hasty attitude and excessive optimism created in the previous process convinced scientists on the subject that producing intelligent computers is a very simple process. As a result, computer experts tried to develop a philosopher-type mechanism and aimed to make intelligent computers by simply loading data. For these reasons, this period has the characteristics of a dark waiting period. 

Renaissance Period (1970-1975): In this process, especially software such as disease diagnosis was developed, they formed the basis of an exciting and long adventure that is still trying to reveal the results that are exciting today. 

Partnership Period (1975-1980): In this period immediately after the Renaissance period, artificial intelligence researchers saw that they could benefit from different branches of science such as psychology, language, etc.

In the 70s, great success was achieved in artificial intelligence with the introduction of the basis of artificial intelligence in subjects such as understanding the native language, robotics issues, and representation of information. 

In the 80s, with a serious increase in efforts to investigate by critical practical studies and industrialized countries in parallel, studies with high goals in a significant part, applications increase with the introduction of artificial intelligence into economic life.

In the 90s, the theme of artificial intelligence, it began to be heard with chess, which is identified with intelligence, and the DeepBlue software developed by IBM defeated the world champion Garry Kasparov in 1997.

With the 2000s  , artificial intelligence began to be found even in artistic activities and even computers began to be able to draw pictures. Today, there are artificial intelligence applications where we get much better-quality results with fact that we have ranked in every field.

In 2016, DeepMind's AlphaGo program defeated Lee Sodol, the world champion, in a game with a probability of over 14.5 trillion after the 4th move. 

As the most important feature of this entrepreneurship process that we are currently in the 2020s, there are attempts to take artificial intelligence out of the laboratory and adapt it to the needs and wishes of today's world. With the spread of many libraries and methods to large masses, it is seen that very wide areas of use have emerged. 

2.4.2 Artificial Intelligence Technologies

If we try to gather artificial intelligence under 3 main headings; The first wave can be done with more limited processors up to the 2000s, with training processes spread over longer periods using more limited memory spaces, and with primitive algorithms. But what happened was the spread of the internet, computers, technology, and the spread of information, and it was not easy to realize this in the flow of life as we could not notice what was happening now.

In the 2000s, computer technologies continued to spread and develop. With these developments, it has become easier to reach more memory and higher processing powers. In this way, artificial intelligence algorithms began to prove themselves by getting rid of normal algorithms. In this process, many different artificial intelligence models have emerged. Since these bits of intelligence are fed by data, big data hunting has started today.  The better your big data and model, the more successful your success begins to increase in terms of model results and even transfers the results much faster and more accurately compared to humans. For example, when pictures of many similar cats are given and piece of training are given, and then new cat pictures are shown, results can be reached with high accuracy in proportion to the similarity to the cat. Is he able to explain or question why he trained a cat, and how he came to this conclusion? Can it make meaning and context? Will they be able to adapt to the new environment, will they be able to improve themselves without people?  It is very difficult to say that artificial intelligence models work the same as the human brain, but we can say that some of them work in a similarly. This is how the 3rd wave will come to life by those who question. Figure 4 shows AI movements. The first wave is the output of artificial intelligence, classical programming, and outputs according to inputs from peripheral units. The second wave of artificial intelligence can be described as an example of intelligent optimization and the development of artificial intelligence algorithms, deep learning or machine learning, while the third wave of artificial intelligence is  characterized as super-intelligent algorithms with humanoid thinking and inference ability that produce decision output.

Figure 4 Artificial Intelligence Movements

Artificial intelligence includes machine learning and even its sub-branch, deep learning. There are certain differences between them. Non-deep, i.e. classic 1st wave machine learning, was a learning method that was more dependent on human interventions. Experts often make sense of the differences between the hierarchy of features to understand the differences to learn and the data that requires more structured data. Deep learning is a sub-branch of machine learning, leveraging known labelled datasets without requiring human intervention. The differences between deep learning and artificial intelligence are shown in Table-1:  (Microsoft)(AI); 

Table 1            Differences Between Deep Learning and Machine Learning

Deep Learning uses neural networks with many layers. These are FFNN, RNN, CNN, GAN etc.

FFNN is the simplest type of neural network. Information is transmitted in only one direction on the network, starting from the input layer to the output layer. Each layer is made up of a series of neurons and is fully connected to all neurons. The last layer represents the predictions.

RNN is a type of repeating neural network. The RNN network records the output of the layer and feeds it back to the input layer so that it can predict the outcome.

CNN, the convoluted neural network is organized in 3 dimensions. These are Height is width and depth. Neurons in one layer are connected to a specific region of the next layer, not to all the neurons in another layer. It takes a single probability score determined during the depth period and creates the output.

GAN is the production model that is trained to create lifelike content such as Generative attacker networks, images, videos. It consists of two different networks called discriminatory and constructive. Each network is trained together. During training, pollution is randomly generated to generate new artificial data like real data. The separator takes its output as input from the renderer and uses the real data to indicate whether the generated data is fake or real. By competing, each network differs. The creator tries to create artificial content that is indistinguishable from real content and tries to accurately classify discriminatory entries as real or artificial. 

After 2015, new revolutionary developments are taking place. GPT-3, DALL-E, such as APIs and gives clues about the future of artificial intelligence.  (14) (Baker) It is seen that with reinforcement learning, very successful progress has been made in the physically based tasks of the movement of complex objects.

3. 3rd Wave in Artificial Intelligence

The world is starting to design digital people with artificial intelligence. Soul Machines Human has created an 'Autonomous Animation Platform' that allows artificial intelligence to learn new things from its interactions with real people by releasing the OS 2.0 operating system and is trying to make digital artificial intelligence by passing the answers through certain filters.  The most interesting project is the Baby X project. With this project, the baby responds and combines it with artificial intelligence and provides training. Decision making goes through certain decision stages, very similar to the known structure of the brain. 

 When using Baby X

• Cognitive Architectures Inspired by Biology
• Neuroscience
• Cognitive science
• Developmental Psychology
• Cognitive Linguistics
• Affective Computing

Together, BabyX bridges the human world, enabling the manifestation and application of various models of the brain to enable scaled interactions and responses.

The facial expression of the Baby X reflects a brain state or human thoughts. Facial behavior is influenced by many factors, cognitive, emotional, and physiological, and as a result, BabyX offers a highly detailed, holistic, and biologically based approach, much more so than has been tried before in animation simulation and traditional CGI. An example study structure is shown in Figure-5.

Figure 5 Human OS 2.0 Working Structure

The study in Figure 6, conducted with computer scientists and psychologists from the University of Washington, showed that robots, just like children, can collect information through exploration and monitor the child or baby to determine how to do it. It can be seen as the beginning of the steps toward making robots that children learn, who can learn by watching their surroundings. For example, you may only need to show robots or similar systems how to fold clothes, wash dishes or similar tasks. For this, we need robots to be able to make sense of actions and do it on their own. 

Initially, the first goal was to understand and imitate simple behaviors. In the future, these models will be developed to help them learn more complex and specific tasks. According to Meltzoff, “Babies learn by watching others, making very good observations, and playing games. Why not design robots to learn this way?

Figure 6  Joint work of developmental psychologists and computer scientists at the UW

3.1 Chaos 

Chaos Theory: It is called the science of being able to predict the next step of "naturally incalculable" systems, and even by its meaning it contains complexity in it. Chaos; It is a set of tools that includes mathematics that allows simple arranged structures to infer from a different situation that opens up to the complex functioning of various natural systems, such as the weather forecasting of meteorology and the calculation of the orbits of asteroids.

Edward Lorenz (mathematician), in the 1960s, while working as a meteorologist at MIT, showed that very slight differences in the initial conditions of the system would lead to unpredictable and significant differences in the system's results. In this way, convective systems (weather forecasting, meteorology) have also found chaotic.  (12)(Call)

 Lorenz came up with calculations consisting of three different differential equations of the first order to determine the temperature exchange of air as an example.

Dx /dt=-a*x+a*y 

Dy /dt=b*x-y -z*x 

Dz /dt=-c*z+x*y

Robert May, a biologist (11), showed in 1975 that Chaos Theory  could also exist for biology. When he examined the number of biological populations over time under certain conditions in a system, he determined how chaotic occurs. These studies are explained by iteration graphs and logistical equations. 

The slightest changes in these inputs constitute the features that distinguish us from each other. In this way, by applying the chaos equation to the inputs of individuals, it draws very different conclusions from very similar piece of training . This gives us diversity.

3.2 Evolution 

Evolutionary algorithm, mutation  inspired by biological evolution, Evolutionary algorithm, mutation inspired https://tr.wikipedia.org/wiki/Mutasyon by biological evolution, genetics, reproductionhttps://tr.wikipedia.org/wiki/Biyolojik_evrim, natural selection,  and recombination  similarly use mechanisms by which the good  and  strong survive. Similar solutions to optimization problems https://tr.wikipedia.org/w/index.php?title=Aday_%C3%A7%C3%B6z%C3%BCm&action=edit&redlink=1 characterize individuals in a population, and selection value functions determine the environment in which the results "live" (13)(Chen). 

The disadvantage of evolution, the fact that millions of trials try to be carried out and the duration takes too long, poses some problems when putting evolution into practice. But why should we operate evolution until a man comes? we need to operate an evolution that will add to man from this point on. In other words, we are creating a front-loading evolution by creating evolution ready to a point. 

In addition, by creating hundreds of individuals and ensuring that everyone has its characteristics, we started to apply the theory of evolution in artificial intelligence by using the elimination mechanism. By providing inheritance, it is possible to provide certain features with front-loading when desired. 

3.3 Metacone (MTCN)

Metacone aims to make a digital brain by combining chaos, evolution, and genetics with artificial intelligence. 

In fact, the human brain does not have much better perception capacity than current software, and it cannot provide continuity as much as any software. It is a structure with flaws and shortcomings. But no current artificial intelligence software can carry human values. 

For example, you can't put a person at the box office for months to read a license plate; they have needs due to being human.  But a computer can run for years without a break.  A person is not always able to notice small details from millions of high-resolution photos, gets tired or has eye sensitivity, cannot perceive under certain pixels without enlarging the photo, there are certain limits to eye capacity. Therefore, man is open to error and has shortcomings. With this concept, we design a brain that is open to errors and has limited capacity. 

As we mentioned earlier in the article, supercomputers are needed to design a real brain. But we don't think the real brain needs to work the way it really is. With software techniques, we can program a brain's number of connections, its attachment just as that brain works, and do the same task with much more effective or even much fewer data than the brain. In addition, mechanical connections in the real brain are made by electrical conduction. But these real connections are not needed in the software. There is no need to create the 2ms delay response necessary for the neuron's impulse in a much easier way and without much more delay. We can construct temporal situations in a very different way. In addition, considering the time it takes to pass through hundreds of neurons in the next channel, it can give very fast response times thanks to software techniques and the capabilities of multiple processors.

 Humanity will be excited about what a world-sized brain can do by creating similar world-class networks like Blockchain. By enabling independent computers to work with each other in the MTCN infrastructure, Metacone will focus on having the ability to learn through its own algorithms in a method more like how a child or even a baby learns, rather than creating models with many labelled educational data by focusing on wave 3 artificial intelligence. It will not only minimize reliance on large data sets, but also address the issue of incorrect training data. The development of computer technologies will provide the environment for new developments in this field and will have significant benefits to the development of Metacone.

Figure 7 A cross-section of the training process by Metacone Numbers tutorial 

Metacone was able to teach artificial intelligence to speak without installing any language, such as when a baby learns to call a mother in any language, or even with the learning curve of a child in the same primary school with mathematically very limited data, as in Figure 7. While doing all this, it used artificial intelligence algorithms as a tool, not as an end. By developing our own algorithm, it wants to start the 3rd wave movement on the way to super intelligence. 

MTCN differs from today's artificial intelligence technologies in many ways.  Referencing ability, randomness, teaching characteristic of the trainer, self-development by thinking, the development of emotional intelligence and high intuitiveness and the ability to enter the results of other artificial intelligences as inputs are the biggest distinguishing features. This system is system that is constantly evolving. 

Table 2            Differences in Machine Learning, Deep Learning and MTCN

4. Result

If the 3rd wave is artificial intelligence, creating digital people, how human beings come to this process should be examined. Evolution and chaos cannot be outside of these matters. When the basic characteristics of a child now of birth are examined, the child's innate evolutionary abilities, diversity and chaoticism should not be ignored. The shortcoming of artificial intelligence is that these instinctive features could not be created. Therefore, we will either wait for some traits to occur evolutionarily or upload them in the first place. We think that the application with the name METACONE is the beginning of a new era in this field. By combining evolution and chaos with the top algorithm that will control classical artificial intelligence we are building a kind of General / Super intelligence that does not need big data that can learn like a baby, and that can learn instantly and infer what is around it. 

5. Resources

1-) Acar, E. (2006) Mortality, Immortality and Artificial Intelligence. Istanbul: Sub-book.

2-) Aysever, R. L., 2001, Machinery and Intelligence, Tübitak Bilim ve Teknik Dergisi, June, p.62-67.

3-) Baştan, S., 2003, Artificial Intelligence, New Communication Technologies and Organizational Change: Towards Smart Organization, Management and Economics: Celal Bayar University Faculty of Economics and Administrative Sciences DErgisi, 10 (1), 187-203.

4-) Çiğdem Dürüşken, 2014 Ancient Philosophy: A Thought Adventure from Homer to Augustine,  Ed. Begüm Çiçekçi, İstanbul, Alfa Basım Yayın, p.  19.

5-) Fırat, S. Ü. and Fırat, O. Z., 2017, A Comparative Study on the Industry 4.0 Revolution: Concepts, Global Developments and Turkey, Toprak İşveren Dergisi (114), p.10-23.

6-) Köroglu, Y., 2017, Theoretical and Practical Limits of Artificial Intelligence.

7-) Özdemir, Ş., 2014, The Impact of the Industrial Revolution on the History of Science: Science and Technology Intertwined.

8-) Sakınç, M. 2015, "The evolution of the brain and mind." Journal of Science and the Future, pp32-p37.

9-) Brain and Consciousness Evolution, T. Erhan Coşan, Brain Awareness Special Issue / Brain Awarene.

10-) Jonathan Borwein (Jon), Michael Rose, Explainer: what is Chaos Theory?  https://theconversation.com/explainer-what-is-chaos-theory-10620

11-) Bishop, Robert, "Chaos", The Stanford Encyclopedia of Philosophy (Spring 2017 Edition), Edward N. Zalta (ed.), URL = <https://plato.stanford.edu/archives/spr2017/entries/chaos/>.

12-) Çağrı Mert Bakırcı, Evrim ağacı https://evrimagaci.org/kaos-teorisi-nedir-dogadaki-kaostan-soz-ederken-neyi-kastediyoruz-8198

13-) AutoML-Zero: Evolving Machine Learning Algorithms From Scratch Esteban Real*1 Chen Liang*1 David R. So1 Quoc V. Le1

14-) EMERGENT TOOL USE FROM MULTI-AGENT AUTOCURRICULA Published as a conference paper at ICLR 2020 Bowen Baker∗ OpenAI / Ingmar Kanitscheider∗ OpenAI / Todor Markov∗ OpenAI / Yi Wu∗   OpenAI    / Glenn Powell  ∗ OpenAI  / Bob McGrew∗ OpenAI / Igor Mordatch∗† Google Brain

15-) (Jennifer Langston)(Translated by Mert Özel) -https://evrimagaci.org/insan-bebeklerinden-ilham-alan-algoritmalar-robotlarin-ogrenme-becerilerine-guc-katiyor-4803 -https://www.washington.edu/news/2015/12/01/uw-roboticists-learn-to-teach-robots-from-babies/

(*0) Yalçınkaya, Y., 2010, The future field of organizations in awareness and diversity of knowledge: Innovation, Turkish Librarianship, 24 (3), p.373-403.

(*1) Hesiod: Jobs and Days – The Birth of the Gods, Translated by Furkan Akderin, 
Istanbul, Say Publications, 2014

(*2) Jaeger, Werner: The Idea of God in the First Greek Philosophers, Translated by Güneş Ayas, 
Istanbul, Ithaki Publications, 2011 

(*3) Erhat, Azra: Mythology Dictionary, Istanbul, Remzi Kitabevi, Twenty-second Edition,
October 2014