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Posted Date: 06/21/2022

Business

1920. Russian Communism and European Inflation

Von Mises said that a socialist economy in the Soviet Union, where the government controls wages and prices, can never succeed, and the biggest fallacy of socialism is the lack of a proper price system. Lenin allowed the state to control the economy and set wages and prices, but the Soviet Union faced a serious economic crisis in return. The Soviet Union was nothing more than a bleak land, plagued by cold, famine, sluggishness and death.

 

Realizing the need for a new policy, Lenin instituted the so-called New Economic Policy, which allowed farmers to sell their agricultural products and gave them ownership of their farmland. And he says allowing small businesses will revive the economy. Lenin, however, is bitterly criticized for abandoning Marx's principles by his left-leaning colleagues because of it. Nevertheless, Lenin was able to confront such criticism head-on. Rather, Lenin denounced his critics as fools, because the government and the Bolsheviks would continue to control heavy industries such as steel, railways and coal—what he called the core sectors of the economy.

 

Less than a year later, Lenin died, and people believed that history was on their side. In fact, less than 30 years later, more than a third of mankind, including Russia, Eastern Europe, and China, came to live under Leninist economic theory, and his successors are strengthening the Communist Party's control over the core economic sectors of the Soviet Union. Stalin introduced a centrally planned economy by the state, and under Stalin, the Communist Party planned and controlled everything in the economy. As communism spread, capitalism seemed to disappear soon.

 

 

On the other hand, unlike the Soviet Union, Germany and Austria were paying the economic price of peace, as Keynes predicted. In order to pay huge war compensation, the defeated countries had to print more money, and the result was inflation and hyperinflation. There is none other than economic catastrophe! Back in the early 1920s, people had to go shopping with a basket full of money. Money was less valuable than wallpaper, and the bill of a million marks was used as firewood for the hearth. Hayek, who was then attending a statistical institute, had his wages increased 200 times in eight months.

 

                         

 

A shoe of 12 marks in 1913 is priced at 32 trillion marks in 1923 10 years later. The price of a beer in Hitler's regular beer shop was 1 billion marks. Killer Inflation! That was it. As a result, the people's assets disappear into the air. This murderous inflation was the reason the Nazis and Hartler were able to come to power. Those who lost their fortune overnight had no choice but to support them.

 

 

In the process, Hayek of Vienna believed that inflation was the root of evil that corrodes society and ruins democracy, and the fight against inflation eventually becomes the foundation of his economic philosophy. As such, Europe in the 1920s was unable to recover from the wounds of World War I, but on the contrary, New York was in the midst of a prosperous 1920s. American cities were booming!
 

 

Mericans spent their money dancing and partying.

They buy cars and drink expensive moonshine.

And I bought a bunch of "stocks".

Posted Date: 06/21/2022

Business

Big game of life phenomena big game

After about 600 million years after the appearance of independent motile single cells such as bacteria, they are eaten by amoebasic cells larger than their own body size and form a symbiotic relationship without being digested inside. A cell is a cell with a nucleus surrounded by a nuclear membrane and has one or more chromosomes. Mitochondria, which are intracellular organelles, are representative evidence of this. Mitochondria are independent organisms with their own genes.

After eukaryotic cells, life phenomena make a big leap in evolution. This is the phenomenon of two or more cells becoming multicellular where they meet. According to scientists, the biggest phenomenon of life was the process of going from unicellular to multicellular, and it took about 2 billion years to get there. There are two main types of multicellularity: 'colony' and 'multicellular animals and plants (living organisms)'.

Stock

A typical colony is jellyfish. When the water temperature changes, in other words, when the conditions for survival become difficult, jellyfish coalesce together. They stick together to form a body up to about 30m long, and the longest living thing on Earth is a colony like this. After the jellyfish form a colony of Jellyfish, the jellyfish attached to the front are in charge of the head, the rear is the tail, and the middle part is responsible for the reproductive organs.

The important point here is that each jellyfish is an independent living organism when the jellyfish live together to form a long colony. When the genetically unrelated gnomes change their environment, they cling to each other in order to survive, eating food and reproductive activities like a single organism. Then, when the conditions are right for them to live, they separate themselves again and lead an independent life. This is a colony, a type of multicellular organism.

Multicellular flora and fauna

These colonies are genetically independent, but multicellular plants and animals are genetically identical. About 60 trillion somatic cells that make up our body are genetically identical. This is because, in principle, the story of creating clones by isolating one human somatic cell is genetically identical. Now it is important how multicellular plants and animals evolve. In particular, an important strategy for multicellular animals is the evolution of the central nervous system, and the core tactic is in the nervous system.

Posted Date: 06/20/2022

Science

Uncertainty and subjective probability of corporate management

Decision making is not about the past, but about the future intrinsically. On the other hand, the essence of the future lies in uncertainty. So, decision making is something about uncertainty. Uncertainty means unpredictability, which means it is random.v

The randomness of this unpredictability can be expressed in other forms of randomness. It is the randomness expressed on the probability distribution. Suppose you toss a coin 100 times and count the number of heads that appear. The reason you are doing this probability experiment is to determine (inference) and determine that the coins used to toss are not balanced. It is reasonable to believe that the number of heads appearing 90 or 15 is statistical evidence that the coin is not balanced.

However, where should the boundary line be drawn for making such statistical inferences? Is the number of heads appearing 77, 65, or 55? We cannot draw any conclusions from a sample of 100 tossing a coin without knowing the distribution of probability that a balanced coin will appear heads.

Posted Date: 06/20/2022

Science

Neuron Cell Body, Neuron and Embodied

The obvious and only purpose of what we see, learn and learn is to make it our own. Scientific thinking is a "skill" that is not easy to make on your own. It's beyond ordinary...

The obvious and only purpose of what we see, learn and learn is to make it our own. I don't just have it as an idea in my head, but I want to use it in the real world.

So, we “train” it to make it our own. If you want to take it in a short time, and if it is knowledge in text form, you can see it over and over and try to solve the problem and make it completely mine. Or you can get it at some point by accumulating and accumulating over a long period of time without intensive effort.

The expressions “I know with my heart, not my head, I know with my body,” are possible expressions when it is embodied as my own.

Whether it's mental or physical, being completely mine is nothing more than neuronal cell bodies and neurons forming a network in the brain, according to research in brain science. Being completely mine from the perspective of brain science is nothing more than a physical thing.

Scientific thinking is a "skill" that isn't easy to do on your own. It is thought that some kind of genius ability beyond the ordinary should be endowed. Just like Sherlock Holmes.

However, the current process of seeing texts about scientific thinking with your eyes and thinking with your head is the process in which nerve cells and neurons form a network of “scientific thinking” in the brain. A genius may not know it, but ordinary people like us will accumulate and accumulate such processes and at some point acquire such abilities.

In the process of constantly reading related articles, your head will start thinking at that moment, and you can gradually deepen your inner workings. That way, you will test it when you occasionally encounter situations in the real world. In doing so, I can make it more and more my own. The information network of neurons for scientific thinking will be fully formed in the brain.

Scientific thinking, namely Sherlock Holmes, Father Brown, Dupin, and Hannibal, such as Zhuge Resonance, can be compared as people who studied mind, but in any case, their extraordinary abilities can enrich our lives.

Let's try it out and use it on an interpersonal object.

A person who practices it in the real world is Dr. Goldlett, a physicist and business scientist. In order to systematize the thought process, Goldlet practiced and researched more than 200 times of linking real-world business and everyday problems with facts observed on paper in a causal relationship. In the process, he was able to obtain necessary and good theories and solutions to problems.

He uncovered numerous "errors" that had been adorned with authority and theory, and enabled us to awaken the thinking that had been dominated by authority and theory. He was able to get a way to prove the right thing, not from authority and theory, but from "the logic inherent in the situation." Since the discovery of arithmetic calculations by the American Management Accounting Association (IMA), this is the background to the emergence of a "thinking process" theory called the Thinking Process, which has been evaluated as the most important "intellectual achievement" of mankind. ASAM TESTING

Making it mine means that the neurons in the neuronal cell body form a network, and the process of thinking about scientific thinking while reading this article is the process of neurons forming a network. For "scientific thinking," neuron cells are interconnected information networks. "Scientific thinking" is also a methodology by which we can give each other "intellectual stimulation".

Posted Date: 06/20/2022

ML & AI

One. Hypothesis-based thinking

The ability to think scientifically, the ability to solve problems within an organization, and the ability of Sherlock Holmes to solve events are all other words for the correct thinking process.

When a new phenomenon is observed in the real world, the scientist does not try to discover additional case data. The reason that a new phenomenon has occurred is because it is already a fact. Instead of collecting additional data, scientists start thinking from there. This "thinking" of a scientist is one of the powerful tools utilized in physics. This is called the Gedanken experiment, or thought experiment. This is an experiment that I just thought of in my head, but never done in reality.

What is the cause of the observed phenomenon?

Thinking, thinking, thinking, and repeating thought experiments in your head. Finally, infer the most probable cause. It is the creation of a cause 'hypothesis'. And if the cause he inferred is correct, he tries to find it because another result from it will surely exist in the real world.

If another result that is sure to occur from the cause he predicted and inferred is observed in the real world, the cause (hypothesis) he deduced is named a theory, and if it is not observed, discard the first hypothesis and infer a new cause (hypothesis) repeat the process of doing it.

Scientific thinking

It is the result of this thought process that Heli predicted that comets would come, Maxwell predicted electromagnetic waves, and Einstein predicted that light would be bent due to the gravitational effect.

How does this apply only to science? How should this be the exclusive domain of scientists? The numerous incidents that occur in the business field, as well as the problems and challenges to be solved, seem to be no different. It is simply the absence of such a thought process.

The story related to the so-called photon hypothesis, which awarded Einstein the Nobel Prize in Physics, is a suitable case to show the whole of this correct thought process. Scientific, Sherlock Holmes-style, mathematical thinking is typically shown. This is the story of Einstein brilliantly solving a new phenomenon that was neither explained nor understood by science observed from the photoelectric effect experiment and the study of blackbody radiation. Through Einstein's case, we also confirm that the scientific thinking process, the Sherlock Holmes' thinking process, and the mathematical thinking process are the same.

Posted Date: 06/06/2022

ML & AI

Artificial intelligence series overview

On March 9, 2016, a shocking incident occurred in which Go player Sedol Lee lost against Google's artificial intelligence (AI) AiphaGo. People who don't usually play Go, those who have nothing to do with the IT industry, and those who think that artificial intelligence is a subject that only appears in movies and cartoons also paid attention to this. This process is reflected in the documentary.

AiphaGo was implemented by deep learning, which is the core technology of artificial intelligence, and was an opportunity to spark interest in deep learning in the general public after the match with Sedol Lee. On the other hand, machine learning, machine learning as an artificial intelligence technology has also begun to be recognized by the public, and the relationship between artificial intelligence, machine learning, and deep learning can be summarized as follows.

Machine learning belongs to a large category of artificial intelligence, and a part of machine learning is classified as deep learning. By analogy, artificial intelligence corresponds to general food, machine learning corresponds to more advanced food, and deep learning corresponds to high-end steak dishes. Therefore, the flower of artificial intelligence can be called deep learning. This does not mean, however, that other technologies in machine learning other than deep learning are useless. It's just the choice of which technology to apply to the situation is different. Naive Bayes algorithm, a type of machine learning technology, is typically applied to the technology of filtering spam in the mail space we encounter every day.

In this series, we want to focus on Bayesian inference underlying deep learning and artificial intelligence technologies. The basis of deep learning is an artificial neural network. Although the Bayesian Neural Network model, which has recently evolved an artificial neural network, is attracting attention, it focuses on artificial neural networks and Bayesian inference itself.

Everyone talks about artificial intelligence. My Facebook news feed is also covered with artificial intelligence content. Articles related to artificial intelligence account for a significant portion of newspapers, and electronic products need to include the term artificial intelligence to sell well. There is even a joke that if you enter the word artificial intelligence in your business plan, you can receive investment. Although this is likely to cause controversy over bubbles, artificial intelligence, especially deep learning, is proving itself day by day by putting out amazing results to the world. In addition, many artificial intelligence software libraries have emerged, becoming a world where even general software developers can easily use artificial intelligence. However, there was a time when artificial intelligence was reserved only for 'doctors'. This is because the theoretical structure of these artificial intelligence technologies is very mathematical and specialized, so it is theoretically overkill for the general public and general software developers.

However, if this series is for general developers, it will mainly be filled with contents about libraries and source code fragments such as Python, Anaconda, TensorFlow, and Keras. The main purpose is to explore the content itself of artificial intelligence rather than to explore what kind of story these technologies consist of and how they work. There will be some mathematical and technical content, but there will be no need to worry at all as it will be developed with common sense content so that ordinary business people can fully understand and access it.