Thursday, November 1, 2018

The Dawn of The Era of Dictatorships; Explained by The Systems Theory.

The Dawn of The Era of Dictatorships; 
Explained by The Systems Theory.
A system is a set of connected, interconnected, interacting, influencing each other, influencing each other's behavior, influencing each other's evolution elements called parts of a system.
Every system has a mission, but often it is not easy to figure that mission out and to formulate it in a clear unambiguous statement. The goal of every system is to survive and to exists as long as possible to fulfill its mission.
If a system dies that means it was not healthy enough to survive. Healthy means stable, effectively functioning, productive, reproductive.
Every system has at least one parameter which describes the health of the system. This parameter is measured as the average value of the values of this parameter measured for individual parts of the system. When many parts of a system have a low value for the health parameter the overall health of a system has a low value and the system becomes ill, sick. This is a sign of a crisis or of an upcoming crisis; this is the sign that soon a system may lose its stability.
A system cannot be healthy if there is a large number of unhealthy parts in it. This is why the ultimate goal of any "smart" system is to keep as many as possible parts in a healthy condition.
Unhealthy elements of a system do not "want" to die; they want to return to health stability, and they want that to happen as soon as possible.
There might be many possible reasons, negative influences, for declining health of a system or its parts. One of the strongest negative influences is the disconnect and/or the dissonance between a system and its environment.
Every system is a sub-system of a larger system. The surrounding of a system is called an environment. There is always an interaction between the system and the environment.
A system may change the environment. An environment may change the system.
In order to survive a system uses some resources coming from an environment and then releases some residues back to an environment. When an environment changes, the system has to evolve accordingly to those changes, or the system eventually breaks apart or dies.
Prolong existence of a system means that the processes happening inside it are stable and the changes happen evolutionary, in a slow manner, fluctuations are small and do not tear the system apart.
When the changes in an environment are large and/or fast, or both, the system has to adapt quickly, and that leads to large fluctuations inside the system. If some of those fluctuations are too large, and if different parts of a system fluctuate in opposite/competing directions, the system might fall apart; it might be divided in smaller parts which individually may adapt faster to the changes in the environment.
When a system has to adapt to some slow changes in the environment, it can be done evolutionary via multiple communication between different parts of a system. This process allows to take into an account interest of all active parts of the system (those which choose to participate). This process takes a long time, but when there is no need for fast decision making this process is sufficient.
When a system has to adapt to drastic changes in the environment, some parts of a system resist any changes because they desire stability, they perceive instability as the threat to their existence. There are forces within a system, there are some elements of that system which always try to bring the system back to equilibrium, back to stability, and tend to do it as soon as possible. But there are also parts of a system which embrace the change. The result depends on the interplay between forces of change and forces trying to restore stability. When fluctuations in different parts of a system are large and threaten to break it apart, the negotiations between different parts begin. But if those negotiations/communications are too slow, the changes needed to be done in the system to address the changes in an environment constantly lag, remain behind and inadequate, do not solve health problems, and the fluctuations only keep growing. There are two common outcomes from this situation: (a) the system eventually falls apart; or (b) one of the parts of the system becomes dominant, suppresses the processes in other parts of the system, and forces all parts of the system move in the same direction. Those parts of the system which resist the enforcement are getting weakened, or damaged, or even cut from the system. If two or more parts of the system fight for domination, and no one wins, the system eventually falls apart, or dies, or getting weakened and becomes absorbed by another system from the environment, i.e. becomes a part of another system.
If the system survives as such, it means one dominant part (or a series of dominant parts replacing each other) was able to navigate/govern the transition of the system from the initial state (the one that was in disbalance with a new environment) to the new stable state (the one that becomes in balance with a new environment). When that happens, eventually the evolution of the system becomes governed again via a long process of multiple communications between may parts of the system.
Every system is inertial, every system has such a property as inertia. Inertia means that every process takes time, no change can happen instantly. The larger a system is the more inertial it is, the more time is needed for a system to change. In a case of a drastic changes in an environment, inertia will not allow a system to quickly initiate required changes. When parts of a system begin communicate on what changes needed to be made, inertia leads to the situation when all individual/partial changes are based on the previous states, previous experience and do not represent adequate solutions.
A society is a system. People and groups of people are the parts of that system. Lately (for the last 20 to 30 years, which is a blink of an eye, history-wise) millions of people all around the word have been feeling the decline/decrease/degradation in their economic status, or the threat of that decline/decrease/degradation.
There are four major reason for that:
1. Climate change.
2. Financial globalization.
3. Rise of human-replacing technologies.
4. Mass migration.
The democratic approach to finding the political solutions to the challenges posed by the four reasons require long negotiations between different parties, participants, political and financial players. That makes the democratic approach to be too slow and inadequate. While negotiations attack one specific issue, the state of that issue worsens and more other issues arise. That leads to more and more people struggling socially and financially. More and more people want their social and economic status be improved and improved quickly. As the result, they turn to an opportunist who offers quick solutions. That leads to the rise of authoritarian politicians.
Of course, quick solutions to difficult problems do not exist, but people demanding a quick solution to their problems do not rely on reason or logic.
This is when we need to state how public education affects politics.
The vast majority of the people whose status is in decline are also people who have no advanced education. While growing up they have not been exposed to scientific facts as well to a scientific way of thinking. They have very narrow knowledge and underdeveloped reasoning abilities. Hence, they do not respond to logical arguments and react based on emotions and the culture of their “tribe”.
When someone has a poor education, it is not his or her fault – at least in a developed country. It is the fault of people who don’t care about the state of public education, or of people who sabotage the quality of public education (e.g. politicians whose power is based on brainwashing and mind manipulation).
When children misbehave, in order to force them into the right behavior, parents told them that if they will not do the right thing a troll will take them in the woods, or if they do the right thing, Santa Claus will bring them a gift.
In order to excite his base Donald Trump tells his followers, that if they will not have a border wall, bad illegal immigrants will take their jobs, rape their women, and kill their children.
In both cases trying to use fact based rational argument is useless and one influences behavior of other people using exactly same psychological tools - exciting strong emotions of fear or reward.
Children do not know enough facts and do not have developed reasoning abilities.
But adults who grow up without adequate education also do not know enough facts and do not have developed reasoning abilities; they are basically children in grownup bodies.
When the number of “adult children” (who become active participants of a political process due to worsening of their life) is significant, opportunistic politicians have a good chance to brainwash and manipulate the minds of millions of people.
When the changes in the environment are significant, the state of the society is in disbalance with an environment; the state of the society keeps declining, and there are many people who are not susceptible to reason and react based purely on emotions and the culture of their “tribe” and who become politically active, the general systems theory demands the inevitability of the rise of the authoritarian politicians all around the world, because democratic institutions are simply too slow to provide adequate solutions.
In the time of a war, people are looking for a general.
In the time that some people perceive as a war, those people are looking of a strong leader.
When the number of those people is large, they begin dominate politics, especially when they have a freedom of expressing their views. The opposition, i.e. people and groups of people who oppose the authoritarian tendency, act based on inertia, i.e. based on the ideas which worked in the past but inadequate to address the changing environment (a new generation of opposition is need to realize that old solutions will not work to solve new problems). 
Hence, the transition from a democratic policies to authoritarian policies is inevitable and unavoidable.
The only question remains – will the "general"/"dictator"/"ruler" be a good decent person, or it will be a liar, a bigot, a misogynist, a narcissists, a racist, and a tyrant?
Appendix I
A system where the parts almost don't interact  with each other, does not evolve. Evolution happens because interactions lead to the fact that the properties of a system are large, or bigger, or wider, or broader, or richer than the sum of the properties of its parts. It means that the most important parts of a system are those which establish, provide, govern interactions between its parts. In a society, the most important people are not the strongest ones, or the richest ones, or the smartest ones but those who can effectively communicate, who have an ability to convince other people what is right or wrong thing to do, the most convincing ones (including con men).
Appendix II
Imagine, that for decades someone was storing every year a shed of firewood to use in the winter time. And this year the temperature was unusually cold from the very beginning. Soon one noticed that three quarters of the stored wood has been already used in just one quarter of the winter time. What to do? Where would he get more firewood? And how would he get it if he has no money left to buy any more wood?
This "story" is a simple model of what has happened all around the world when the four major changes in environment of the human society made old and well established ways of governing obsolete, outdated, not working for majority but only for a few. As described in the main part of this piece, the rise of the authoritarian leaders is an inevitable consequence of the large disbalance between the system and the environment. What we see very clearly is how the property of inertia manifests itself in the solutions proposed by major political forces. The Republicans bet on the trickle-down economics, when large tax cuts to big corporations will boos the economy. It may work in a short term but will never work in a long run. Just a year after the large tax cut of 2016 numbers show that there is only one major improvement for the middle class Americans, namely, a very low unemployment rate. It means that people who had been out of work now have a stable income. But that income for them, as well as for the majority of the working Americans, practically does not grow. The gains in productivity and profitability go to a very narrow layer of rich Americans. However, the trickle-down economics will not solve the problems of the growing deficit, the upcoming cuts in social security and even pensions. This will lead to a social explosion, which may lead to an actual dictatorship as the reaction to another false promise from the politicians and the government.
The Democrats promote the solution based on tax increase for the rich. But for too many Americans phrase "let's raise taxes" is a no go, it goes against their culture, and too many people simply believe that raising taxes will inevitably hurt the economy. And it is not entirely wrong, because a mere governmental redistribution of money will force business into hiding their funds, moving to tax havens, corruption, and also will lead to the economic slowdown. Which again will lead to a social explosion, which may lead to an actual dictatorship as the reaction to another false promise from the politicians and the government. But the Democrats have no other ideas.
Until the new generation of politicians, people who can think beyond old economic theories, grows up, the attraction of the populous to a strong leader will only grow. Hopefully, eventually a strong leader with the new economic views, and also with the deep belief in a democracy, will rise from the "political mob" and will lead the transformation of the society to the new stable economic and political state.
But that will require decades of time. 
Appendix III
Well, dear Reader, if you have read everything above, I would strongly recommend to read this piece, as well: "The Degradation of White Male American Elite".  

Monday, October 29, 2018

The Definition of Machine Learning

Lately, many popular online publications have been trying to explain to the populous what "Machine Learning" is.
That moved me to extract the following text from the main article "On The Definition of AI" and post it as a separate piece.
There are many definitions of "Machine Learning".
For example, “Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.”
None of those "definitions" are definitions. They merely describe the scientific field of machine learning but do not define machine learning. Mostly because they do not define "learning". 
If they did, the rest would be obvious. 
This is the definition of Machine Learning: "machine learning it is what people do when they learn, but this time it is done by an artificially made object, i.e. by a machine". After a short general introduction most of the authors offer some version of a brief description of specific methods used to process different patterns (could be found in any textbook on AI or ML).
The true goal is to define "learning".
"Learning" also has many definitions. The most common one (which comes in various forms) is "learning is the process of the acquisition of knowledge", or "the knowledge obtained during the processes of learning". Both definitions are correct, in their way, because they do describe learning. But that type of learning is not the type AI professionals have in mind when they say "machine learning". Those two definitions do not allow to establish if learning has actually occurred beyond mere memorization (a.k.a. "acquisition of knowledge"). Machine learning as a memorization is clearly of no interest, because these days we all know very well that machines can accumulate, store and retrieve huge amount of information. Of course, the algorithms, techniques for acquiring and processing that information represent important technical part of machine learning.But that part has little to do with the actual process called "learning". Even blind and deaf people can learn to the highest level (up to getting PhD).
As an expert in human intelligence, I define "learning" (more accurately, "productive learning") as a processes leading to a production of knowledge; as the first approximation (the scientific thinking in action), learning is a process of utilization of currently active knowledge in order to produce new knowledge (for example, the statement "I learned how to do it" represents some of the new knowledge developed during learning). The criterion of "learning" ("actual learning", "real learning", "true learning", "productive learning") is the ability to use existing knowledge to generate knowledge previously not available to the actor of learning. Machine learning is happening when a machine (an artificially manufactured object) produces new knowledge based on the knowledge currently available to the machine. 
BTW: what is "knowledge"? Without an operational definition of "knowledge" how do we know if the new knowledge has been produced? If a machine takes a text and randomly permute and recombine letters, words, sentences will it be "new knowledge"?. 
More importantly, what types of knowledge exist? how does knowledge evolve? what is the structure of knowledge? how is the structure of knowledge reflected in the structure of neural network processing that knowledge? People in AI don't seem interested in those questions. At least there is no single page from 1100 pages of "Artificial Intelligence: A modern Approach" (by Stuart J. Russell, Peter Norvig; 3d edition) where those questions about knowledge would be discussed. They talk about "knowledge" as if it is something obvious, or define "knowledge" as "information", which is a severe simplification, in part because it ignores an important feature of "knowledge" - it has a vector; it is purposeful (in general).  They define learning as making a match between a hypothetical knowledge and the factual knowledge (meaning "information").  This does formally describe a procedure leading to "new knowledge": (1) state a hypothesis; (2) gather facts; (3) compare; (4) decide. For example: (1) this is a banana? (2) run image recognition; (3) correlation 0.98; (4) ye, that is a banana! (if needed, e.g. to decrease % of mistakes, learning can be "reinforced", and "deepened"). But (A) for people true learning usually begins after learning how to recognize various shapes; (B) this learning ignores "learning as a skill development"; (C) and also it ignores the central feature of learning - its intentionality (humans have a desire to learn, including about themselves, built into the genetic code; good teaching is based on that; bad teaching ignores or even tries to break this desire).
Finally, since the ultimate mission of learning is progress:
(1) acquisition of knowledge is useless if it does not lead to the development of new practice (starting from the development of new individual skills).
(2) the development of new practice (starting from the development of new individual skills) always lead beyond acquisition of knowledge to development of new knowledge.
That means that AI developers also need to define "skills", "new skills", "machine skills", "skill development", etc., in a way assessable for a machine and by a machine.
Machine learning is happening when a machine (an artificially manufactured object) develops new skill based on the skills currently available to the machine.
When current AI recognizes a pattern (visual, audio, numerical) it only makes a statement in the form "yes - that is that thing", "no - that is not that thing". But the processes in the network which lead to the final statement also have their own patterns. In a brain, there is at least one another network of a higher level which analyzes and recognizes the patterns happening in the lower network making a decision about the pattern/object. That higher level network generates another signal - a doubt - "are your sure"? And then there is another network which makes another decision "yes, I am sure" or "no, I am not sure". And then ... - long story, but you see the pattern.
No AI is even close to mirror this type of pattern/pattern/pattern recognition (that requires developing the hierarchy of networks analyzing the hierarchy of patterns).
That is why I also added another post on the matter: "Relax, the real AI is not coming any soon" (that post also has some insights on what "common sense" is).
P.S. The field of AI training will become much more important than it is today.
Although, not many AI professionals see it so far.
For more on AI:

Thank you for visiting,
Dr. Valentin Voroshilov
Education Advancement Professionals

To learn more about my professional experience:
The voices of my students 
"The Backpack Full of Cash": pointing at a problem, not offering a solution
Essentials of Teaching Science

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