Abstraction
Could computers understand and represent irrational numbers without knowing their exact values? Humans can. The measurement of generic intelligence is critical to further development of artificial intelligence (AI). However, the existing methods: Turing Test and its variants cannot really measure intrinsic intelligence capabilities. Based on the studies of knowledge development, several essential design goals for intelligence measurement are identified. A new method: Gu Test is proposed, to distinguish strong AI and regular machines, and meet some of these design goals. Further improvement could be done in future.
1. The Measurement of Generic Intelligence
Measurement is so important in sciences and technologies. Just as clocks are necessary to advanced studies of motion and speed, centrifugal governors are critical to make steam engines usable.
The measurement of generic intelligence is also important to artificial intelligence (AI). However, the existing measuring methods, such as Turing Test and its variants, are mainly behavior-based, knowledge-based, or task-based, etc., which cannot really measure intrinsic intelligence capabilities.
A new way of measurement : Gu Test, is proposed, to distinguish strong AI from regular machines.
2. Turing Test and Chinese Room concern
Alan Turing described an imitation game in his paper Computing Machinery and Intelligence [1], which tests whether a human could distinguish a computer from another human only via communication without seeing each other.
It is a black box test, purely based on behavior. To pass this kind of tests, computers only need to imitate humans.
So John Seale raised a Chinese Room issue [2], i.e., computers could pass this test by symbolic processing without really understanding the meanings of these symbols.
Also, Turing Test uses interrogation to test, so it only can test those human characteristics which already be understood well by humans and can be expressed in communication. Humans still have very limited understanding of life, psychology, and intelligence. So those intrinsic intelligence abilities which humans do not understand well yet could not be tested only via interrogation.
3. Variants of Turing Test
There are several variants of Turing Test which aim at improving on it.
One is Feigenbaum test. According to Edward Feigenbaum, "Human intelligence is very multidimensional", "computational linguists have developed superb models for the processing of human language grammars. Where they have lagged is in the 'understand' part", "For an artifact, a computational intelligence, to be able to behave with high levels of performance on complex intellectual tasks, perhaps surpassing human level, it must have extensive knowledge of the domain." [3].
There are two issues in this test. One is current computers only can store and process knowledge in some data forms. If some knowledge cannot be represented as data, then the "understanding part" of such knowledge would not be solved. The other issue is whether extensive knowledge is necessary to test strong AI, since individual humans may not have very extensive knowledge in certain domains.
Feigenbaum test is actually a very good method to measure expert systems. However, to measure generic AI or test strong AI, the essentials of "understanding part" need be identified. Whether they could be transformed into data and data processing should be parts of testing. Gu Test actually tries to solve these issues.
Another variant is Shane Legg and Marcus Hutter's solution [4], which is actually agent-based. In their framework, an agent sends its actions to the environment and received observations and rewards from it. If their framework is used to test strong AI, then it assumes that all the interactions between humans and their environment could be modeled by actions/observations/rewards. This assumption has not been tested.
Humans can play some roles of agents, but they are not just agents. Humans could make paradigm evolution, which usually means gain deeper observations, take better actions, and gain more rewards than what already in any definitions.
Actions/observations/rewards could be defined for specific tasks and agents. But how could these be defined for paradigm evolution, and for the whole life of humans ? Humans could make exceptions and innovations. Without these, there would be no Euclid, Galileo and Columbus, etc.
There are some essential parts of intrinsic intelligence which are not in their framework. Much more research could be done to further study the difference between humans and agents.
Even if Turing Test is enhanced with vision and manipulation ability, or with methods like statistical inference, etc., it still does not test the difference between knowledge and data, between simulated behavior and intrinsic intelligence, etc.
4. The Design Goals for Generic Intelligence Measurement
Based on the analysis done in previous sections, some design goals are proposed here:
1) Resolve Chinese Room issue, i.e., to test the real understanding, not just behavior imitating or symbolic processing.
2) Not just rely on interrogation. Find some ways to test those intrinsic intelligence abilities which have not been understood and expressed well.
3) Test those concepts, knowledge and intelligence which cannot be represented as data yet.
4) Involve as less domain knowledge as possible, since regular humans may not have much knowledge in specific domains.
5) Include those intrinsic capabilities commonly necessary in many domains, with which humans can develop intelligence in many domains.
6) Include a sequence of leveled tests, since humans are able to make continuous progresses in intelligence.
7) Include a framework to test structured intelligence and be able to make paradigm evolution, since humans can develop sophisticated knowledge structures and make paradigm evolution.
5. Gu Test
Based on these design goals, Gu Test is proposed. It should include a sequence of test levels, and be able to test structured intelligence and make paradigm shift.
However, currently only a first test step is suggested, to meet the goals from 1) to 5). The work to meet the design goals 6) and 7) will be left to future researches.
The first test step of Gu Test is : to test whether testees could understand irrational numbers without knowing their exact values. It is a white box test. Average humans with certain basic education can. Current computers most likely cannot.
It tests the real understanding; It does not rely on interrogation, but tests some intrinsic ability; Humans can pass this test, but they probably do not know why they have this ability yet; It tests some concepts and knowledge which cannot be represented as data; Irrational number is a primitive concept developed in Pythagoras' age, who is a poineer in philosophy and mathematics; The concept is necessary to so many domains, but involves very little domain-specific knowledge.
6. Future Research
Much more work need be done to extend Gu Test to meet the design goals 6) and 7). To really understand the essentials of intelligence, people have to study the history of knowledge development, philosophy, mathematics, sciences, etc.
References
[1] Turing, A. M., 1950, "Computing machinery and intelligence". Mind 59, 433–460.
[2] Searle, John. R., 1980, "Minds, brains, and programs". Behavioral and Brain Sciences 3 (3): 417-457.
[3] Feigenbaum, Edward A., 2003, "Some challenges and grand challenges for computational intelligence". Journal of the ACM 50 (1): 32–40.
[4] Legg, S. & Hutter, M., 2006, "A Formal Measure of Machine Intelligence”, Proc. 15th Annual Machine Learning Conference of Belgium and The Netherlands, pp.73-80.
Wednesday, December 28, 2011
Tuesday, December 13, 2011
Different Approaches For Knowledge System Development (version 2)
1) Introduction
Could computers measure and represent irrational numbers ? This question relates to some essentials of human intelligence. To understand this question, people have to retrace the whole history of philosophy and mathematics, back to Pythagoras age.
Humans with certain education could understand irrational numbers without knowing their exact values. Turing Test is not a good measurement for intelligence. The author of this article constructs Gu Test, which could test intelligence levels better.
This article is not a complete review of philosophies and sciences. It only addresses the essentials and methodologies for knowledge development, for the interests of artificial intellignece and education, etc. So although Dmitri Ivanovich Mendeleev and Albert Einstein are extremely important scientists, they are not discussed here since they essentially followed the classic scientific approach: Galileo-Newton approach.
It does not try to cross the boundary between knowledge and religions. Only religious rites are mentioned.
By approach, it means coherent approach in this paper. Coherence does not guarantee correctness. However, incohenrence is always prone to problems and errors.
2) Various Approaches
The difficulties in artificial intelligence (AI) researches challenge the limits of classic scientific approaches. It would be helpful to review the history how people used various ways to develop knowledge.
Several approaches from ancient time are identified here. They are: Pythagoras-Plato approach; Socrates-Stoicism approach; Euclid-Archimedes approach; Yi-Jing approach from ancient China; approaches from ancient India and other countries, etc.
In theoretic research of AI, there is a very important issue: the difference between Euclid's constructive approach and many of modern mathematics. Say, could people construct irrational numbers on computers ?
Some approaches from medieval age played important roles in sciences: they are Ibn al-Haytham's approach, Al-Biruni's approach, and Avicenna's approach, etc.
Galileo-Newton approach, the foundation of current sciences, evolved from Euclid-Archimedes and Ibn al-Haytham approaches. But there are differences between them. Some non-classic approaches from Charles Darwin, Adam Smith, Sigmund Freud, etc., are also different from Galileo-Newton approach.
The following sections will first discuss the strength and limitations of the first three ancient approaches, the medieval approaches, then Galileo-Newton approach. Several non-classic approaches and theoretic issues important to AI will also be discussed. Gu Test is proposed to measure intelligence levels based on these studies.
The ancient approaches from Yi-Jing, India, and other countries, would be discussed in separate articles, if possible.
3) Pythagoras-Plato Approach
Thales is a pioneer in mathematical proof. Egyptians and Babylonians might know Thales Theorem before him, but he was likely the first one providing a valid proof for it.
Although Thales tried to explain natural phenomena not based on mythology, he is a Hylozoist who believed everything is alive, and there is no difference between the living and the dead. He did not develop a coherence approach, but had significant influences on Pythagoras.
The first philosopher should be Pythagoras, who built a coherent systematic view. He formed a school of scholars to study philosophy, mathematics, music, etc.
Pythagorean are famous for Pythagorean Theorem. They are pioneers in mathematics, a systematic study. They also proposed a non-geocentric model that the Earth runs around a central fire which suggests both the Sun and the Earth are not the center of universe. They might develop or formulate the idea the Earth is round.
Pythagoras also taught religious rites and practices in his school, so came his beliefs. He and Plato believed there be a perfect and persistent abstract world, and an imperfect and sensible world. They pursued the beauty of abstract perfection. Plato followed this philosophy and developed it into maturity.
Pythagorean made many early contributions to knowledge. They tried to construct complicated things with simpler ones. They believed whole numbers be simple and perfect, and tried to represent all numbers with quotient of two whole numbers. Here they faced the first mathematical crisis: some, actually most of the numbers, cannot be expressed in such a way. They called these irrational numbers.
This discloses the problems of Pythagoras-Plato approach: the way they construct or interpret mathematics may not fit into the reality. The beauty of mathematics may not be able to explain an sensible world. Constructivism is important. But how to construct ?
This has a consequence for AI: how could people construct irrational numbers on computers, or could computers distinguish irrational numbers from rational numbers?
The issue of irrational numbers is actually related to measurement. Euclid described it in a better way as mentioned in Section 5, which is not understood by many modern mathematicians. Measurement is again associated to nonlinear, chaos or deterministic uncertainty, etc. So people better think of it as a tip of an iceberg, rather than a solved problem which they could forget about it.
4) Socrates-Stoicism approach
Socrates led different beliefs. He did not take the beauty of abstraction as a doctrine. There is a Socratic method. He asked people to question each other to find the problems. When people discuss their arguments explicitly, they could find the problems and understand them better.
Socrates asked many questions, but did not give many answers. This might be a good attitude. Socrates taught by playing a role model. By admitting his ignorance, he suggested other people also to admit their ignorance.
Admitting their ignorance is not beautiful, not even pleasant, but an extremely critical step to make further progresses. However, this attitude is offensive to many people. Socrates was voted to death eventually, probabily under accumulated anger from others.
Then Socrates played a role model again by accepting the death to show the rule of laws.
Also, "Aristotle attributed to Socrates the discovery of the method of definition and induction, which he regarded as the essence of the scientific method." (from wikipedia).
Socrates promoted rationale and ethics, which was followed by Cynicism and Stoicism. However, the strange behaviors of Cynicism actually showed the frustrations faced by this approach: they did not find effective ways to discover much more knowledge. This task would be achieved by Euclid-Archimedes, Ibn al-Haytham, and Galileo approaches later.
Sophism was an enemy to Socrates, and is also an enemy to future sciences. It does not provide a coherent approach.
Aristotelianism is not coherent, too. Although Aristotle adored Socrates and claimed he was against sophism, his way is actually a mixture of Socrates, Plato and sophism without coherence. So he included assertions like a flying arrow is at rest in his book.
His syllogism is an unsuccessful summarization and simulation of the methods in mathematical proofs. Aristotle did not know how the logic really works in mathematics. So he missed a very important factor: how to make the premises in syllogism valid and concrete, i.e. the first principle.
Since Aristotle did not know how to apply logic and reasoning correctly, he did not know how to build coherent theories. His book The Physics brought little values to physics, but many misleadings. He just put togather whatever he knew or believed into huge collections without paying attention to coherence.
Aristotle was actually a naturalist. He made some contributions to zoological taxonomy based on observation. He is not the first one using observation. And he does not have a coherent approach.
5) Euclid-Archimedes Approach
Euclid is the first one who established a concrete systematic theory for a domain. He is more like a scientist, than a pure mathematician.
He did not concern much of the beauty of abstraction. In the proof of the number of prime numbers, he used the word "measure", instead of a number divides another number. Many modern mathematicians think not being abstract enough is Euclid's limitation.
However, Euclid using measurement for division, implies the accurate value of irrational numbers cannot be measured by rational numbers. This becomes a problem in modern computer sciences: how could a computer represent irrational numbers and distinguish them from rational numbers ?
Measurability is still a critical problem in modern physics and AI. So, this usage is not Euclid's limitation, but his insight. He caught the essentials of the problems.
He usually constructed solutions rather than just proving the existence of unknown solutions. His system is incredible concrete after more than two thousand years, much more concrete than many of modern mathematics.
He constructed the geometry system by some simple axioms, and derived other theorems from these axioms. Although this looks like Pythagorean's way to construct complicated numbers by simpler whole numbers, they are different.
Euclid guarantees the correctness of derived theorems by making axioms simple and straightforward, thus self-evident. So Euclid solved the first principle issue in certain extent. Pythagorean did not show why whole numbers could be used to express all numbers, they just believed it is the beauty. Euclid's geometry is a good example of correct logic.
There are still limitations of Euclid's approach. It mainly works in mathematics, and cannot find all theorems and laws in real needs. When Euclid worked on optics, his approach for first principle faced a problem: he did not have justified reasons to choose emission theory instead of intromission theory, although no justified reasons to make the opposite choice at that time, too. These limitations are partially solved by Ibn al-Haytham approach and Galileo-Newton approach, only partially.
Euclid was truly thought as a scientist in early days. Only after Galileo founded Physics, Euclid retired from scientists, and became a mathematician only.
Archimedes, one of the greatest engineers, was highly influenced by Euclid.
6) Medieval approaches
Pythagoras is very insightful in mathematics, philosophy, music, religious practices, etc. Plato developed the philosophy in his style into maturity. Socrates and Stoicism knew the way to develop rationale and ethics. Euclid and Archimedes designed theoretic and real systems in rigorous forms.
They all made big contributions to knowledge development, and still have big influences so far, but also with problems. Their accomplishments are still very limited. It is some medieval scholars who brought in some new factors critical to future sciences.
Ibn al-Haytham used some procedure to do scientific research: observe, form conjectures, Testing and/or criticism of a hypothesis using experimentation, etc. He used this procedure to prove intromission theory. However, this procedure is not a complete scientific approach. He can only use it to reach individual results, still under Euclid's geometry view.
Although he did some brilliant work in optics, due to this geometry view and his way of thinking, he cannot gain deep and comprehensive understanding of physics.
Al-Biruni put an emphasis on experimentation. He tried to conceptualize both systematic errors and observational biases, and used repeated experiments to control errors. He might be a pioneer of comparative sociology.
Avicenna discussed the philosophy of science and summarized several methods to find a first principle. He developed a theory to distinguish the inclination to move and the force. He discussed the soul and the body, the perceptions. Probably he is a very important scholar misunderstood by many modern people.
Only after their efforts, big scientific progresses became possible.
7) Galileo-Newton Approach
Although Nicolaus Copernicus started the new age of sciences, he (and Johannes Kepler) still followed Euclid-Archimedes approach, the geometry view.
It is Galileo Galilei who formed many substantial understanding of the physical world and triggered a scientific revolution with his comprehensive and systematic thinking. Without such a way to think, people cannot build a new world theory from individual isolated conclusions. So his approach is different from Ibn al-Haytham's method. Isaac Newton developed his theories based on Galileo's work. This classic scientific approach is named as Galileo-Newton approach.
There are still limitations in this approach. It does not work well in AI, psychology, economics, etc. Those fields relate to humans. Measurability is still a big concern. There are even problems in Newton's four rules of reasoning stated in his Mathematical Principles of Natural Philosophy.
Actually, many important knowledge systems were not built with this approach.
8) Non-Classic Approaches
Although as great academic contributors, Charles Darwin, Adam Smith, and Sigmund Freud built their theories not with Galileo-Newton approach. These three theories are in the descending order of closeness to sciences. No surprise, their theories relate to humans.
Epicurus, Arthur Schopenhauer, Friedrich Nietzsche illustrated many important ideas, also related to human natures. Their ways are different from classic approaches, either.
9) Inadequate Summarizing Efforts
Many people tried to summarize the knowledge systems, such as: René Descartes, David Hume, Immanuel Kant, etc.
Georg Wilhelm Friedrich Hegel, Karl Marx, Bertrand Russell even made more ambitious efforts.
Just they all missed some or many important aspects.
10) Measurement and Constructivism
Turing Test is not a right way to test the intelligence of computers. It is superficial and too subjective. Instead, Gu Test is designed to better measure the intelligence levels of computers.
Gu Test contains various tests for different intelligence levels. One necessary, but not sufficient test for strong AI is : could computers understand the real meaning of irrational numbers, measure their values, and represent them in their memory ?
Humans with certain education could understand what irrational numbers are and what PI is although they do not know the exact values of these numbers. Some experts could do certain accurate measurement for them.
So Gu Test could test some essentials of intelligence, and avoid the so-called Chinese Room issue.
In the physical world, measuring does not mean knowing the exact value. This is a difference between mathematics and physics. That is why "after Galileo founded Physics, Euclid retired from scientists, and became a mathematician only".
11) Gödel Theorems and the Limitations of Mathematics and Positivism
Gödel theorems illustrate the intrinsic problems in mathematics systems beyond certain complexity. There are foundational crisis in mathematics as in http://en.wikipedia.org/wiki/Foundations_of_mathematics#Foundational_crisis.
More important is, mathematics cannot explain all the potentials in reality.
Positivism is an ideal goal for Galileo-Newton approach. However, as said, Charles Darwin, Adam Smith, Sigmund Freud and many others built their theories and systems not strictly with this approach.
In the whole knowledge system, Positivism is more like a Utopia, rather than a reality. Even so, as one of important doctrines in sciences, Positivism should not be ignored, especially in the conclusion stage.
12) Main Contributions
The main contributions of this article are:
1) Identify an important intelligence phenomena: humans with certain education could understand irrational numbers without knowing their exact values.
2) Construct a Gu Test to better measure intelligence levels based on 1).
3) Show Turing Test is not a good measurement for Strong AI based on 2).
4) Clarify Galileo-Newton approach is different from Ibn al-Haytham's method.
5) Claim there are limitations and problems in Newton's four rules of reasoning stated in his Mathematical Principles of Natural Philosophy.
Please correct me if I am wrong.
13) Future Researches
To be explored and explained in future.
Could computers measure and represent irrational numbers ? This question relates to some essentials of human intelligence. To understand this question, people have to retrace the whole history of philosophy and mathematics, back to Pythagoras age.
Humans with certain education could understand irrational numbers without knowing their exact values. Turing Test is not a good measurement for intelligence. The author of this article constructs Gu Test, which could test intelligence levels better.
This article is not a complete review of philosophies and sciences. It only addresses the essentials and methodologies for knowledge development, for the interests of artificial intellignece and education, etc. So although Dmitri Ivanovich Mendeleev and Albert Einstein are extremely important scientists, they are not discussed here since they essentially followed the classic scientific approach: Galileo-Newton approach.
It does not try to cross the boundary between knowledge and religions. Only religious rites are mentioned.
By approach, it means coherent approach in this paper. Coherence does not guarantee correctness. However, incohenrence is always prone to problems and errors.
2) Various Approaches
The difficulties in artificial intelligence (AI) researches challenge the limits of classic scientific approaches. It would be helpful to review the history how people used various ways to develop knowledge.
Several approaches from ancient time are identified here. They are: Pythagoras-Plato approach; Socrates-Stoicism approach; Euclid-Archimedes approach; Yi-Jing approach from ancient China; approaches from ancient India and other countries, etc.
In theoretic research of AI, there is a very important issue: the difference between Euclid's constructive approach and many of modern mathematics. Say, could people construct irrational numbers on computers ?
Some approaches from medieval age played important roles in sciences: they are Ibn al-Haytham's approach, Al-Biruni's approach, and Avicenna's approach, etc.
Galileo-Newton approach, the foundation of current sciences, evolved from Euclid-Archimedes and Ibn al-Haytham approaches. But there are differences between them. Some non-classic approaches from Charles Darwin, Adam Smith, Sigmund Freud, etc., are also different from Galileo-Newton approach.
The following sections will first discuss the strength and limitations of the first three ancient approaches, the medieval approaches, then Galileo-Newton approach. Several non-classic approaches and theoretic issues important to AI will also be discussed. Gu Test is proposed to measure intelligence levels based on these studies.
The ancient approaches from Yi-Jing, India, and other countries, would be discussed in separate articles, if possible.
3) Pythagoras-Plato Approach
Thales is a pioneer in mathematical proof. Egyptians and Babylonians might know Thales Theorem before him, but he was likely the first one providing a valid proof for it.
Although Thales tried to explain natural phenomena not based on mythology, he is a Hylozoist who believed everything is alive, and there is no difference between the living and the dead. He did not develop a coherence approach, but had significant influences on Pythagoras.
The first philosopher should be Pythagoras, who built a coherent systematic view. He formed a school of scholars to study philosophy, mathematics, music, etc.
Pythagorean are famous for Pythagorean Theorem. They are pioneers in mathematics, a systematic study. They also proposed a non-geocentric model that the Earth runs around a central fire which suggests both the Sun and the Earth are not the center of universe. They might develop or formulate the idea the Earth is round.
Pythagoras also taught religious rites and practices in his school, so came his beliefs. He and Plato believed there be a perfect and persistent abstract world, and an imperfect and sensible world. They pursued the beauty of abstract perfection. Plato followed this philosophy and developed it into maturity.
Pythagorean made many early contributions to knowledge. They tried to construct complicated things with simpler ones. They believed whole numbers be simple and perfect, and tried to represent all numbers with quotient of two whole numbers. Here they faced the first mathematical crisis: some, actually most of the numbers, cannot be expressed in such a way. They called these irrational numbers.
This discloses the problems of Pythagoras-Plato approach: the way they construct or interpret mathematics may not fit into the reality. The beauty of mathematics may not be able to explain an sensible world. Constructivism is important. But how to construct ?
This has a consequence for AI: how could people construct irrational numbers on computers, or could computers distinguish irrational numbers from rational numbers?
The issue of irrational numbers is actually related to measurement. Euclid described it in a better way as mentioned in Section 5, which is not understood by many modern mathematicians. Measurement is again associated to nonlinear, chaos or deterministic uncertainty, etc. So people better think of it as a tip of an iceberg, rather than a solved problem which they could forget about it.
4) Socrates-Stoicism approach
Socrates led different beliefs. He did not take the beauty of abstraction as a doctrine. There is a Socratic method. He asked people to question each other to find the problems. When people discuss their arguments explicitly, they could find the problems and understand them better.
Socrates asked many questions, but did not give many answers. This might be a good attitude. Socrates taught by playing a role model. By admitting his ignorance, he suggested other people also to admit their ignorance.
Admitting their ignorance is not beautiful, not even pleasant, but an extremely critical step to make further progresses. However, this attitude is offensive to many people. Socrates was voted to death eventually, probabily under accumulated anger from others.
Then Socrates played a role model again by accepting the death to show the rule of laws.
Also, "Aristotle attributed to Socrates the discovery of the method of definition and induction, which he regarded as the essence of the scientific method." (from wikipedia).
Socrates promoted rationale and ethics, which was followed by Cynicism and Stoicism. However, the strange behaviors of Cynicism actually showed the frustrations faced by this approach: they did not find effective ways to discover much more knowledge. This task would be achieved by Euclid-Archimedes, Ibn al-Haytham, and Galileo approaches later.
Sophism was an enemy to Socrates, and is also an enemy to future sciences. It does not provide a coherent approach.
Aristotelianism is not coherent, too. Although Aristotle adored Socrates and claimed he was against sophism, his way is actually a mixture of Socrates, Plato and sophism without coherence. So he included assertions like a flying arrow is at rest in his book.
His syllogism is an unsuccessful summarization and simulation of the methods in mathematical proofs. Aristotle did not know how the logic really works in mathematics. So he missed a very important factor: how to make the premises in syllogism valid and concrete, i.e. the first principle.
Since Aristotle did not know how to apply logic and reasoning correctly, he did not know how to build coherent theories. His book The Physics brought little values to physics, but many misleadings. He just put togather whatever he knew or believed into huge collections without paying attention to coherence.
Aristotle was actually a naturalist. He made some contributions to zoological taxonomy based on observation. He is not the first one using observation. And he does not have a coherent approach.
5) Euclid-Archimedes Approach
Euclid is the first one who established a concrete systematic theory for a domain. He is more like a scientist, than a pure mathematician.
He did not concern much of the beauty of abstraction. In the proof of the number of prime numbers, he used the word "measure", instead of a number divides another number. Many modern mathematicians think not being abstract enough is Euclid's limitation.
However, Euclid using measurement for division, implies the accurate value of irrational numbers cannot be measured by rational numbers. This becomes a problem in modern computer sciences: how could a computer represent irrational numbers and distinguish them from rational numbers ?
Measurability is still a critical problem in modern physics and AI. So, this usage is not Euclid's limitation, but his insight. He caught the essentials of the problems.
He usually constructed solutions rather than just proving the existence of unknown solutions. His system is incredible concrete after more than two thousand years, much more concrete than many of modern mathematics.
He constructed the geometry system by some simple axioms, and derived other theorems from these axioms. Although this looks like Pythagorean's way to construct complicated numbers by simpler whole numbers, they are different.
Euclid guarantees the correctness of derived theorems by making axioms simple and straightforward, thus self-evident. So Euclid solved the first principle issue in certain extent. Pythagorean did not show why whole numbers could be used to express all numbers, they just believed it is the beauty. Euclid's geometry is a good example of correct logic.
There are still limitations of Euclid's approach. It mainly works in mathematics, and cannot find all theorems and laws in real needs. When Euclid worked on optics, his approach for first principle faced a problem: he did not have justified reasons to choose emission theory instead of intromission theory, although no justified reasons to make the opposite choice at that time, too. These limitations are partially solved by Ibn al-Haytham approach and Galileo-Newton approach, only partially.
Euclid was truly thought as a scientist in early days. Only after Galileo founded Physics, Euclid retired from scientists, and became a mathematician only.
Archimedes, one of the greatest engineers, was highly influenced by Euclid.
6) Medieval approaches
Pythagoras is very insightful in mathematics, philosophy, music, religious practices, etc. Plato developed the philosophy in his style into maturity. Socrates and Stoicism knew the way to develop rationale and ethics. Euclid and Archimedes designed theoretic and real systems in rigorous forms.
They all made big contributions to knowledge development, and still have big influences so far, but also with problems. Their accomplishments are still very limited. It is some medieval scholars who brought in some new factors critical to future sciences.
Ibn al-Haytham used some procedure to do scientific research: observe, form conjectures, Testing and/or criticism of a hypothesis using experimentation, etc. He used this procedure to prove intromission theory. However, this procedure is not a complete scientific approach. He can only use it to reach individual results, still under Euclid's geometry view.
Although he did some brilliant work in optics, due to this geometry view and his way of thinking, he cannot gain deep and comprehensive understanding of physics.
Al-Biruni put an emphasis on experimentation. He tried to conceptualize both systematic errors and observational biases, and used repeated experiments to control errors. He might be a pioneer of comparative sociology.
Avicenna discussed the philosophy of science and summarized several methods to find a first principle. He developed a theory to distinguish the inclination to move and the force. He discussed the soul and the body, the perceptions. Probably he is a very important scholar misunderstood by many modern people.
Only after their efforts, big scientific progresses became possible.
7) Galileo-Newton Approach
Although Nicolaus Copernicus started the new age of sciences, he (and Johannes Kepler) still followed Euclid-Archimedes approach, the geometry view.
It is Galileo Galilei who formed many substantial understanding of the physical world and triggered a scientific revolution with his comprehensive and systematic thinking. Without such a way to think, people cannot build a new world theory from individual isolated conclusions. So his approach is different from Ibn al-Haytham's method. Isaac Newton developed his theories based on Galileo's work. This classic scientific approach is named as Galileo-Newton approach.
There are still limitations in this approach. It does not work well in AI, psychology, economics, etc. Those fields relate to humans. Measurability is still a big concern. There are even problems in Newton's four rules of reasoning stated in his Mathematical Principles of Natural Philosophy.
Actually, many important knowledge systems were not built with this approach.
8) Non-Classic Approaches
Although as great academic contributors, Charles Darwin, Adam Smith, and Sigmund Freud built their theories not with Galileo-Newton approach. These three theories are in the descending order of closeness to sciences. No surprise, their theories relate to humans.
Epicurus, Arthur Schopenhauer, Friedrich Nietzsche illustrated many important ideas, also related to human natures. Their ways are different from classic approaches, either.
9) Inadequate Summarizing Efforts
Many people tried to summarize the knowledge systems, such as: René Descartes, David Hume, Immanuel Kant, etc.
Georg Wilhelm Friedrich Hegel, Karl Marx, Bertrand Russell even made more ambitious efforts.
Just they all missed some or many important aspects.
10) Measurement and Constructivism
Turing Test is not a right way to test the intelligence of computers. It is superficial and too subjective. Instead, Gu Test is designed to better measure the intelligence levels of computers.
Gu Test contains various tests for different intelligence levels. One necessary, but not sufficient test for strong AI is : could computers understand the real meaning of irrational numbers, measure their values, and represent them in their memory ?
Humans with certain education could understand what irrational numbers are and what PI is although they do not know the exact values of these numbers. Some experts could do certain accurate measurement for them.
So Gu Test could test some essentials of intelligence, and avoid the so-called Chinese Room issue.
In the physical world, measuring does not mean knowing the exact value. This is a difference between mathematics and physics. That is why "after Galileo founded Physics, Euclid retired from scientists, and became a mathematician only".
11) Gödel Theorems and the Limitations of Mathematics and Positivism
Gödel theorems illustrate the intrinsic problems in mathematics systems beyond certain complexity. There are foundational crisis in mathematics as in http://en.wikipedia.org/wiki/Foundations_of_mathematics#Foundational_crisis.
More important is, mathematics cannot explain all the potentials in reality.
Positivism is an ideal goal for Galileo-Newton approach. However, as said, Charles Darwin, Adam Smith, Sigmund Freud and many others built their theories and systems not strictly with this approach.
In the whole knowledge system, Positivism is more like a Utopia, rather than a reality. Even so, as one of important doctrines in sciences, Positivism should not be ignored, especially in the conclusion stage.
12) Main Contributions
The main contributions of this article are:
1) Identify an important intelligence phenomena: humans with certain education could understand irrational numbers without knowing their exact values.
2) Construct a Gu Test to better measure intelligence levels based on 1).
3) Show Turing Test is not a good measurement for Strong AI based on 2).
4) Clarify Galileo-Newton approach is different from Ibn al-Haytham's method.
5) Claim there are limitations and problems in Newton's four rules of reasoning stated in his Mathematical Principles of Natural Philosophy.
Please correct me if I am wrong.
13) Future Researches
To be explored and explained in future.
Friday, October 21, 2011
The Development of Knowledge System
This is a series of studies of knowlede system development, which is fundamental to artificial intelligence(AI) and education. Both AI and education lack of good theories so far.
Based on the results of these studies, the studies will evolve into two branches later. One from sciences aspect: the dynamics of intelligence; one from humanity aspect: educational psychology. These two branches will be highly related to each other in future, although not yet now.
A proposal for the studies of dynamics of intelligence is available at: The Proposal for A Three-level Intelligence Model
Educational psychology does not have concrete systematic results for people beyond teenage. Some breakthrough could be expected if sufficient resources are available for this series of studies.
The first part of this is : The Different Approaches for Knowledge System Development.
It is not just history interests, but a critical understanding of how knowledge systems were, are, and will be developed.
Based on the results of these studies, the studies will evolve into two branches later. One from sciences aspect: the dynamics of intelligence; one from humanity aspect: educational psychology. These two branches will be highly related to each other in future, although not yet now.
A proposal for the studies of dynamics of intelligence is available at: The Proposal for A Three-level Intelligence Model
Educational psychology does not have concrete systematic results for people beyond teenage. Some breakthrough could be expected if sufficient resources are available for this series of studies.
The first part of this is : The Different Approaches for Knowledge System Development.
It is not just history interests, but a critical understanding of how knowledge systems were, are, and will be developed.
The Different Approaches For Knowledge System Development
In theoretic research of artificial intelligence, there is a very important issue: the difference between Euclid's constructive approach and many modern mathematician's approaches. Please note the word 'constructive' here.
For those who are only interested in technologies and engineering, they better know, one of the greatest engineers, Archimedes, was highly influenced by Euclid.
Unfortunately, most of current students spend much time in learning geometry, but they know nothing about the philosophy and methodology established by Euclid. So they could apply mathematics in a wrong way later, such as in economics which is almost dominated by mathematical solutions now.
So comes this study. Several approaches for knowledge system development are identified here. They are: Pythagoras-Plato approach; Socrates-Stoicism approach; Euclid-Archimedes approaches; Yi-Jing approach from ancient China; approaches from ancient India and others, etc.
Galileo-Newton approach, the foundation of current sciences, evolved from Euclid-Archimedes approach. But the two are also different somehow.
The first three approaches are discussed here. The others would be covered if time and resources are available in future.
Please note only the differences and problems are discussed here, with no mention who are correct and who are wrong. However, Aristotle's approach is not listed here, because it might be wrong, not just with some problems. I discussed The Aristotle's Approach in a different article, since it is more controversial.
1) Pythagoras-Plato Approach
The first philosopher should be Pythagoras, because he called himself so. He is famous for the Pythagorean theorem, of the length of three sides of right triangle. Here mathematics and philosophy started togather.
Those who think philosophy is almost dead in modern time, may be wrong. A long list of philosophies in mathematics is attached at the end of this article. Mathematics and philosophy are highly related to each other, from the beginning to now.
Pythagoras also taught religious rites and practices in his school, so came his beliefs. He and Plato believed there is a perfect and persistent abstract world, and an imperfect and sensible world. They persued the beauty of abstract perfection.
Pythagorean made many early contributions to knowledge. They tried to represent complicated things with simpler ones. They also gave mathematics their own interpretation. They believed whole numbers are simple and perfect, and tried to represent all numbers with quotient of two whole numbers. Here they faced a mathematical crisis: some, actually most of the numbers, cannot be expressed in such a way. They called these irrational numbers.
This discloses the problems of Pythagoras-Plato approach: could the beauty of mathematics be used to express rationale and fairness, etc., as done by Pythagorean ?
Please note irrational numbers are actually related to nonlinear and chaos or deterministic uncertainty. So although people accept irrational numbers now, is this mathematical crisis really solved ? As said, the studies here is not about history, but about the development of knowledge systems, artificial intelligence, and educational psychology, etc.
2) Socrates-Stoicism approach
Socrates led different beliefs. There is a Socratic method. He asked people to question each other to find the problems. When people discuss explicitly their arguments, they could find the problems and answers easily.
Socrates asked many questions, but did not give many answers. This actually might be a good attitude. Since he asked all people including himself to admit they are igorant.
Admitting their ignorance is an extremely critical step to make future progresses. However, this attitude are offensive to many people, and Socrates was voted to death eventually.
Also, "Aristotle attributed to Socrates the discovery of the method of definition and induction, which he regarded as the essence of the scientific method." (from wikipedia).
Socrates promoted rationale and ethics, which was followed by Cynicism and Stoicism. However, the strange behaviors of Cynicism actually showed the frustrations faced by this approach: they did not find effective ways to discover much more knowledge. This task would be achieved by Euclid-Archimedes approach and Galileo-Newton approach later.
3) Euclid-Archimedes Approaches
Euclid is the first one who established a concrete systematic theory for a domain. He is more like a scientist, rather than a pure mathematician.
He did not concern much of the beauty of abstraction. In the proof of the number of prime numbers, he used the language 'measure', instead of if a number is dividable by another number. Many modern mathematicians think not being abstract enough is a limitation of Euclid.
Please note measurability is still a critical problem in modern physics and artificial intelligence. So, this is actually Euclid's strength. He caught the essentials of the problems.
He usually constructed solutions rather than just proving the existence of unknown solutions. His system is incredible concrete after more than two thousand years, much more concrete than much of modern mathematics.
He constructed the whole system by some simple axioms, and derived other theorems from these axioms. Although this looks like Pythagorean's representing complicated numbers by simpler whole numbers, they are different.
Euclid guarrentees the correctness of derived theorems by makng axioms simple and straghtforward, thus self-evident. Pythagorean did not show why whole numbers could be used to express all numbers, they just believed it is the beauty. Euclid's works are good examples of logic application.
There is still a limitation of Euclid's approach. His system mainly works in geometry, and cannot find all theorems and laws. This limitation is partially solved by Galileo-Newton approach, please note only 'partially'.
Euclid was truly thought as a scientist in early days. Only after Galileo founded Physics, Euclid retired from scientists, and became a mathematician only.
Galileo-Newton approach is still limited in its application domains. It does not work well in artificial intelligence, psychology, economics, etc. Measurability is still a big concerns.
Pythagoras is very insightful in mathematics, philosophy, religious practices and beliefs. Plato developed the philosophy in his style to maturity. Socrates and Stoicism knew the way to develop rationale and ethics. Euclid and Archimedes designed theoretic and real systems in rigorous ways. They all made big contributions to the development of knowledge systems.
They still have big influences so far, but also with their problems. Even Galileo-Newton approach did not solve most of the problems. The problems already solved are only a small portion.
From irrational numbers, to the modern foundational crisis in mathematics, there are some essential problems not solved yet. People only tried to avoid them. New approaches and philosophies are needed. Usually, establishment of a new domain means a new type of philosophy.
For the foundational crisis in mathematics, please see:
http://en.wikipedia.org/wiki/Foundations_of_mathematics#Foundational_crisis
------------------
The philosophies in mathematics:
(From http://en.wikipedia.org/wiki/Philosophy_of_mathematics)
Mathematical realism
Platonism
Full-blooded Platonism
Empiricism
Mathematical Monism
Logicism
Formalism
Conventionalism
Psychologism
Intuitionism
Constructivism
Finitism
Structuralism
Embodied mind theories
New Empiricism
Aristotelian realism
Fictionalism
Social constructivism or social realism
Quasi-empiricism
Popper's "two senses" theory
For those who are only interested in technologies and engineering, they better know, one of the greatest engineers, Archimedes, was highly influenced by Euclid.
Unfortunately, most of current students spend much time in learning geometry, but they know nothing about the philosophy and methodology established by Euclid. So they could apply mathematics in a wrong way later, such as in economics which is almost dominated by mathematical solutions now.
So comes this study. Several approaches for knowledge system development are identified here. They are: Pythagoras-Plato approach; Socrates-Stoicism approach; Euclid-Archimedes approaches; Yi-Jing approach from ancient China; approaches from ancient India and others, etc.
Galileo-Newton approach, the foundation of current sciences, evolved from Euclid-Archimedes approach. But the two are also different somehow.
The first three approaches are discussed here. The others would be covered if time and resources are available in future.
Please note only the differences and problems are discussed here, with no mention who are correct and who are wrong. However, Aristotle's approach is not listed here, because it might be wrong, not just with some problems. I discussed The Aristotle's Approach in a different article, since it is more controversial.
1) Pythagoras-Plato Approach
The first philosopher should be Pythagoras, because he called himself so. He is famous for the Pythagorean theorem, of the length of three sides of right triangle. Here mathematics and philosophy started togather.
Those who think philosophy is almost dead in modern time, may be wrong. A long list of philosophies in mathematics is attached at the end of this article. Mathematics and philosophy are highly related to each other, from the beginning to now.
Pythagoras also taught religious rites and practices in his school, so came his beliefs. He and Plato believed there is a perfect and persistent abstract world, and an imperfect and sensible world. They persued the beauty of abstract perfection.
Pythagorean made many early contributions to knowledge. They tried to represent complicated things with simpler ones. They also gave mathematics their own interpretation. They believed whole numbers are simple and perfect, and tried to represent all numbers with quotient of two whole numbers. Here they faced a mathematical crisis: some, actually most of the numbers, cannot be expressed in such a way. They called these irrational numbers.
This discloses the problems of Pythagoras-Plato approach: could the beauty of mathematics be used to express rationale and fairness, etc., as done by Pythagorean ?
Please note irrational numbers are actually related to nonlinear and chaos or deterministic uncertainty. So although people accept irrational numbers now, is this mathematical crisis really solved ? As said, the studies here is not about history, but about the development of knowledge systems, artificial intelligence, and educational psychology, etc.
2) Socrates-Stoicism approach
Socrates led different beliefs. There is a Socratic method. He asked people to question each other to find the problems. When people discuss explicitly their arguments, they could find the problems and answers easily.
Socrates asked many questions, but did not give many answers. This actually might be a good attitude. Since he asked all people including himself to admit they are igorant.
Admitting their ignorance is an extremely critical step to make future progresses. However, this attitude are offensive to many people, and Socrates was voted to death eventually.
Also, "Aristotle attributed to Socrates the discovery of the method of definition and induction, which he regarded as the essence of the scientific method." (from wikipedia).
Socrates promoted rationale and ethics, which was followed by Cynicism and Stoicism. However, the strange behaviors of Cynicism actually showed the frustrations faced by this approach: they did not find effective ways to discover much more knowledge. This task would be achieved by Euclid-Archimedes approach and Galileo-Newton approach later.
3) Euclid-Archimedes Approaches
Euclid is the first one who established a concrete systematic theory for a domain. He is more like a scientist, rather than a pure mathematician.
He did not concern much of the beauty of abstraction. In the proof of the number of prime numbers, he used the language 'measure', instead of if a number is dividable by another number. Many modern mathematicians think not being abstract enough is a limitation of Euclid.
Please note measurability is still a critical problem in modern physics and artificial intelligence. So, this is actually Euclid's strength. He caught the essentials of the problems.
He usually constructed solutions rather than just proving the existence of unknown solutions. His system is incredible concrete after more than two thousand years, much more concrete than much of modern mathematics.
He constructed the whole system by some simple axioms, and derived other theorems from these axioms. Although this looks like Pythagorean's representing complicated numbers by simpler whole numbers, they are different.
Euclid guarrentees the correctness of derived theorems by makng axioms simple and straghtforward, thus self-evident. Pythagorean did not show why whole numbers could be used to express all numbers, they just believed it is the beauty. Euclid's works are good examples of logic application.
There is still a limitation of Euclid's approach. His system mainly works in geometry, and cannot find all theorems and laws. This limitation is partially solved by Galileo-Newton approach, please note only 'partially'.
Euclid was truly thought as a scientist in early days. Only after Galileo founded Physics, Euclid retired from scientists, and became a mathematician only.
Galileo-Newton approach is still limited in its application domains. It does not work well in artificial intelligence, psychology, economics, etc. Measurability is still a big concerns.
Pythagoras is very insightful in mathematics, philosophy, religious practices and beliefs. Plato developed the philosophy in his style to maturity. Socrates and Stoicism knew the way to develop rationale and ethics. Euclid and Archimedes designed theoretic and real systems in rigorous ways. They all made big contributions to the development of knowledge systems.
They still have big influences so far, but also with their problems. Even Galileo-Newton approach did not solve most of the problems. The problems already solved are only a small portion.
From irrational numbers, to the modern foundational crisis in mathematics, there are some essential problems not solved yet. People only tried to avoid them. New approaches and philosophies are needed. Usually, establishment of a new domain means a new type of philosophy.
For the foundational crisis in mathematics, please see:
http://en.wikipedia.org/wiki/Foundations_of_mathematics#Foundational_crisis
------------------
The philosophies in mathematics:
(From http://en.wikipedia.org/wiki/Philosophy_of_mathematics)
Mathematical realism
Platonism
Full-blooded Platonism
Empiricism
Mathematical Monism
Logicism
Formalism
Conventionalism
Psychologism
Intuitionism
Constructivism
Finitism
Structuralism
Embodied mind theories
New Empiricism
Aristotelian realism
Fictionalism
Social constructivism or social realism
Quasi-empiricism
Popper's "two senses" theory
Aristotle's Approach
Aristotle's approach is very controversial, So it is not discussed in The Different Approaches in Knowledge Development, but in a seperated article here.
Euclid's and Newton's theories are still mainly correct, although with some problems. They are just limited in the extent they could be applied. However, Aristotle's approach might be wrong.
Although Aristotle adored Socrates, he is a student of Plato, and Plato is a student of Socrates, their approaches are very different. As said, Socrates asked people including himself to admit their ignorance, which is important to knowledge development, but offensive to many people. Eventually Socrates was voted to death under accumulated anger.
However, Aristotle talked like he knew everything. His attitude suggested if others learn from him, they also could know everything, which was very pleasant. So he attracted many financial supports.
Unfortunately, many of what he taught were wrong. His huge volumns The Physics shows Aritotle did not really know Physics. Actually after his famous student Alexander conquered much of the East, Alexander found many of Aristotle taught were wrong.
Aristotle also studied ethics. However, he suggested Alexander to be "a despot to the barbarians ... and to deal with the latter as with beasts or plants". He complained Alexander's pretense of divinity later. However, a conquerer being a despot to the barbarians, and to deal with the latter as with beasts or plants, would naturally lead to pretense of divinity. It is normal psychology. It is Aristotle who taught these.
Alexander once said: "If I were not Alexander, then I should wish to be Diogenes" (Diogenes is one of cynicism philosophers, who promoted ethics). He did not say: "If I were not Alexander, then I should wish to be Aristotle".
Fair to say, Aristotle is a main contributor to early logic studies. However, his geocentric view and his introduction of a flying arrow is at rest, shows such logic is very shallow. Aristotle did not know how to apply logic correctly. It is Euclid who showed how logic works. Actually a simple experiment could be designed at Aristotle's time to show the main argument of geocentric view is wrong.
Pythagoras is very insightful in mathematics, philosophy, religious practices and beliefs. Plato developed the philosophy in his style to maturity. Socrates and Stoicism knew the ways to develop rationale and ethics. Socrates even accepted the death to show his ethics view. Euclid and Archimedes designed theoretic and real systems in rigorous ways. They all made big contributions to the development of knowledge systems.
Frankly speaking, Aristotle's main contribution is in zoology, but not much even in botany. He did not know Physics, he did not know how to apply logic correctly.
Euclid's and Newton's theories are still mainly correct, although with some problems. They are just limited in the extent they could be applied. However, Aristotle's approach might be wrong.
Although Aristotle adored Socrates, he is a student of Plato, and Plato is a student of Socrates, their approaches are very different. As said, Socrates asked people including himself to admit their ignorance, which is important to knowledge development, but offensive to many people. Eventually Socrates was voted to death under accumulated anger.
However, Aristotle talked like he knew everything. His attitude suggested if others learn from him, they also could know everything, which was very pleasant. So he attracted many financial supports.
Unfortunately, many of what he taught were wrong. His huge volumns The Physics shows Aritotle did not really know Physics. Actually after his famous student Alexander conquered much of the East, Alexander found many of Aristotle taught were wrong.
Aristotle also studied ethics. However, he suggested Alexander to be "a despot to the barbarians ... and to deal with the latter as with beasts or plants". He complained Alexander's pretense of divinity later. However, a conquerer being a despot to the barbarians, and to deal with the latter as with beasts or plants, would naturally lead to pretense of divinity. It is normal psychology. It is Aristotle who taught these.
Alexander once said: "If I were not Alexander, then I should wish to be Diogenes" (Diogenes is one of cynicism philosophers, who promoted ethics). He did not say: "If I were not Alexander, then I should wish to be Aristotle".
Fair to say, Aristotle is a main contributor to early logic studies. However, his geocentric view and his introduction of a flying arrow is at rest, shows such logic is very shallow. Aristotle did not know how to apply logic correctly. It is Euclid who showed how logic works. Actually a simple experiment could be designed at Aristotle's time to show the main argument of geocentric view is wrong.
Pythagoras is very insightful in mathematics, philosophy, religious practices and beliefs. Plato developed the philosophy in his style to maturity. Socrates and Stoicism knew the ways to develop rationale and ethics. Socrates even accepted the death to show his ethics view. Euclid and Archimedes designed theoretic and real systems in rigorous ways. They all made big contributions to the development of knowledge systems.
Frankly speaking, Aristotle's main contribution is in zoology, but not much even in botany. He did not know Physics, he did not know how to apply logic correctly.
Friday, August 19, 2011
Life, Psychology, and Intelligence: A Three-Level Model of Intelligence (V2)
Artifical Intelligence (AI) are there for many years, but not as a comprehensive successful solution yet, due to the lack of measurement of intelligence and dynamics of intelligence.
Measurement plays critical roles in the development of dynamics: clock, telescope, microscope, etc. We even do not have a good way to measure intelligence now.
The concept Computational Complexity is used to measure computation, not intelligence. Current supercomputers already have comparable computing power and memory as human brains, but they are not even close to human intelligence now.
This is a clear indication: computation is different from intelligence, so Turing Machine and Computational Complexity Theory are not a right theoretic framework for intelligence and AI.
Turing Machine or equivalences, are actually highly influenced by deterministic models. Back to the dynamics for the real world, there are quantum theories, etc., which suggest the possibility of nondeterministism.
Some scientists tried to build the human mental models based on quantum theories. However, the difference between the time scale of neuron firing and excitations in microtubules and the decoherence time tells us there are some links in the middle still missing.
That is the reason I propose this three-level model: Life, Psychology, and Intelligence, to fit in the gap. Psychology is a subdomain of life, built on top of the other parts of life. Intelligence is a subdomain of both life and psychology, and built on top of the other parts of both life and psychology.
Ilya Prigogine's theory of systems far from equilibrium is a good foundation for life phenomena. My goal is to further study and accumulate the knowledge and models of psychology based on Brussels-Austin group and Ilya Prigogine's theories. Once concrete enough foundation has been constructed for this middleware, we coud combine the quantum theories and intelligence models.
So far, most of AI researches are based on methods of equilibrium. These approaches are difficult to make progresses in many areas. Probably people should try to re-focus AI researches on methods of far from equilibrium, rather than only on those of equilibrium.
There are some discussions of ontological aspect and epistemic aspect in Brussels-Austin approach. In real world, due to measurability, there could be several possibilities:
1) Measurable Determinism, determined by measurable factors: environment and internal states, etc.
2) Unmeasurable Determinism, determined by some factors which are not measurable.
3) Nondeterminism. With this model, there could be somethings such as free wills, etc.
Systems far from equilibrium, combined with the possibilities of measurable or unmeasurable, could be the way to illustrate the complexity of psychology and intelligence.
Based on my three-level model, the psychological concepts such as 'will' or 'free will' could be re-studied based on systems far from equilibrium. New concepts and models of intelliegnce could be proposed based on further studies.
Since life is a typical type of systems far from equilibrium, very different from other systems, Brussels-Austin group approach even could gain some hints from the studies based on my propsoal, to re-energize and push forward their research.
In this proposal, there are seven main points, if no one proposed them before, are my new contributions:
1) Propose the concept of Dynamics of Intelligence
2) AI fails as a comprehensive solution so far, due to the lack of measurement of intelligence and dynamics of intelligence.
3) Computation is different from intelligence. Computational complexity is used to measure computation, not intelligence. Turing Machine is not a right theoretic framework for artificial intelligence.
4) The difference between the time scale of neuron firing and excitations in microtubules and the decoherence time, does not exclude the possiblility to build psychology and intelligence models on quantum theories. Just people need build the missing middleware between them first.
5) The theories of far from equilibrium could be used as the foundation of the middleware mentioned in 3).
6) AI researches better to re-focus on methods of far from equilibrium, rather than only on those of equilibrium.
7) Brussels-Austin approach could use life phenomena as good study targets to gain hints, re-energize their efforts, and push forward the researches.
8) A three-level model for intelligence: Life, Psychology, and Intelliegnce. Psychology depends on the other parts of life. Intelligence depends on the other parts of both life and psychology.
If someone already proposed some or all of these ideas before, please let me know. I would really appreciate.
Measurement plays critical roles in the development of dynamics: clock, telescope, microscope, etc. We even do not have a good way to measure intelligence now.
The concept Computational Complexity is used to measure computation, not intelligence. Current supercomputers already have comparable computing power and memory as human brains, but they are not even close to human intelligence now.
This is a clear indication: computation is different from intelligence, so Turing Machine and Computational Complexity Theory are not a right theoretic framework for intelligence and AI.
Turing Machine or equivalences, are actually highly influenced by deterministic models. Back to the dynamics for the real world, there are quantum theories, etc., which suggest the possibility of nondeterministism.
Some scientists tried to build the human mental models based on quantum theories. However, the difference between the time scale of neuron firing and excitations in microtubules and the decoherence time tells us there are some links in the middle still missing.
That is the reason I propose this three-level model: Life, Psychology, and Intelligence, to fit in the gap. Psychology is a subdomain of life, built on top of the other parts of life. Intelligence is a subdomain of both life and psychology, and built on top of the other parts of both life and psychology.
Ilya Prigogine's theory of systems far from equilibrium is a good foundation for life phenomena. My goal is to further study and accumulate the knowledge and models of psychology based on Brussels-Austin group and Ilya Prigogine's theories. Once concrete enough foundation has been constructed for this middleware, we coud combine the quantum theories and intelligence models.
So far, most of AI researches are based on methods of equilibrium. These approaches are difficult to make progresses in many areas. Probably people should try to re-focus AI researches on methods of far from equilibrium, rather than only on those of equilibrium.
There are some discussions of ontological aspect and epistemic aspect in Brussels-Austin approach. In real world, due to measurability, there could be several possibilities:
1) Measurable Determinism, determined by measurable factors: environment and internal states, etc.
2) Unmeasurable Determinism, determined by some factors which are not measurable.
3) Nondeterminism. With this model, there could be somethings such as free wills, etc.
Systems far from equilibrium, combined with the possibilities of measurable or unmeasurable, could be the way to illustrate the complexity of psychology and intelligence.
Based on my three-level model, the psychological concepts such as 'will' or 'free will' could be re-studied based on systems far from equilibrium. New concepts and models of intelliegnce could be proposed based on further studies.
Since life is a typical type of systems far from equilibrium, very different from other systems, Brussels-Austin group approach even could gain some hints from the studies based on my propsoal, to re-energize and push forward their research.
In this proposal, there are seven main points, if no one proposed them before, are my new contributions:
1) Propose the concept of Dynamics of Intelligence
2) AI fails as a comprehensive solution so far, due to the lack of measurement of intelligence and dynamics of intelligence.
3) Computation is different from intelligence. Computational complexity is used to measure computation, not intelligence. Turing Machine is not a right theoretic framework for artificial intelligence.
4) The difference between the time scale of neuron firing and excitations in microtubules and the decoherence time, does not exclude the possiblility to build psychology and intelligence models on quantum theories. Just people need build the missing middleware between them first.
5) The theories of far from equilibrium could be used as the foundation of the middleware mentioned in 3).
6) AI researches better to re-focus on methods of far from equilibrium, rather than only on those of equilibrium.
7) Brussels-Austin approach could use life phenomena as good study targets to gain hints, re-energize their efforts, and push forward the researches.
8) A three-level model for intelligence: Life, Psychology, and Intelliegnce. Psychology depends on the other parts of life. Intelligence depends on the other parts of both life and psychology.
If someone already proposed some or all of these ideas before, please let me know. I would really appreciate.
Saturday, August 6, 2011
The Proposal for A Three-level Intelligence Model
I wrote three articles for my proposal of a new intelligence model, a pure research proposal.
In The Things AI and Robots Can Do Well and Not Well So Far, I summarizes the current status of AI and robotics, to justify the need to develop more advanced AI theories and methodologies.
In Life, Psychology, and Intelligence, I introduced a three-level model of intelligence, based on quantum theories and systems of far from equilibrium. Measurability or unmeasurability highly affect knowledge models, and life is a type of systems far from equilibrium. These are critical factors for a generic intelligence model.
In Reductionism and Whole/Parts, I described the philosophical reasoning to build this model on low level theories: quantum and far from equilibrium systems. This model is not pure reductionism as explained there.
This model assumes the existence of the physical world. Although unmeasurability is mentioned, it doesn't take agnostic position. Because things could be measured up to certain degrees, and we don't know where the up limit is yet even if it DOES exist.
In The Things AI and Robots Can Do Well and Not Well So Far, I summarizes the current status of AI and robotics, to justify the need to develop more advanced AI theories and methodologies.
In Life, Psychology, and Intelligence, I introduced a three-level model of intelligence, based on quantum theories and systems of far from equilibrium. Measurability or unmeasurability highly affect knowledge models, and life is a type of systems far from equilibrium. These are critical factors for a generic intelligence model.
In Reductionism and Whole/Parts, I described the philosophical reasoning to build this model on low level theories: quantum and far from equilibrium systems. This model is not pure reductionism as explained there.
This model assumes the existence of the physical world. Although unmeasurability is mentioned, it doesn't take agnostic position. Because things could be measured up to certain degrees, and we don't know where the up limit is yet even if it DOES exist.
The Difference between Life and Machine
Machines are very useful. However, the tasks they could do are constrained by many factors and grow slowly now. To explore the potential of AI to sustain the growth, people need understand the differences between life and machines
.
Mechanical clocks and watches with extremely high precision and sophistication appeared long, long ago before computers, as well as those old automatons. The steam engines and centrifugal governors back in 18th century can generate power much better than humans. However, they do not have AI. They are only machines.
It is obvious there are still huge differences between life and current machines.
The difficult part is what cause the differences and how much could be reduced. Similiar debates on this topic were repeated again and again for decades without much progress.
This is a clear indication we do not have a proper theoretic framework for AI yet to anwser this question. We even cannot define the most basic concept for AI:the intelligence itself.
Doing some work better than humans is not an evidence of intelligence. A great machine does not necessarily have great AI, which is still very limited in its applications.
There is a concept: strong AI, which means: "the intelligence of a machine that can successfully perform any intellectual task that a human being can" (from wikipedia). Obvious, there is not strong AI so far.
We even do not have a way to evaluate the current status, to measure how far away an AI systems are from strong AI, which may causes illusions to many people.
We have a concept: weak AI, which does not "match or exceed the capabilities of human beings, as opposed to strong AI" (from wikipedia). However weak AI is only a vague phrase, which does not measure how strong or how weak an AI system is.
This is another indication people do not have a proper theoretic framework for AI so far.
To have a good estimation how strong an AI system is, we could redefine strong AI as strong human AI, and break down the big category of weak AI into those such as:
1) strong ape AI, match or exceed apes' capability in all intellectual aspects
2) strong monkey AI, match or exceed monkeys' capability in all intellectual aspects
3) strong cat AI, match or exceed cats' capability in all intellectual aspects
4) strong bee AI, match or exceed bees' capability in all intellectual aspects
5) strong fish AI, match or exceed fishes' capability in all intellectual aspects
etc...
People could add more as they like. When using these fine-grained categories, at least people can have clear sense what are the differences between AI systems and a specific type of life.
Looks we don't have strong cat AI yet. Technical singularity is still far away.
Strong human AI is a superset of both life and machine. We don't know if it is achievable or not yet. It remains as the last boundary to cross between life and machine. I propose a three-level model of Life, Psychology and Intelligence. Hope further research on this model could illustrate how close machines could be made to life and humans.
.
Mechanical clocks and watches with extremely high precision and sophistication appeared long, long ago before computers, as well as those old automatons. The steam engines and centrifugal governors back in 18th century can generate power much better than humans. However, they do not have AI. They are only machines.
It is obvious there are still huge differences between life and current machines.
The difficult part is what cause the differences and how much could be reduced. Similiar debates on this topic were repeated again and again for decades without much progress.
This is a clear indication we do not have a proper theoretic framework for AI yet to anwser this question. We even cannot define the most basic concept for AI:the intelligence itself.
Doing some work better than humans is not an evidence of intelligence. A great machine does not necessarily have great AI, which is still very limited in its applications.
There is a concept: strong AI, which means: "the intelligence of a machine that can successfully perform any intellectual task that a human being can" (from wikipedia). Obvious, there is not strong AI so far.
We even do not have a way to evaluate the current status, to measure how far away an AI systems are from strong AI, which may causes illusions to many people.
We have a concept: weak AI, which does not "match or exceed the capabilities of human beings, as opposed to strong AI" (from wikipedia). However weak AI is only a vague phrase, which does not measure how strong or how weak an AI system is.
This is another indication people do not have a proper theoretic framework for AI so far.
To have a good estimation how strong an AI system is, we could redefine strong AI as strong human AI, and break down the big category of weak AI into those such as:
1) strong ape AI, match or exceed apes' capability in all intellectual aspects
2) strong monkey AI, match or exceed monkeys' capability in all intellectual aspects
3) strong cat AI, match or exceed cats' capability in all intellectual aspects
4) strong bee AI, match or exceed bees' capability in all intellectual aspects
5) strong fish AI, match or exceed fishes' capability in all intellectual aspects
etc...
People could add more as they like. When using these fine-grained categories, at least people can have clear sense what are the differences between AI systems and a specific type of life.
Looks we don't have strong cat AI yet. Technical singularity is still far away.
Strong human AI is a superset of both life and machine. We don't know if it is achievable or not yet. It remains as the last boundary to cross between life and machine. I propose a three-level model of Life, Psychology and Intelligence. Hope further research on this model could illustrate how close machines could be made to life and humans.
Wednesday, July 20, 2011
Reductionism and Whole/Parts
In my previous post, I proposed a three-level model of intelligence: Life, Psychology, and Intelligence, which is based on the theories of far from equilibrium which again could be based on quantum theories. So total 5-level models, including life and non-life. This article explains some philosophical reasoning behind this model.
These models sounds like reductionism, but not exactly.
Let's first consider the phrase "the whole is greater than the sum of its parts", which has been talked by many, many people and many, many times.
In reality, the highly abstracted "whole" could be much less than the sum of its parts, if people ignore too many details and don't have enough knowledge of parts.
So "the whole is greater than the sum of its parts" is only true when you have the enough knowledge of parts. Usually it is not the case. People should be aware of the constraints of those sayings which sounds correct.
However, in practice, many people still follow such type of thinking, not exactly, but actually in a loosed form, consciously or not consciously, "the whole could provide additional information to the sum of its parts". Here the constraint of "enough knowledge of parts" is removed. But pay attention to "could". You take the risk for your own by following this thinking. It only COULD be true.
The constraint tells us, people need pay attention to the details of parts, especially when things are not done well. By coming back to fundamentals, people could enrich their understanding, adjust their approaches.
However, only low-level details are not good enough. By the loosed version of "the whole could provide additional information to the sum of its parts", people could establish high-level principles, which may be not obvious in low-level regular phenomina, to understand the whole better. Brussels-Austin approach could gain hints from the studies of life, psychology and intelligence, to push their researches further.
So although "the whole is greater than the sum of its parts" is not always true, this three-level model of intelligence is not pure reductionism.
These models sounds like reductionism, but not exactly.
Let's first consider the phrase "the whole is greater than the sum of its parts", which has been talked by many, many people and many, many times.
In reality, the highly abstracted "whole" could be much less than the sum of its parts, if people ignore too many details and don't have enough knowledge of parts.
So "the whole is greater than the sum of its parts" is only true when you have the enough knowledge of parts. Usually it is not the case. People should be aware of the constraints of those sayings which sounds correct.
However, in practice, many people still follow such type of thinking, not exactly, but actually in a loosed form, consciously or not consciously, "the whole could provide additional information to the sum of its parts". Here the constraint of "enough knowledge of parts" is removed. But pay attention to "could". You take the risk for your own by following this thinking. It only COULD be true.
The constraint tells us, people need pay attention to the details of parts, especially when things are not done well. By coming back to fundamentals, people could enrich their understanding, adjust their approaches.
However, only low-level details are not good enough. By the loosed version of "the whole could provide additional information to the sum of its parts", people could establish high-level principles, which may be not obvious in low-level regular phenomina, to understand the whole better. Brussels-Austin approach could gain hints from the studies of life, psychology and intelligence, to push their researches further.
So although "the whole is greater than the sum of its parts" is not always true, this three-level model of intelligence is not pure reductionism.
Things AI/Robots Can Do Well and Not Well So Far
1) AI can play chess quite well. Chess is played on small 8x8 boards with strict rules, a task extremely fitting to AI. However, chess softwares are continuely being improved by humans.
Even for chess, I don't know for a fixed chess software without any farther tuning from humans, how long it can keep winning against top human players. Since humans can learn better than AI so far.
2) AI can play GO well, but not so well as chess, due to combinatorial explosion. GO is played on 19x19 boards, also with strict rules.
3) Auto car pilots. It runs on roads built for cars and with highly-regulated traffic rules. Only a few cases are reported so far. Don't know how well these systems can run in large scale and randomly picked road situations.
A reasonable guess is: potentially auto car pilots could do well in standard car racing competition if not already, which runs on routes regulated better than usual roads, easier for safety. Although improving performance might be more difficult initially than running at normal speeds, safety could be more difficult to solve eventually.
4) Robots' capabilities are highly constrained by AI.
They can play Rubiks Cube extremely well, since there are fixed algorithms. They can play music instruments well, with fixed actions. They can even play pingpong with humans, which is on simple platform with simple rules. Hopefully, robots could catch up with or even exceed humans soon with pingpong.
They can walk. With two legs, they can walk on flat places, or stairs with regular shapes, but not good at mountain surfaces yet. With four legs, they could walk on rough terrains, but may not on very steep hills or rocky mountains as deers and other animals can, not to mention to find a route by themselves there. With six legs, they can even climb on coarse walls or trees, but not seen on glass or steel walls (by me) yet, probably would in near future since glass or steel wall surface is well-defined.
They could emulate simple creatures like fishes, moths, somehow snakes, but not even behave like chicken, rats, cats, dogs, etc., yet so far.
So even to achieve goals of weak AI, robots still need much more advanced AI theories and methodologies.
There is one category: industrial robots, which already play significant roles in reality. They are designed for well-defined tasks. However, there are lessons could be taken from the Saturn project in 1980s, if people want to make big breakthrough.
5) Handwriting recognization, voice recognization, image and video-processing, etc. These can do well in certain contexts and with specific targets to recognize, such as moving objects. AI could recognize what people write better if it already knows what type of things are written: digitals, English letters, Chinese characters, mathematical signs, etc. Or for voice: what languages people are speaking, or they are singing a song, or just playing Kouji, etc.
......
People could add more to this list. However the list would still not be long. In most situations, computers only can aid and enhance humans, not replace them.
AI cannot do most things humans can. So IMHO, AI is still in poineering stage. Only a few things could be implemented in software so far. More research is needed, including much philosophical thinking.
Don't limit youself to software implementation in AI researches. Most of them cannot be implemented so far.
Even for chess, I don't know for a fixed chess software without any farther tuning from humans, how long it can keep winning against top human players. Since humans can learn better than AI so far.
2) AI can play GO well, but not so well as chess, due to combinatorial explosion. GO is played on 19x19 boards, also with strict rules.
3) Auto car pilots. It runs on roads built for cars and with highly-regulated traffic rules. Only a few cases are reported so far. Don't know how well these systems can run in large scale and randomly picked road situations.
A reasonable guess is: potentially auto car pilots could do well in standard car racing competition if not already, which runs on routes regulated better than usual roads, easier for safety. Although improving performance might be more difficult initially than running at normal speeds, safety could be more difficult to solve eventually.
4) Robots' capabilities are highly constrained by AI.
They can play Rubiks Cube extremely well, since there are fixed algorithms. They can play music instruments well, with fixed actions. They can even play pingpong with humans, which is on simple platform with simple rules. Hopefully, robots could catch up with or even exceed humans soon with pingpong.
They can walk. With two legs, they can walk on flat places, or stairs with regular shapes, but not good at mountain surfaces yet. With four legs, they could walk on rough terrains, but may not on very steep hills or rocky mountains as deers and other animals can, not to mention to find a route by themselves there. With six legs, they can even climb on coarse walls or trees, but not seen on glass or steel walls (by me) yet, probably would in near future since glass or steel wall surface is well-defined.
They could emulate simple creatures like fishes, moths, somehow snakes, but not even behave like chicken, rats, cats, dogs, etc., yet so far.
So even to achieve goals of weak AI, robots still need much more advanced AI theories and methodologies.
There is one category: industrial robots, which already play significant roles in reality. They are designed for well-defined tasks. However, there are lessons could be taken from the Saturn project in 1980s, if people want to make big breakthrough.
5) Handwriting recognization, voice recognization, image and video-processing, etc. These can do well in certain contexts and with specific targets to recognize, such as moving objects. AI could recognize what people write better if it already knows what type of things are written: digitals, English letters, Chinese characters, mathematical signs, etc. Or for voice: what languages people are speaking, or they are singing a song, or just playing Kouji, etc.
......
People could add more to this list. However the list would still not be long. In most situations, computers only can aid and enhance humans, not replace them.
AI cannot do most things humans can. So IMHO, AI is still in poineering stage. Only a few things could be implemented in software so far. More research is needed, including much philosophical thinking.
Don't limit youself to software implementation in AI researches. Most of them cannot be implemented so far.
Life, Psychology, and Intelligence: A Three-Level Model of Intelligence
Studies of Artifical Intelligence (AI) are still very difficult in many areas, such as in Robotics, etc. When things are not going well enough, people have to come back to fundamentals.
Traditionally, many of AI researches are based on Turing Machine or equivalences, which are highly influenced by deterministic models.
However, for the real world, there are quantum theories, etc., which suggest the possibility of nondeterministism.
Some scientists tried to build the human mental models based on quantum theories. However, the difference between the time scale of neuron firing and excitations in microtubules and the decoherence time tells us there are some links in the middle still missing.
That is the reason I propose this three-level model: Life, Psychology, and Intelligence, to fit in the gap.
Psychology is a subdomain of life, built on top of the other parts of life. Intelligence is a subdomain of both life and psychology, and built on top of the other parts of both life and psychology.
Ilya Prigogine's theory of systems far from equilibrium is a good foundation for life phenomena. My goal is to further study and accumulate the knowledge and models in psychology based on Brussels-Austin group and Ilya Prigogine's theories. Once concrete enough foundation has been constructed for this middleware, we could combine the quantum theories and intelligence models.
So far, most of AI researches are based on methods of equilibrium. These approaches are difficult to make progresses in many areas. Probably people should try to re-focus AI researches on methods of far from equilibrium, rather than only on those of equilibrium.
There are some discussions of ontological aspect and epistemic aspect in Brussels-Austin approach. In real world, due to measurability, there could be several possibilities:
1) Measurable Determinism, determined by measurable factors: environment and internal states, etc.
2) Unmeasurable Determinism, determined by some factors which are not measurable.
3) Nondeterminism. With this model, there could be somethings such as free wills, etc.
Systems far from equilibrium, combined with the possibilities of measurable or unmeasurable, could be the way to illustrate the complexity of psychology and intelligence.
Based on my three-level model, the psychological concepts such as 'will' or 'free will' could be re-studied based on systems far from equilibrium. New concepts and models of intelliegnce could be proposed based on further studies.
Since life is a typical type of systems far from equilibrium, very different from other systems, Brussels-Austin group could even gain some hints from the studies based on my propsoal, to re-energize and push forward their researches.
In this proposal, there are five main points which, if no one proposed before, are my new contributions:
1) The difference between the time scale of neuron firing and excitations in microtubules and the decoherence time, does not exclude the possiblility to build psychology and intelligence models on quantum theories. Just people need build the missing middleware between them first.
2) The theories of far from equilibrium could be used as the foundation of the middleware mentioned in 1).
3) AI researches better to re-focus on methods of far from equilibrium, rather than only on those of equilibrium.
4) Brussels-Austin approach could use life phenomena as good study targets to gain hints, re-energize their efforts, and push forward the researches.
5) A three-level model for intelligence: Life, Psychology, and Intelliegnce. Psychology depends on the other parts of life. Intelligence depends on the other parts of both life and psychology.
If someone already proposed some or all of these ideas before, please let me know. I would really appreciate.
Traditionally, many of AI researches are based on Turing Machine or equivalences, which are highly influenced by deterministic models.
However, for the real world, there are quantum theories, etc., which suggest the possibility of nondeterministism.
Some scientists tried to build the human mental models based on quantum theories. However, the difference between the time scale of neuron firing and excitations in microtubules and the decoherence time tells us there are some links in the middle still missing.
That is the reason I propose this three-level model: Life, Psychology, and Intelligence, to fit in the gap.
Psychology is a subdomain of life, built on top of the other parts of life. Intelligence is a subdomain of both life and psychology, and built on top of the other parts of both life and psychology.
Ilya Prigogine's theory of systems far from equilibrium is a good foundation for life phenomena. My goal is to further study and accumulate the knowledge and models in psychology based on Brussels-Austin group and Ilya Prigogine's theories. Once concrete enough foundation has been constructed for this middleware, we could combine the quantum theories and intelligence models.
So far, most of AI researches are based on methods of equilibrium. These approaches are difficult to make progresses in many areas. Probably people should try to re-focus AI researches on methods of far from equilibrium, rather than only on those of equilibrium.
There are some discussions of ontological aspect and epistemic aspect in Brussels-Austin approach. In real world, due to measurability, there could be several possibilities:
1) Measurable Determinism, determined by measurable factors: environment and internal states, etc.
2) Unmeasurable Determinism, determined by some factors which are not measurable.
3) Nondeterminism. With this model, there could be somethings such as free wills, etc.
Systems far from equilibrium, combined with the possibilities of measurable or unmeasurable, could be the way to illustrate the complexity of psychology and intelligence.
Based on my three-level model, the psychological concepts such as 'will' or 'free will' could be re-studied based on systems far from equilibrium. New concepts and models of intelliegnce could be proposed based on further studies.
Since life is a typical type of systems far from equilibrium, very different from other systems, Brussels-Austin group could even gain some hints from the studies based on my propsoal, to re-energize and push forward their researches.
In this proposal, there are five main points which, if no one proposed before, are my new contributions:
1) The difference between the time scale of neuron firing and excitations in microtubules and the decoherence time, does not exclude the possiblility to build psychology and intelligence models on quantum theories. Just people need build the missing middleware between them first.
2) The theories of far from equilibrium could be used as the foundation of the middleware mentioned in 1).
3) AI researches better to re-focus on methods of far from equilibrium, rather than only on those of equilibrium.
4) Brussels-Austin approach could use life phenomena as good study targets to gain hints, re-energize their efforts, and push forward the researches.
5) A three-level model for intelligence: Life, Psychology, and Intelliegnce. Psychology depends on the other parts of life. Intelligence depends on the other parts of both life and psychology.
If someone already proposed some or all of these ideas before, please let me know. I would really appreciate.
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