[Fis] Fis Digest, Vol 105, Issue 12
钟义信
zyx at bupt.edu.cn
Thu Oct 19 08:48:53 CEST 2023
Dear Krassimir, Dear Eric, and Dear Colleagues,
The discussion is going on well thanks to all your efforts.
Here is a few points I would like to mention (or re-mention).
(1) The purpose of the "declaration on Paradigm Change in AI" is to make an appeal for change the paradigm used in AI.
(2) There may have different understanding on the concept of paradigm. However, the concept of paradigm for a scientific discipline has been re-defined as the scientific world view and the associated methodology because the scientific worldview and its methodology as a whole is the only factor that can determine whether a scientific discipline needs a "revolution" (Kuhn's language).
(3) The major result of "paradigm change in AI" is to change the methodology used in AI, including the principles of "pure formalism" and "divide and conquer". This is because of the fact that the former principle leads to the ignoring the meaning and value and thus leads to the loss of understanding ability and explaining ability while the latter one leads to the loss of the general theory for AI. Note that "no explaining ability" and "no general theory" are the most typical and also most concerned problems for current AI.
(4) There is difference between human intelligence and human wisdom. One of the functions of human wisdom is to find the to-be-solved problem which must be meaningful for human purpose of improving the living and developing. Yet, the function of human intelligence is to solve the problem defined by human wisdom.
(5) Human intelligence can be simulated by machine. But human wisdom cannot be simulated by machine because machine is non-living beings that has no its own purpose and cannot understand human purpose. No purpose means no wisdom.
I wonder if you agree or not. Comments are welcome!
Best regards,
Prof. Yixin ZHONG
AI School, BUPT
Beijing 100876, China
------------------ Original ------------------
From: "Krassimir Markov"<itheaiss at gmail.com>;
Date: Thu, Oct 19, 2023 03:32 AM
To: "fis"<fis at listas.unizar.es>;
Subject: Re: [Fis] Fis Digest, Vol 105, Issue 12
Dear Yixin, Eric and FIS colleagues,Let me present some thoughts about
The “Intelligence” Paradigm
For those who are not familiar with the concepts of "paradigm" and "paradigm shift", I would recommend texts from Wikipedia that explain it clearly enough.
I myself maintain a neutral position in the dispute between Popper and Kuhn regarding the development of science. Both theses have their grounds, but at different levels and stages. In fact, in this case, the law of quantitative accumulation, which leads to qualitative changes, applies. Obviously, in a number of cases the paradigm shift happens in leaps and bounds, while in others it happens smoothly and barely perceptibly.
For example, the accumulation of sufficient observations and evidences regarding the shape of the earth required a shift to a new paradigm: from the "Earth is flat" paradigm to the "Earth is not flat" paradigm.
Sometimes opposing paradigms can coexist, not negating each other, but complementing each other. For example, this is the case with Euclid's fifth postulate (the parallel postulate).
The postulate has long been considered self-evident or inevitable, but no evidence has been found. Eventually, it was discovered that reversing the postulate gave valid, albeit different, geometries. A geometry where the parallelism postulate does not hold is known as non-Euclidean geometry.
With regard to the paradigm of "intelligence" we have a similar situation. We have at least two opposing paradigms based on two opposing postulates.
The first, let's call it the "flat intelligence postulate", was well articulated by Yixin in his post:
"Intelligence is the ability to solve problems, but not the ability to detect and define problems, the latter of which is one of the faculties of wisdom."
The second, let's call it the "non-flat intelligence postulate", will sound unifying: "Intelligence is both the ability to solve problems and the ability to detect and define problems" (Eric), but in different directions in the hierarchy of intelligences (KM)". This is how we arrive at the idea of cybernetic systems, where there is a controller and a controlled, but the controller is connected to the environment from which it receives controlling influences and is, in practice, both "controller" and "controlled", but in different aspects of the system.
To be continued ...
На ср, 18.10.2023 г. в 15:07 ч. <fis-request at listas.unizar.es> написа:
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Today's Topics:
1. Re: Paradigm AI - I guess we call it Genius (Eric Werner)
---------- Forwarded message ----------
From: Eric Werner <eric.werner at oarf.org>
To: Karl Javorszky <karl.javorszky at gmail.com>
Cc: "钟义信" <zyx at bupt.edu.cn>, fis <fis at listas.unizar.es>
Bcc:
Date: Wed, 18 Oct 2023 14:07:13 +0200
Subject: Re: [Fis] Paradigm AI - I guess we call it Genius
Dear Karl,
Thank you for bringing this important point to my attention. Here are some thoughts:
I guess we call it Genius
Difference between generating and understanding or reading
Super intelligence, requires genius or generational understanding
Generative intelligence
Creative intelligence
Compositional intelligence
Formative intelligence
Evolutional intelligence
Restricting, intelligence to problem-solving, dismisses, creative acts of composition in science and the arts
Think of Heinz Kohut’s formation of the self in psychology versus Freudian reactive psychology
It’s the difference between discovering a theorem, and proving the theorem
It’s the difference between school-boy problem-solving, and Newton
Some psychologists think of intelligence in relationship to testing people for their ability to cope in educational institutions. They want to see if they are college material or not.
With future All systems were talking about Newton level intelligence not college level intelligence
Kantian synthetic intelligence
We better be ready for that! If not, we got some real problems.
That is why making these systems social and cooperative is so essential.
We may quickly reach a point where the compositional creative intelligence of artificial models is so powerful, we will not be able to understand them. Not just how they work. We already don't understand how they work now. But their reasoning and new outputs such, as for example, mathematical insights. Imagine a system that can reason and develop 2,000 years of mathematics in a few minutes. It is precisely this overarching linking of knowledge that makes for real intelligence such as that of Leibniz or Newton. The old school model of psychological testing of intelligence uses a definition of intelligence that is to limiting for AI models. AI models are not your evey day student.
Best wishes,
Eric
On 10/18/23 12:59 PM, Karl Javorszky wrote:
Dear Eric,
Your statement: „The essence of general intelligence is the ability to not only solve an externally given problem but to be creative and find and define problems.” is at deviance to accepted delineations of concepts in the trade of psychology. Rohracher [1] has defined in 1969 (and to my knowledge, no one has disputed this wording): “Intelligence is the degree of efficiency [of the CNS] while solving new problems.”
What you refer to is subsumed variously under: creativity, alertness, curiosity, vitality, spontaneity.
There is consensus in the epistemology of psychology that there can exist no final, conclusive, all-encompassing theory of personality (in which intelligence and adaptability/curiosity would or would not be separated as concepts), because if such an ultimate, final, true theory of personality would exist, that assumption would negate the axiomatic rule that one can always learn something new, at least about himself. There is, by definition, no end to introspection and philosophy. One can always come up with a new theory of personality and one cannot rule out that a new theory of personality would be more reasonable, truer, more conclusive than anything that has existed before.
Psychologists see theories about mind and soul in the same way believers see their God. It is impossible to recognize all features of God, let alone to insist that one has a correct reading.
So, if you decide not to distinguish between efficiency of solving new problems and ability and tendency towards finding new problems to solve, you are free to do so. Established use of words splits the two personality traits.
I have prepared a statement about the key word “otherwise”. The word is needed to scale the efficiency of mental processes while solving new problems (aka ‘intelligence’) by scaling the diversity/similarity properties of alternatives. To be able to efficiently choose between alternatives, one needs to have alternatives that are different among each other. The task is to find such collections of symbols that are alternatives to each other, not by machinations by humans, but as members of a symbols collection. This task is not easy to solve while using the symbols set in the traditional, Sumerian ways only. One needs to assume that symbols have their own properties, by their nature, immanent to them.
Due to the two-messages-per-week rule, the contribution shall come next week.
Karl
[1] Rohracher, H.: Einführung in die Psychologie, Urban & Schwarzenberg, Wien 1951
Am Mi., 18. Okt. 2023 um 12:01 Uhr schrieb Eric Werner <eric.werner at oarf.org>:
Dear Yixin,
Thank you for you comments!
To your point (2): The essence of general intelligence is the ability to not only solve an externally given problem, but to be creative and find and define problems. For example, given a knowledge of mathematics and physics and data to generate new mathematics and new insights into the nature of the world.
To your point (3): Biotechnology and AI are somewhat independent fields. AI can help genome research and decoding genomes. But once genomes are decoded that information can be used to construct more general AI models. When I say "architecture" I meant the architecture of the human brain encoded in the human genome. This architectural information can be used to guide the structuring of AI models be be more potent and more human like. And, AI may well help in the process of structuring its future version. That is what I meant by selfreferencing.
To the more general point, formalization of social information can help guide the improvement of AI models to be more social and have greater abilities in a AI-robot social setting.
All the best,
Eric
On 10/18/23 9:16 AM, 钟义信 wrote:
Dear Eric,
Thank you for the interesting talk on "Paradigm AI" from which I learned a lot.
As a discussant, may I propose some of my understanding. Comments are welcome.
(1) I appreciate your idea that saying "Physics paradigm PPD does not fit well with AI paradigm" and "Information paradigm PID is a better fit". This is the valuable common basis, between you and me, concerning the PPD, PID and AI.
(2) How to define the concept of intelligence? This is a very difficult problem. To my own understanding, the following short statement may serve as one of the candidates: Intelligence is the ability to solve problem but not the ability to find and define problem, the latter of which is one of the abilities for wisdom.
(3) The paradigm for AI can be used as the paradigm for bio-technology with certain simplification and specialization. This judgement is not based on their "structure/architecture", but based on their "information function" - which is the basic function in both AI and biotechnology, that is to seek opportunity for "living (or solving problem)" and to avoid the "danger (or failing to problem solving)".
Once again, comments and criticisms are most welcome.
Best regards,
Prof. Yixin ZHONG
AI School, BUPT
Beijing 100876, China
------------------ Original ------------------
From: "Eric Werner"<eric.werner at oarf.org>;
Date: Tue, Oct 17, 2023 02:32 AM
To: "fis"<fis at listas.unizar.es>;
Subject: [Fis] Paradigm AI
Here are some brief thoughts on Paradigms and AI by I presume was written by Yixin Zhong since I cannot read Chinese.
Paradigm AI
I agree that the physics paradigm PPD doesn’t fit well with the AI paradigm, and that the information paradigm PID is a better fit
Artificial intelligence systems, don’t necessarily learn from human beings. In unsupervised learning they learn from data and not from humans.
The problem, and becomes really how to define what intelligence is: Which of the following is it?
Rational inference
Summarizing large amounts of text and data
Making new predictions based on scientific theories and available data
Developing new theories that explain the data in the more succinct way, and making new predictions
Developing new technologies independently of human input
Planning and executing the actions and intentions of a robot
Having social intelligence
Being cooperative with a human being in achieving a task
Interrelating two discipline, such as physics and mathematics, to make new discoveries
Understanding, genomes in the way that human beings cannot
Designing new organisms by designing their genomes
I agree with the language of a new paradigm, such as artificial intelligence will develop slowly step by step in conjunction with its use -both conceptually and experimentally .
In a new paradigm entire new language is created as a paradigm is developed
The language evolves in concert with a new ontology suggested by the paradigm
It is an ontology of objects, technologies, actions, and strategies
What will be particularly interesting, is the linking of the paradigm of artificial intelligence with the paradigm of biotechnology
Biotechnology and AI will truly link the human brain with the artificial brain
The genome of the natural brain will be reflected in the architecture of the artificial brain
Hence by using AI to decode the genome of the natural brain, it will be self-reflected in the design of the developing artificial brain
This will bring unprecedented social and rational functionality to the artificial brain
Note that the biotech-genome paradigm also is founded on the information paradigm.
Thank you Yixin Zhong for your input and emphasizing the intimate relationship of information and AI paradigms.
Best wishes,
Eric
--
Dr. Eric Werner
Oxford Advanced Research Foundation
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