Scientific knowledge formalization
Alex Shkotin
John,
I am happy you agreed here:
JFS:"Alex: "We need to formalize our scientific theories to use computers to their full potential." I agree,..."
AS: And the next step is to just align our terminology: not necessarily use the same, but to understand used by other parts.
JFS:"…but the formalization is ALWAYS context dependent. The engineering motto is fundamental:
ALL THEORIES ARE WRONG, BUT SOME ARE USEFUL.
That is true about formalization. It's only precise for subjects that can be expressed in finite bit strings. For 99.9% of all the information we get every second of our lives, vagueness is inescapable. We must deal with it by informal methods of approximations. Any formal statement is FALSE in general, but it may be useful when the limitations are made explicit.
"
AS: We do not use the term context when describing the situation in which the entity being studied is located (usually a system in some state and process). Usually it is described with what other systems and how it interacts and what happens on the border. Remotely acting forces are generally known: gravity and electromagnetic field. Of course we must take into account external flows of bodies, for example particles in the case of ISS. By the way, at the moment for some systems it is necessary to describe their information interaction. You can try to cover all this with the term context, but usually it seems that this is not used. But why not!
I'll write more about finite bit strings later.
In general: our robots must use formal language and algorithmic reasoning and acting. If they are boring we will have to endure it.
Let me remind myself that the English language is formal at its core and for the language of communication between robots and people it is better to simply talk about simple English, etc.
Alex
John F Sowa
hpo...@verizon.net
John,
And then there is the virtual world that humans have created – and continue to create/destroy. All our institutions, geopolitical boundaries, names and categories for things and concepts that can’t be discerned by looking at “the real world”. Pluto is no longer a planet. Says who? What if we change the location of the Prime Meridian? The Brits won’t be the only ones to be upset. And why are they called Brits?
Hans
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Alex Shkotin
John,
Let me clarify what I meant by "English is HOL" by example.
Sentence: "I see a blue jay drinking out of the birdbath."
HOL-structure: (I see ((a (blue jay)) (drinking (out of)) (the birdbath)))
where
"of" is an unary operator used in postfix form, applied to "out" being an argument. As a result we get "(out of)" an expression or term.
But this term is itself an unary operator used in postfix form, applied to "drinking" to create a term "(drinking (out of))", being binary operator in infix form being applied to two arguments: left one: "(a (blue jay))", and right one: "(the birdbath)".
As a result we have a proposition which is a right argument for another binary operator in infix form "see", which has the left argument "I".
And we are talking here not about Logic, but about Language.
In every syntactically correct phrase, words are combined: one word is applied to another. The result is something like molecules, but in the World of Words.
How to get this structure from a chain of words? How to work with these structures to get what? Some pictures? True|false value?
This is the questions 🔬
Alex
--
John F Sowa
Alican Tüzün
@John
Doesn't narrowing down the meaning of a symbol typically lead to a more "precise" interpretation?
If a set of symbols (or sign vehicle) signifies a more limited set of immediate objects, it results in a more specific reference. This increased specificity can lead to
a more focused interpretation (the effect or interpretation in the mind). Overall, sign creation will be more "precise".
E.g., Number 1 and word One. The latter symbol can be interpreted with more things, while the former is less. Overall, isn't the sign-making with Number 1 easier or, in your discussion words, more "precise"?
If I understood something wrong, please correct me.
@Alex
Also, from my observation of Alex's work, in my opinion, that's what he is trying to achieve. Also correct me, Alex, if I understood wrong.
Best,
Alican
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Alexandre Rademaker
How to get this structure from a chain of words?
Some people prefer to produce a semantic representation directly from a flat list of words; see https://en.wikipedia.org/wiki/Abstract_Meaning_Representation. Usually, data is used to train a parser to produce AMR from the original surface forms.
I prefer a grammar-based parser that can produce a deep syntactic and semantic structure. See
http://delph-in.github.io/delphin-viz/demo/#input=I%20see%20a%20blue%20jay%20drinking%20out%20of%20the%20birdbath.&count=500&grammar=erg2018-uw&tree=true&mrs=true&dmrs=true
For "I see a blue jay drinking out of the birdbath.” the English Resource Grammar produced 162 possible readings. Each can be further expanded into many possible concrete semantics representations given the possible nesting of quantifiers. See https://aclanthology.org/2023.icnlsp-1.19/ and code at https://github.com/ibm/mrs-logic. I am now working on translating it to Lean.
Of course, there are many variations among the three options above. To cite one more, see https://en.wikipedia.org/wiki/Combinatory_categorial_grammar.
I hope I got your question right. I am sorry if I said basic things that all forum members know.
I agree with John about the vagueness of natural language and the "continuous range of meanings,” as he put it. That is why I believe in precise formal grammar that does not try to guess the intended meaning of the utterance eagerly but licenses all possible interpretations for downstream applications.
Best,
Alexandre
deddy
>
> For a natural language, almost every word has a continuous range of meanings.
>
See classic George Miller "Ambiguous Words"
13 simple Robert Frost words offer 3.6 TRILLION combinations.
https://www.thekurzweillibrary.com/ambiguous-words
So far no acknowledgement at all of the existance of "unnatural language."
Unnatural language being the strings / labels / terms used INSIDE software applications. Many universes of written but minimally spoken terminology that AFAIK is entirely ignored in the current interest in AI & ontologies.
For those who expect "meaning" from statistics... long, long, long ago I encountered an insurance company that had found 70 different "names" for the concept "policy number."
AI LLMs / ontologies address this ... how?
______________________
David Eddy
> John,
>
> Let me clarify what I meant by "English is HOL" by example.
>
> Sentence: "I see a blue jay drinking out of the birdbath."
>
> HOL-structure: (I see ((a (blue jay)) (drinking (out of)) (the
> birdbath)))
>
> where
>
> "of" is an unary operator used in postfix form, applied to "out" being
> an argument. As a result we get "(out of)" an expression or term.
>
> But this term is itself an unary operator used in postfix form,
> applied to "drinking" to create a term "(drinking (out of))", being
> binary operator in infix form being applied to two arguments: left
> one: "(a (blue jay))", and right one: "(the birdbath)".
>
> As a result we have a proposition which is a right argument for
> another binary operator in infix form "see", which has the left
> argument "I".
>
> And we are talking here not about Logic, but about Language.
>
> In every syntactically correct phrase, words are combined: one word is
> applied to another. The result is something like molecules, but in the
> World of Words.
>
> How to get this structure from a chain of words? How to work with
> these structures to get what? Some pictures? True|false value?
>
> This is the questions 🔬
>
> Alex
>
> All contributions to this forum are covered by an open-source
> license.
> For information about the wiki, the license, and how to subscribe or
> unsubscribe to the forum, see http://ontologforum.org/info
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>
Ravi Sharma
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hpo...@verizon.net
Understood, Ravi. I was just trying to illustrate with some examples of what I meant by the virtual world created by humans in addition to the natural world that John was referring to in his email. We tend to overlook the enormous amount of information we humans create about things that don’t actually exist in the natural world, at least not in the sense of material objects. Of course, we often create physical representations of some types of such things, but even there many of those representations have been reduced to bit patterns on some digital storage media, aka, “the cloud”. The physical properties of such representations bear no inherent relationship to the things they represent, such as the deed to a piece of property or a movie (which itself may represent something that doesn’t actually exist in the natural world). That general problem was one of the big challenges faced by Sagan and crew when working on the Voyager plaque/disk.
Hans
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Nadin, Mihai
This is the semiotics of the subject. Discussed many times by our Forum. Indeed, without referencing C.S. Peirce on the matter we will not make progress. In this vein: syntax and semantics are important. But sign processes are driven by the pragmatics: representations have a purpose or can be associated with purposes.
Mihai Nadin
From: hpolzer via ontolog-forum <ontolo...@googlegroups.com>
Sent: Thursday, October 10, 2024 3:58 PM
To: ontolo...@googlegroups.com
Subject: RE: [ontolog-forum] Scientific knowledge formalization
Understood, Ravi. I was just trying to illustrate with some examples of what I meant by the virtual world created by humans in addition to the natural world that John was referring to in his email. We tend to overlook the enormous amount of information we humans create about things that don’t actually exist in the natural world, at least not in the sense of material objects. Of course, we often create physical representations of some types of such things, but even there many of those representations have been reduced to bit patterns on some digital storage media, aka, “the cloud”. The physical properties of such representations bear no inherent relationship to the things they represent, such as the deed to a piece of property or a movie (which itself may represent something that doesn’t actually exist in the natural world). That general problem was one of the big challenges faced by Sagan and crew when working on the Voyager plaque/disk.
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Ravi Sharma
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Chuck Woolery
Hans,
Thank you for making this profoundly important point 😉
cw
From: hpolzer via ontolog-forum <ontolo...@googlegroups.com>
Sent: Thursday, October 10, 2024 4:58 PM
To: ontolo...@googlegroups.com
Subject: RE: [ontolog-forum] Scientific knowledge formalization
Understood, Ravi. I was just trying to illustrate with some examples of what I meant by the virtual world created by humans in addition to the natural world that John was referring to in his email. We tend to overlook the enormous amount of information we humans create about things that don’t actually exist in the natural world, at least not in the sense of material objects. Of course, we often create physical representations of some types of such things, but even there many of those representations have been reduced to bit patterns on some digital storage media, aka, “the cloud”. The physical properties of such representations bear no inherent relationship to the things they represent, such as the deed to a piece of property or a movie (which itself may represent something that doesn’t actually exist in the natural world). That general problem was one of the big challenges faced by Sagan and crew when working on the Voyager plaque/disk.
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hpo...@verizon.net
Mihai,
Just to be clear, and maybe I don’t fully understand your comments, I wasn’t focused on the representations, but rather on the things that are being represented. The Prime Meridian, as Ravi’s comments underscore, is not a physical entity and is undetectable in the natural world. Rather it is a construct of human society. So are corporations and other human institutions.
A corporation is not a physical entity. It may be the owner of physical property, but that property is not the corporation. Nor are the pieces of paper that create the corporation the physical corporation. If those pieces of paper are destroyed, the corporation doesn’t go away unless there is a complete breakdown of the society in which the corporation is created. Nor are the officers of the corporation the physical manifestation of the corporation. We have lots of information about such things floating around in cyberspace that represents attributes that are not detectable in the natural world and don’t necessarily obey any particular natural laws. Which is why we developed technologies such as bar codes and RFID, and all the different forms of IDs that we all carry around with us, among others.
Hans
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Nadin, Mihai
Institutions can be interpreted as signs. So can governments, etc. The semiosis, i.e. sign process is open ended. Your comments explain your understanding—and I am sure they inform your decisions.
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hpo...@verizon.net
Mihai,
I am sure that institutions could be interpreted as signs for some contexts and purposes. But that raises the question as to what objects such signs are representing and what sensing and logic mechanisms might be used to detect and interpret them. Or are you saying that institutions can’t be objects at all, just signs? Signs of what? Groups of people that come/work together for some purpose(s) in some context(s)?
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hpo...@verizon.net
Ravi,
I don’t have an answer for your question. Maybe John has some perspectives on it.
I did want to highlight the nature of the problem (which I believe John was trying to do in his email, as well). I suppose that is the first step towards developing a solution to it, or at least an approach to addressing the problem.
I will note that later versions of the original Pioneer plaque added more and different encoded human artistic artifacts that would not be straightforward to interpret by an alien civilization, even if they interpreted the more technical material correctly (i.e., as we intended it to be interpreted). Who knows if artistic material is a common trait of intelligent life? I wonder what they would make of some representation of human history or our current understanding of our planet’s evolution and that of its lifeforms. Or emails from this discussion forum! Maybe they would put up some huge galactic sign saying don’t go near this planet or its lifeforms!
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John F Sowa
Ravi Sharma
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Alex Shkotin
With robots it's better not to use vague terms or sentences. It's dangerous.
Good robots will tell: I don't understand, bad ones can make a mess of things.
Alex
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Alex Shkotin
Alican,
My way is to represent knowledge formally. The precision of knowledge itself remains the same initially and may be better after we apply knowledge processing algorithms to this formalized knowledge.
Precision in Physics and Engineering, and many other sciences and all technologies is an important and well known topic and type of activity. This is a topic to study in every particular science and technology. My work only formalizes this kind of knowledge.
Alex
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Alexandre Rademaker
We don’t necessarily need to throw away the meanings. A safe translation should account for a 1-N mapping.. from surface to logical representations. Context or even some statistical preference can select the most preferable reading.
—-
Alexandre Rademaker
http://arademaker.github.io
Alexandre Rademaker
On 11 Oct 2024, at 06:59, Alex Shkotin <alex.s...@gmail.com> wrote:
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Alex Shkotin
Alexandre,
OMG this vague NL! It is such a pity you did not understand me. Let me explain in more words.
The work described a new structure on how to treat and combine English words into phrases and sentences. My dream is that somebody or maybe myself in the future shall give us an algorithm: chain of words as input, HOL-tree of words as output.
My question was not about which technology to use but the algorithm itself.
Anyway thank you for the overview of possible approaches.
Alex
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Alexandre Rademaker
I guess we will never have a single, unified and correct algorithm to this problem. I mentions some approaches that I know more about, but there are many more variants derived from different schools of thought from syntax to semantics and pragmatics.
Thank you for clarifying and bring this interesting discussion for the forum.
Best,
alex.shkotin
David,
About your question at the end. The main topic is words, phrases, and sentence usage. What are the objects and processes we are talking about?
To keep it simple the robot gets some math structure from reality and we are talking with it "looking" at the same reality.
Robot has finite structure and deterministic algorithms. How accurate it may be in "recognition", "description", conversation and especially action?
`I see the lights of the village glimpse through the rain and darkness. And you, robot?`
Alex
Nadin, Mihai
To Hans Polzer and everyone else interested:
C. S. Peirce: I define a sign as anything which is so determined by something else, called its Object, and so determines an effect upon a person, which effect I call its interpretant, that the later is thereby mediately determined by the former. ( EP2, 478)
Institutions: when interpreted semiotically, i.e., as signs, are defined through their functions in society. They can be seen as signs of a shared understanding (a jail is not a concert house, but could house concerts), or of imitation of other institutions (iconic nature of hospitals, or military barracks), or indexical (finger prints of the economy, such as banks…). Those working in institutions share in the understanding of what they role is, and can affect the manner in which functions are accomplished (the rude bureaucrat is not the same as the helpful nurse or the hurried physician).
Yes, my answer to you is kept as simple as possible.
This is a longer text with more details.
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John F Sowa
John F Sowa
Ravi Sharma
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John F Sowa
@John
Doesn't narrowing down the meaning of a symbol typically lead to a more "precise" interpretation?
If a set of symbols (or sign vehicle) signifies a more limited set of immediate objects, it results in a more specific reference. This increased specificity can lead to
a more focused interpretation (the effect or interpretation in the mind). Overall, sign creation will be more "precise".
E.g., Number 1 and word One. The latter symbol can be interpreted with more things, while the former is less. Overall, isn't the sign-making with Number 1 easier or, in your discussion words, more "precise"?
If I understood something wrong, please correct me.
@Alex
Also, from my observation of Alex's work, in my opinion, that's what he is trying to achieve. Also correct me, Alex, if I understood wrong.
Best,
Alex Shkotin
Alexandre,
I look at the relationship between the HOL-structure and the chain of words not as a translation, but as a restoration of the structure.
When a sentence is formed in the mind, it is a HOL-structure. Now it needs to be spoken, i.e., expressed word by word without using any auxiliary words. And the one who perceived this chain must restore the HOL-structure in his mind.
I have already given an example of a meaningless statement in Russian, which is clearly recognized syntactically, and therefore has a very specific HOL-structure.
The technology for constructing a HOL-structure can be any of those you mentioned.
In the framework of a theory, where the same unit of knowledge is expressed in different natural and formal languages, we can probably talk about translation. I am more accustomed to talking about formalization and verbalization.
Of course, if a unit of knowledge written in Russian or English is presented as a HOL-structure, we can dream about translation.
By the way, a fairly "smart" IDE could build the HOL-structure as the sentence is typed, which would completely save us from the syntactic analysis phase. As stated in the conclusion.
Alex
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Alex Shkotin
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alex.shkotin
John and Alican,
The continuum is just a convenient and powerful abstraction. Yes, the number of atomic nuclei in the bodies around us is huge, but they are stable even when a spaceship takes off or a volcano erupts: the atomic nuclei at the input and output are the same, just a different configuration.
"Alican: Also, from my observation of Alex's work, in my opinion, that's what he is trying to achieve.
Yes. And that is why I keep telling him to avoid turning a true but vague statement into a precise but false statement.
"
To which I always answer that we formalize scientific and technological (engineering) knowledge. And Alexandre Rademaker gave a nice example. We formalize knowledge from two sides: formal ontologies and provers. But we need to unite by keeping theoretic knowledge in a framework.
"The degree of precision should be appropriate to the requirements of the subject matter.
"
Regarding "subject matter", I would clarify: the degree of precision is established when setting the task. So in engineering, if the precision is not specified, it is considered that the error is within half a decimal place beyond the last declared one.
You can order the production of "a perfect sphere of vanilla ice cream that is exactly 10 centimeters in diameter in a cone that is precisely 9.7 cm in diameter at the top and 15 cm in length" but the manufacturing engineer will say that tolerances and fits of 10±0.5 cm and 15±0.5 cm are OK. But 9.7±0.05 cm is for extra money.
The logic of precision is one of the most subtle and important in technologies, engineering, and at home.
We formalize not only theoretical knowledge, but also task solving. We create digital twins for particular objects to apply our formal theories to.
Alex
John F Sowa
Alex Shkotin
John,
For me the brain is a hardware for the mind to live in. And I feel it was hard to embed the mind into the matter.
And for all brain lovers it may be interesting that we now have "Whole-Brain Connectome of an adult female Drosophila. AI-segmented, expert-proofread neurons with millions of connections, crowdsourced labels, and neurotransmitters."
There is no need to develop a universal formalization. There are many of them. It is enough to point to Wolfram language, Lean, Isabelle, Coq, HOL4 etc.
Moreover, for all these universal formalizations (even in Python) the main question is one: what kind of knowledge processing can be done on them?
And here it seems that DL-reasoners are the best in practice so far🎣
Alex
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Nadin, Mihai
Dear and respected Alex Shkotin,
Dear and respected colleagues,
Eight smart things slime molds can do without a brain | NOVA | PBS
Mechanicist view points undermine our ability to understand what things are and how to translate this understanding into computer language. Neither the genome nor the connectome will explain the nature of living processes. The Big-Bang was a time of NO LIFE in whatever form the earth might have been. At this moment (pretty much like von Neumann described it) the biomass of living matter exceeds the mass of non-living matter. And it will continue to increase. Physics and chemistry guided us in understanding the non-living matter. The purposeful living matter is less well understood. https://www.google.com/books/edition/Understanding_Living_Systems/wN_LEAAAQBAJ?hl=en a book claiming
It’s time to admit that genes are not the blueprint for life
https://www.nature.com/articles/d41586-024-00327-x
I can go on. Trees have a mind on their own. So do blades of grass.
But I abstain from bringing myself or my research into the conversation. Unless the perspective changes (“For me the brain is a hardware for the mind to live in” says Shkotin) we will not make progress in improving knowledge representation, and will continue to waste time on talking about formalization without understanding what is formalized. Never mind: the means used in formalization are not neutral. They affect the outcome.
Don’t shoot the pianist if you do not like the music.
Mihai Nadin
From: ontolo...@googlegroups.com <ontolo...@googlegroups.com> On Behalf Of Alex Shkotin
Sent: Sunday, October 13, 2024 5:52 AM
To: ontolo...@googlegroups.com
Cc: CG <c...@lists.iccs-conference.org>
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Ricardo Sanz
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Ricardo Sanz
Head of Autonomous Systems Laboratory
Escuela Técnica Superior de Ingenieros Industriales
Center for Automation and Robotics
Jose Gutierrez Abascal 2.
28006, Madrid, SPAIN
Alican Tüzün
@ricardo
The great philosopher Stephan Pepper explains the mechanistic view as one of the four "world hypotheses," along with formalism, contextualism, and organicism. In this forum, most people lean towards contextualists; hence, the mechanistic view is strongly criticized. However, people here are also smart; hence, they accept the principle of fallibilism.
The mechanistic view according to the SP (from my notes): The world operates like a machine, where all phenomena can be understood through the analysis of their constituent parts and the predictable, causal relationships between them.
Wiki for the book: https://en.wikipedia.org/wiki/World_Hypotheses
Best,
Alican
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Alex Shkotin
Dear and respected Mihai Nadin,
Dear and respected colleagues,
Formalization is understood as the mathematical notation of theories, the objects and processes they study, problem formulations and their solutions. The theory also includes algorithms for actions with objects and processes.
Mathematical notation makes it possible to write more accurately and check using algorithms.
Before moving on to writing a unit of knowledge in a formal (aka mathematical language), theoretical knowledge must be organized, systematized, terminology must be verified. Primary terms must be identified and the rest must receive precise, thorough definitions.
Of course, in order to do all this, it must be precisely indicated what objects and processes we are studying.
The laws of motion of living matter are studied in biology and other life sciences. Formalization of these theories will not make them worse. Rather, we get a concentration of knowledge. And this will be good progress in improving at least the storage and accumulation of knowledge.
Computers also make smart things.
The point of views of a particular science will be formalized (recorded mathematically).
The transition to formalization can be compared to the transition from the Roman numeral system to the one brought by the Arabs.
Formalization does not create new knowledge. It systematizes, concentrates and verifies the existing one.
Therefore, to begin with, the theory is simply ordered and built systematically. As it was with geometry from Euclid to Gilbert.
It is easy to see how useful formalization is when solving any formalized problem using a computer.Let me cite
"We mainly describe the situation of a person working with his mind and not with the help of a processor, and we emphasize the
advantage of formalization. In this case, all a person does is reformulate the task to processor.
(PDF) Specific tasks of Ugraphia on a particular structure (formulations, solutions, placement in the framework). Available from: https://www.researchgate.net/publication/380576198_Specific_tasks_of_Ugraphia_on_a_particular_structure_formulations_solutions_placement_in_the_framework [accessed Oct 14 2024].
"
You say that during formalization, part of the knowledge can be distorted. I am sure that experts will immediately point this out, since formalization is done in close contact with experts in the subject area.
If you have a specific unit of knowledge that cannot be formalized, then what is it?
What is wrong for example here?
"The mission of the GO Consortium is to develop a comprehensive, computational model of biological systems, ranging from the molecular to the organism level, across the multiplicity of species in the tree of life."
Alex
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John F Sowa
Dear and respected Mihai Nadin,
Dear and respected colleagues,
alex.shkotin
JFS:"For continuous subject matter, it may be very bad. It all depends on the application."
Let's take Geometry. Most of the objects there are continuous. Is there an example of a term or task which cannot be formalized properly?
Let's take Hilbert's axioms from here to align terminology and axioms. Later we need the primary source to be absolutely sure.
Alex
Michael DeBellis
John to Alex:
Alex,Your statement (from the end of your note) depends on what subject you're talking about. "Let me remind myself that the English language is formal at its core and for the language of communication between robots and people it is better to simply talk about simple English, etc."No. That depends entirely on the subject matter.. If your sentence is about mathematics, it can be translated very accurately to and from a mathematical formula. But if your statement is about what you see when you open your eyes, every word and phrase about the scene would be vague.Just consider the sentence "I see a blue jay drinking out of the birdbath." There is a continuous infinity of information in the image that you saw. No matter how long you keep describing the situation, a skilled artist could not draw or paint an accurate picture of what you saw.However, if the artist had a chance to look at the scene for just a few seconds, he or she could draw or paint an image that would be far more accurate than anything you could describe.That is just one short example of the difference between the discrete (and describable) and the continuous (and undescribable).Conclusion: An ontology of something that runs on digital computer can be specified precisely in English or Russian or any other natural language. But an ontology of the real world in all its continuous detail can never be expressed precisely in any language with a discrete set of words or symbols.John
John,
I am happy you agreed here:
JFS:"Alex: "We need to formalize our scientific theories to use computers to their full potential." I agree,..."
AS: And the next step is to just align our terminology: not necessarily use the same, but to understand used by other parts.
JFS:"…but the formalization is ALWAYS context dependent. The engineering motto is fundamental:
ALL THEORIES ARE WRONG, BUT SOME ARE USEFUL.
That is true about formalization. It's only precise for subjects that can be expressed in finite bit strings. For 99.9% of all the information we get every second of our lives, vagueness is inescapable. We must deal with it by informal methods of approximations. Any formal statement is FALSE in general, but it may be useful when the limitations are made explicit.
"
AS: We do not use the term context when describing the situation in which the entity being studied is located (usually a system in some state and process). Usually it is described with what other systems and how it interacts and what happens on the border. Remotely acting forces are generally known: gravity and electromagnetic field. Of course we must take into account external flows of bodies, for example particles in the case of ISS. By the way, at the moment for some systems it is necessary to describe their information interaction. You can try to cover all this with the term context, but usually it seems that this is not used. But why not!
I'll write more about finite bit strings later.
In general: our robots must use formal language and algorithmic reasoning and acting. If they are boring we will have to endure it.
Let me remind myself that the English language is formal at its core and for the language of communication between robots and people it is better to simply talk about simple English, etc.
Alex
Alex Shkotin
Michael,
We may discuss separately where in the Chomsky hierarchy are NL. But all this hierarchy is about formal languages.
So your "natural languages (type 0, recursively enumerable sets that require Turing machine to parse)" means that NL are formal 🙂
For "I saw the man on the hill with a telescope" we get at the output of parse phase two syntactically and semantically correct structures (being it derivation-parsing (aka syntax) tree or HOL-structure), and only the pragmatic phase will choose one.
Alexandre Rademaker pointed us here to a great progress on NL parsing: "Still, the English Resource Grammar (http://delph-in.github.io/delphin-viz/demo/) is robust enough for parsing >80% of Wikipedia (in 2006)."
Should we say that English is mainly formal?
Let's talk without "formal, natural".
There are two important questions:
(1) What is the result of the parse phase?
Traditionally for context-free languages it's a derivation-parsing (aka syntax) tree. But there are other ways. My proposal is in (PDF) Program structure.
(2) What is the structure of an NL-sentence? My proposal is (PDF) English is a HOL language message #1X.
But we have many. And here the question is which one is more suitable for further processing. From semantic check to KB uploading.
It is important for semantic rules (aka well-formedness constraints) being coded easily. etc.
And without "formal" my idea is even more straight forward: English sentence is a HOL-expression 🌊
Alex
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Michael DeBellis
We may discuss separately where in the Chomsky hierarchy are NL. But all this hierarchy is about formal languages.
So your "natural languages (type 0, recursively enumerable sets that require Turing machine to parse)" means that NL are formal
For "I saw the man on the hill with a telescope" we get at the output of parse phase two syntactically and semantically correct structures (being it derivation-parsing (aka syntax) tree or HOL-structure), and only the pragmatic phase will choose one.
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Alexandre Rademaker
For those interested to know more about ERG and its parsing capabilities, https://aclanthology.org/L10-1296/
Alex Shkotin
Michael,
I'm a big proponent of using theories and an enthusiast of theoretical knowledge formalization.
So if we'll dive into formal language theory we should accept one in particular to discuss any details. My reference book is [1].
We may also choose one or another theory of NL, from R. Montegue, to Everett, and Chomsky.
And I am happy you never heard that English is a HOLanguage. It's a kind of insight🙂
Only inside a theory and for any term can we decide what kind of ambiguity is possible and how to work with.
And by the way if we work directly with IDE, there is no syntax analysis phase at all, as a sentence immediately is formed as a uni-labeled tree: "If a tree editor is created, then building and storing a program in the form of a tree will make the lexical analysis and parsing unnecessary."
Alex
[1] Aho, A.V and Ullman, J. D. , The Theory of Parsing, Translation, and Compiling, Volume 1: Parsing, Englewood Cliffs, N.J.: Prentice-Hall. (1972)
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Chris Partridge
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Alex Shkotin
Nice topic. One distinction I know is that there are two ways to show a described plane figure: one particular and mirrored. In general they will not be congruent.
By the way, I heard in one seminar that the initial Euclidian text came without any pictures - just text.
If we back to our topic: diagrammatic thinking is formal 🎣
Alex
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John F Sowa
John,
For me the brain is a hardware for the mind to live in. And I feel it was hard to embed the mind into the matter.
And for all brain lovers it may be interesting that we now have "Whole-Brain Connectome of an adult female Drosophila. AI-segmented, expert-proofread neurons with millions of connections, crowdsourced labels, and neurotransmitters."
There is no need to develop a universal formalization. There are many of them. It is enough to point to Wolfram language, Lean, Isabelle, Coq, HOL4 etc.
Moreover, for all these universal formalizations (even in Python) the main question is one: what kind of knowledge processing can be done on them?
And here it seems that DL-reasoners are the best in practice so far.
Alex
Alex Shkotin
JFS:"There is a huge amount of research on the brains of humans and other beasts. It's interesting, but it is not directly relevant to the issues of software design and development."
Yesterday I just listened to an interesting report [1] about modeling the work of the mind and psyche in the paradigm of "software + hardware". The author claims that everything is patented and after accreditation he will tell us the details.
Alex
[1] Модель человеческой психики в перспективе построения искусственной - Владимир Крюков — Семинар AGI
Model of the human psyche in the perspective of constructing an artificial one - Vladimir Kryukov - AGI Seminar
Michael DeBellis
Thanks for your kind words, and for the link to the entertaining video.
(I put the captions on and doubled the speed, I got through about half
of it before I bailed out.)
The TL/DR is that I agree with you. Its easy for some random guy on the
internet to make Big Claims about brain-inspired or brain-mimetic AI,
but it has never worked out in the past and there is no reason to think
that it will do so here either. There is a long history of these sorts
of models in psychology. The most well-known of these that is still in
use is the ACT-R theory from John Anderson (CMU). You can use google
scholar with the search terms "anderson j act-r", lots of stuff comes up.
AFAIK none of these cognitive-psychology-inspired models predicts
behavior under naturalistic conditions. And that is really where the
rubber meets the road. If you can't predict natural behavior then you've
failed the most important generalization test. You might recall that
this is what destroyed Chomsky-inspired approaches to language. They
never predicted real behavior either. What did solve that problem? LLMs.
And LLMs have almost nothing to do with the brain.
The most "brain-like" ML models are the reinforcement learning-based
models that were the main focus of Deep Mind. When those sorts of
systems are trained to solve a specific problem, such as protein folding
or playing Go, they work very, very well. But they don't generalize to
new tasks. So their use has been very limited.
I expect that ML models that more closely reflect our understanding of
human psychology and neuroscience will be developed in the future. But
the future is not now.
Take care,
Jack
-------------------------------------------------------
Jack Gallant
Class of 1940 Chair
Department of Neuroscience
Affiliate, Electrical Engineering and Computer Science
Programs in Bioengineering, Vision Science & Biophysics
co-Director, Henry H. Wheeler Jr. Brain Imaging Center
University of California at Berkeley
web site: http://gallantlab.org
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Nadin, Mihai
Dear and respected colleagues,
To fully endorse the view Michael DeBellis made. As the machine theology dominated the discussion on the brain-mind relation, I felt like asking:
Have you read what those in the brain science so clearly articulated?
Let me suggest: Goerge Musser, Putting ourselves back in the equation, 2023
And even more: Philip Ball, How Life Works, 2023
Those serious about ontology engineering owe it to themselves to be better informed. Jack Gallant is one of the many dedicated scientists who debunked some of the arguments made on ontolog-forum. So did Sejnowski.
Of course, “LLMs have almost nothing to do with the brain.” (the almost is justified: mathematics, which undergirds LLMs, has a lot to do with the brain).
Best wishes.
Mihai Nadin
We owe it to ourselves to be competent!
From: ontolo...@googlegroups.com <ontolo...@googlegroups.com>
On Behalf Of Michael DeBellis
Sent: Friday, November 22, 2024 12:23 PM
To: ontolo...@googlegroups.com
Subject: Re: [ontolog-forum] Designing a top-level processor (was Scientific knowledge formalization
I was going to reply to this by saying "based on what I know about neuroscience..." but then I thought it would be better to get an actual neuroscientist so I sent an email to Jack Gallant at Berkeley. His reply is below. When he says he agrees with me, in my email to him I said that it makes no sense for anyone to claim they are designing hardware and/or software based on the architecture of the mind/bain because there are so many fundamental things we currently don't know about that architecture. There is a difference between technologies inspired by the human brain such as artificial neural nets (which are different from biological neural nets in fundamental ways) and recent architectures that have HW and SW that is analogous to layers and columns in the brain. That is reasonable and has shown impressive results such as LLMs. But to claim that you are essentially mimicking the architecture of the human brain is simply not credible. IMO, claims like this are in the same league as claims that "I've found a way to do cold fusion and will describe my patent for it soon". Dr. Gallant runs the Gallant Lab at UC Berkeley and has done some amazing work such as using fMRI to do "mind reading" and tell what picture a person is viewing from their brain scan. His response is below. BTW, I strongly agree with him where he says "LLMs have almost nothing to do with the brain". The more I've studied LLMs the more I realize how massively different they are from humans learning/using language.
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John F Sowa
jsi...@measures.org
On Nov 22, 2024, at 3:30 PM, John F Sowa <so...@bestweb.net> wrote:
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Alex Shkotin
Michael,
Thank you for the detailed answer. But your point
"I said that it makes no sense for anyone to claim they are designing hardware and/or software based on the architecture of the mind/bain because there are so many fundamental things we currently don't know about that architecture"
what you sent to JG is a little bit far from what I wrote "...modeling the work of the mind and psyche in the paradigm of "software + hardware"".
And all the report was actually about what this guy is knowing about mind and psyche. With a lot of references to main theories developed in this area.
As the result JG criticizes a point which is out of discourse at all: "Its easy for some random guy on the internet to make Big Claims about brain-inspired or brain-mimetic AI", as all the work of the guy is mind and psyche inspired.
But the JG point "none of these cognitive-psychology-inspired models predicts behavior under naturalistic conditions." is important.
Alex
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John F Sowa
John F Sowa
From: hpolzer via ontolog-forum <ontolo...@googlegroups.com>
Understood, Ravi. I was just trying to illustrate with some examples of what I meant by the virtual world created by humans in addition to the natural world that John was referring to in his email. We tend to overlook the enormous amount of information we humans create about things that don’t actually exist in the natural world, at least not in the sense of material objects. Of course, we often create physical representations of some types of such things, but even there many of those representations have been reduced to bit patterns on some digital storage media, aka, “the cloud”. The physical properties of such representations bear no inherent relationship to the things they represent, such as the deed to a piece of property or a movie (which itself may represent something that doesn’t actually exist in the natural world). That general problem was one of the big challenges faced by Sagan and crew when working on the Voyager plaque/disk.
From: ontolo...@googlegroups.com <ontolo...@googlegroups.com> On Behalf Of Ravi Sharma
Hans
Human made markers have been in use for a long time but used to operate in their own small areas of applications.
For example Ujjain India Meridian is where their astronomers calculated time for a millennia or two.
When they tried to apply this in today's context they found that both US and India will be split by the dateline.
Some strange results like metric - US (old British) units' lack of conversion led to space missions failures.
But hope we can correct all such aberrations going forward!
Staying with as close to reality as you suggest, I support.
Regards
Thanks.
Ravi
On Thu, Oct 10, 2024 at 10:43 AM deddy <de...@davideddy.com>
For a natural language, almost every word has a continuous range of meanings.
And this is only for "natural" language.
See classic George Miller "Ambiguous Words"
13 simple Robert Frost words offer 3.6 TRILLION combinations.
https://www.thekurzweillibrary.com/ambiguous-words
So far no acknowledgement at all of the existance of "unnatural language."
Unnatural language being the strings / labels / terms used INSIDE software applications. Many universes of written but minimally spoken terminology that AFAIK is entirely ignored in the current interest in AI & ontologies.
For those who expect "meaning" from statistics... long, long, long ago I encountered an insurance company that had found 70 different "names" for the concept "policy number."
AI LLMs / ontologies address this ... how?
David Eddy
> -------Original Message-------
> From: John F Sowa <so...@bestweb.net>
>
> Alex,
>
> There are two very different issues: (1) Syntactic translation from
> one notation to another; (2) Semantic interpretation of the source or
> target notations.
>
> For a formally defined notation, such as FOL or any notation that is
> defined by its mapping to FOL, there is a single very precise
> definition of its meaning.
>
> For a natural language, almost every word has a continuous range of
> meanings. The only words (or phrases) that have a precise meaning are
> technical terms from some branch of science or engineering. Examples:
> hydrogen, oxygen, volt, ampere, gram, meter...
>
> If you translate a sentence from a natural language to formal
> language, that might narrow down the meaning in the target language,
> But that very precise meaning may be very differentt from what the
> original author had intended.
>
> Summary: Translation is not magic. It cannot make a vague sentence
> precise.
>
> John
> _______________________________________
>
> FROM: "Alex Shkotin" <alex.s...@gmail.com>
>
> John,
>
> Let me clarify what I meant by "English is HOL" by example.
>
> Sentence: "I see a blue jay drinking out of the birdbath."
>
> HOL-structure: (I see ((a (blue jay)) (drinking (out of)) (the
> birdbath)))
>
> where
>
> "of" is an unary operator used in postfix form, applied to "out" being
> an argument. As a result we get "(out of)" an expression or term.
>
> But this term is itself an unary operator used in postfix form,
> applied to "drinking" to create a term "(drinking (out of))", being
> binary operator in infix form being applied to two arguments: left
> one: "(a (blue jay))", and right one: "(the birdbath)".
>
> As a result we have a proposition which is a right argument for
> another binary operator in infix form "see", which has the left
> argument "I".
>
> And we are talking here not about Logic, but about Language.
>
> In every syntactically correct phrase, words are combined: one word is
> applied to another. The result is something like molecules, but in the
> World of Words.
>
> How to get this structure from a chain of words? How to work with
> these structures to get what? Some pictures? True|false value?
>
> This is the question
>
> Alex.
Ravi Sharma
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Alex Shkotin
John,
As far as I remember, the origin of the ancient Greek "meta" was the name given to Aristotle's lectures and books that followed Physics.
So, returning to our topic, it simply means "another language." And then another and another? Hegel called it "bad infinity."
By the way, HOL, or better HOl, I suggest deciphering as a Higher Order language. Many different axioms and rules of inference can be written in this language.
If we talk about metaphysics as a science, Kant singled out three of its main tasks: prove that free will exists, that the soul is immortal and that God exists.
As for reality, objects are involved in processes, and many objects are active. And some objects have their own ideal, in which they invent a lot of things.
As a result, we have: the real world and a bunch of ideal worlds "in people's heads," at least.
What relation thoughts and images have to reality - here it is different.
As Pushkin wrote: "I will shed tears over fiction." ("Над вымыслом слезами обольюсь.")
And fortunately, Higher Order language is enough to formalize knowledge of any level.
The maxim is: give me your language (initial, meta, meta-meta, etc.) and I will formalize it in Isabelle/HOL 🏋️
Look at example [1]. Where we have not only a theorem, but the proof.
But it should be a language for recording knowledge. Algorithmic languages are another topic for me.
Alex
[1] well, it's a FOL as a part of HOL 👑
eng
axioms: Mary is a woman. Any woman is a person.
theorem: Mary is a person.
Isabelle/HOL
theory OWL2_Example
imports Main
begin
typedecl Object
consts Person :: "Object ⇒ bool"
consts Woman :: "Object ⇒ bool"
consts Mary :: "Object"
axiomatization where mary_is_a_woman: "Woman Mary"
axiomatization where woman_subclass_person: "⋀x. Woman x ⟹ Person x"
theorem mary_is_a_person: "Person Mary"
using mary_is_a_woman woman_subclass_person
by auto
end
--