- CG - lists.cs.uni-kassel.de

by John F Sowa

That is certainly true. The people who designed and developed GPT and related LLM-based systems admit that fact: Mihai Nadin: [ChatGPT] is syntactic. I made this claim repeatedly. It is a mimicking machine of high performance (brute computation). But the proponents of Generative AI confuse the issues with a large cloud of highly technical terminology (AKA human generated BS). They claim that if they increase the amount of data to some immense amount, they will have covered all the options so that the probability of finding a correct answer will auto-magically converge to 0.9999999.... They have persuaded Elon Musk and other gullible investors that by pouring more billions and even trillions of $$$ into building ultra-massive computer systems, they will magically become ultra-intelligent. Unfortunately, the WWW has huge amounts of false, fraudulent, mistaken, misled, social media, espionage, counter-espionage, dangerous, and disastrous data. Detecting and deleting all that garbage is extremely difficult. People have tried to use LLM-based technology to find, evaluate, and erase such data -- and they have failed, miserably. As I have repeatedly said, anything LLMs generate is a hypothesis (AKA abduction or guess). Before any abduction can be accepted, it must be evaluated by deduction (AKA reliable reasoning methods). There are almost 80 years of reliable methods developed by AI and computer science. They are essential for reliable computation. All commercial computing systems that require high reliability (banking, engineering, scientific research, aeronautics, space exploration, etc.) require extremely high precision. They also use statistical methods for many purposes, but they use statistics with precise error bounds. Those high precision methods control the world economy and support human life. None of those computations can be replaced by LLM-based methods. Many of them can benefit from LLM-based computations -- but ONLY IF those computations are EVALUATED by traditional deductive methods. John ---------------------------------------- From: "Nadin, Mihai" <nadin(a)utdallas.edu> It is syntactic. I made this claim repeatedly. It is a mimicking machine of high performance (brute computation). Mihai Nadin Sent from planet earth

8 months, 3 weeks

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[CfP] [CoKA] : --- Final Call --- "Conceptual Knowledge Acquisition: Challenges, Opportunities, and Use Cases" workshop at CONCEPTS 2024

by Tom Hanika

***CoKA: --- Final Call for Contributions*** DEADLINE EXTENDED TO: *August 7th, 2024* (23:59 AoE) ================================================================ Conceptual Knowledge Acquisition: Challenges, Opportunities, and Use Cases Workshop at the 1st International Joint Conference on Conceptual Knowledge Structures (CONCEPTS 2024) September 9–13 2024, Cádiz, Spain Workshop Website: https://www.kde.cs.uni-kassel.de/coka/ Conference website: https://concepts2024.uca.es ================================================================ Formal concept analysis (FCA) can help make sense of data and the underlying domain --- provided the data is not too big, not too noisy, representative of the domain, and if there is data in the first place. What if you don’t have such data readily available but are prepared to invest in collecting it and have access to domain experts or other reliable queryable sources of information? Conceptual exploration comes to the rescue! Conceptual exploration is a family of knowledge-acquisition techniques within FCA. The goal is to build a complete implicational theory of a domain (with respect to a fixed language) by posing queries to a domain expert. When properly implemented, it is a great tool that can help organize the process of scientific discovery. Unfortunately, proper implementations are scarce and success stories of using conceptual exploration are somewhat rare and limited in scope. With this workshop, we intend to analyze the situation and, maybe, find a solution. If - you succeeded in acquiring new knowledge about or building a satisfying conceptual representation of some domain with conceptual exploration before; - you attempted conceptual exploration in application to your problem but failed miserably; - you want to use conceptual exploration to analyze some domain, but you don’t know where and how to start; - you are aware of alternatives to conceptual exploration; then come to the workshop to share your experiences, insights, ideas, and concerns with us! ================== Keywords and Topics ================== Knowledge Acquisition and Capture Conceptual Exploration Design Patterns and Paradigmatic Examples successful use cases and real-world applications challenges and lessons learned application principles missing theoretical foundations missing technical infrastructure integration with other theories and technologies ========================= Duration, Format, and Dates ========================= We invite contributions in the form of an extended abstract of up to two pages. In addition, supplementary material, such as data sets, detailed descriptions, or visualizations, may be submitted. The workshop is planned for half a day within the conference dates and at the same venue. It will consist of several short presentations each followed by a plenary discussion. Please send your contributions until *August 7th, 2024* (23:59 AoE) to tom.hanika(a)uni-hildesheim.de. If you are not sure whether your contribution matches the topics or the format of the workshop, you are welcome to contact the organizers prior to submitting the abstract. An acceptance notification will be sent within two weeks upon receiving the submission. =================== Workshop Organizers =================== - Tom Hanika, University of Hildesheim - Sergei Obiedkov, TU Dresden - Bernhard Ganter, Ernst-Schröder-Zentrum, Darmstadt

8 months, 3 weeks

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Constraints and Indications

by Jon Awbrey

Constraints and Indications • 1 • https://inquiryintoinquiry.com/2024/07/02/constraints-and-indications-1-a/ All, The system‑theoretic concept of “constraint” is one that unifies a manifold of other notions — definition, determination, habit, information, law, predicate, regularity, and so on. Indeed, it is often the best way to understand the entire complex of concepts. Entwined with the concept of “constraint” is the concept of “information”, the power signs bear to reduce uncertainty and advance inquiry. Asking what consequences those ideas have for Peirce’s theory of triadic sign relations led me some years ago to the thoughts recorded on the following page. Pragmatic Semiotic Information • https://oeis.org/wiki/Pragmatic_Semiotic_Information Here I am thinking of the concept of constraint that constitutes one of the fundamental ideas of classical cybernetics and mathematical systems theory. For example, here is how W. Ross Ashby introduces the concept of constraint in his Introduction to Cybernetics (1956). ❝A most important concept, with which we shall be much concerned later, is that of “constraint”. It is a relation between two sets, and occurs when the variety that exists under one condition is less than the variety that exists under another. Thus, the variety of the human sexes is 1 bit; if a certain school takes only boys, the variety in the sexes within the school is zero; so as 0 is less than 1, constraint exists.❞ (1964 ed., p. 127). At its simplest, then, constraint is an aspect of the subset relation. The objective of an agent, organism, or similar regulator is to keep within its viable region, a particular subset of its possible state space. That is the constraint of primary interest to the agent. Reference — • Ashby, W.R. (1956), Introduction to Cybernetics, Methuen, London, UK. Resources — Survey of Cybernetics • https://inquiryintoinquiry.com/2024/01/25/survey-of-cybernetics-4/ Survey of Inquiry Driven Systems • https://inquiryintoinquiry.com/2024/02/28/survey-of-inquiry-driven-systems-… Survey of Pragmatic Semiotic Information • https://inquiryintoinquiry.com/2024/03/01/survey-of-pragmatic-semiotic-info… Regards, Jon cc: https://www.academia.edu/community/VrKv7y

9 months

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Re: [ontolog-forum] top layer ontologies, domain layer, application layer and data models

by John F Sowa

Alex and Lars, The issues are complex, and they require a major effort to explain in detail. It's not a task that an email list -- such as Ontolog Forum -- can even begin to address. For a start, I recommend 117 slides (with many, many links and references): https://www.jfsowa.com/talks/vrmind.pdf . I am not claiming that my 117 slides solve or explain all the issues. But they summarize many issues and point to many more references for further details and explanations. Alex: I prefer First Order LANGUAGE. As there are so many logics right now. And by the way, the FOL framework (as we discussed after Barwise) does not have numbers of any kind. Every version of FOL is isomorphic to the versions that were independently discovered by Frege (1879) and Peirce (1885). Nobody but Frege ever used his notation. But everybody adopted Peirce's version with minor changes in the choice of symbols. Most importantly, anything stated in one version can be mapped to and from every other version automatically without the slightest change of meaning. However, there are various subsets and supersets of FOL. The Object Management Group (OMG) developed the DOL standard for defining the mappings among them. The HeTS system can automatically map any DOL notation to and from equivalent notations. It can also map any notation to any more expressive notation. As for numbers, they are a family of systems that can be defined in FOL. As soon as the axioms are added to the set of FOL specifications, numbers become available. Please read slides 84 to 105 of vrmind.pdf. Lars: Information is actually not a good word for what is stored in the brain. Try mneme (as coined and defined by Richard Semon) or (retrievable/mnemic) engram. One reason being - in short - that information (the process) is rather associated with the creation of engrams. And information as the stimulus of perception is also different from engrams. I agree. I was using the word 'information' for what is stored in computer systems. Please see the 117 slides of vrmind.pdf. I admit that 117 slides require a large amount of reading. But I suggest that you just start at slide 2 and flip through the slides until you find something interesting. In summary, there are many other groups that do detailed R & D and specifications of standards. As a mailing list that also sponsors various conferences, Ontolog Forum is not a place for developing standards. Anybody who wants to do such work should join a project that does develop standards. John

9 months

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ChatGPT is BS

by John F Sowa

I used the abbreviation BS to avoid being flagged by things that flag stuff. The authors are not condemning ChatGPT. As they say, "We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs" . . . I agree with that comment. It emphasizes my point: LLMs generate hypotheses (guesses) whose truth values are unknown. Technically, they may be called abductions. Further testing and deduction are necessary before any abduction can be trusted. Following is the abstract of the article at https://link.springer.com/article/10.1007/s10676-024-09775-5 John ______________________________ ChatGPT is bullshit Michael Townsen Hicks · James Humphries · Joe Slater Abstract: Recently, there has been considerable interest in large language models: machine learning systems which produce humanlike text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.

9 months

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Google AI does not understand satire

by John F Sowa

Following is an offline email note that shows why nobody is using LLMs to check Social Media for notes that should be deleted or flagged as dangerous for one reason or another. That is a very important application that would be worth a huge amount of money if it could be done accurately. John _____________ On Jun 15, 2024, John F Sowa wrote: But Google AI does not understand satire. On Jun 16, 2024 XXX wrote: Or sarcasm, humor, anger, internet flame wars, stupidity, poetry, hunger or any other emotion or part of being intelligent or self-conscious. Turns out that a gussied up autocorrect doesn’t handle subtlety. Who knew? No one knew it was that hard. What is an emotion? https://www.gocomics.com/doonesbury/1974/09/20

9 months, 1 week

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Theme One Program • Jets and Sharks

by Jon Awbrey

Theme One Program • Jets and Sharks 1 • https://inquiryintoinquiry.com/2024/06/20/theme-one-program-jets-and-sharks… All, It is easy to spend a long time on the rudiments of learning and logic before getting down to practical applications — but I think we've circled square one long enough to expand our scope and see what the category of programs envisioned in Theme One can do with more substantial examples and exercises. During the development of the Theme One program I tested successive implementations of its Reasoning Module or Logical Modeler on examples of logical problems current in the literature of the day. The PDP Handbook of McClelland and Rumelhart set one of the wittiest gems ever to whet one's app‑titude so I could hardly help but take it on. The following text is a light revision of the way I set it up in the program's User Guide. Example 5. Jets and Sharks — The propositional calculus based on “minimal negation operators” can be interpreted in a way resembling the logic of activation states and competition constraints in one class of neural network models. One way to do this is to interpret the blank or unmarked state as the resting state of a neural pool, the bound or marked state as its activated state, and to represent a mutually inhibitory pool of neurons A, B, C by the minimal negation proposition (A , B , C). The manner of representation may be illustrated by transcribing a well‑known example from the parallel distributed processing literature (McClelland and Rumelhart 1988) and working through a couple of the associated exercises as translated into logical graphs. Minimal Negation Operators • https://oeis.org/wiki/Minimal_negation_operator Displayed below is the text expression of a traversal string which Theme One parses into a cactus graph data structure in computer memory. The cactus graph represents a single logical formula in propositional calculus and this proposition embodies all the logical constraints defining the Jets and Sharks data base. Theme One Guide • Jets and Sharks • Log File • https://inquiryintoinquiry.files.wordpress.com/2022/08/theme-one-guide-e280… References — • McClelland, J.L. (2015), Explorations in Parallel Distributed Processing : A Handbook of Models, Programs, and Exercises, 2nd ed. (draft), Stanford Parallel Distributed Processing Lab. ( https://web.stanford.edu/group/pdplab/ ) ( https://web.stanford.edu/group/pdplab/pdphandbook/ ) Section 2.3 ( https://web.stanford.edu/group/pdplab/pdphandbook/handbookch3#x7-320002.3 ) Figure 2.1 ( https://web.stanford.edu/group/pdplab/pdphandbook/jetsandsharkstable.png ) • McClelland, J.L., and Rumelhart, D.E. (1988), Explorations in Parallel Distributed Processing : A Handbook of Models, Programs, and Exercises, MIT Press, Cambridge, MA. “Figure 1. Characteristics of a number of individuals belonging to two gangs, the Jets and the Sharks”, p. 39, from McClelland (1981). • McClelland, J.L. (1981), “Retrieving General and Specific Knowledge From Stored Knowledge of Specifics”, Proceedings of the Third Annual Conference of the Cognitive Science Society, Berkeley, CA. Regards, Jon cc: https://www.academia.edu/community/VWYGZo

9 months, 1 week

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Principia Logico-Metaphysica by Edward N. Zalta

by John F Sowa

Alex, Thanks for that info. Zalta's Principia is a huge compendium (1501 pages with more to come) of formal definitions and proofs. The Isabelle theorem prover is an excellent basis for automated reasoning with and about anything that uses those definitions. And the supporting software can be downloaded for free. As I said before, I was highly skeptical about any formal definitions developed in Ontolog Forum, because this is not a standards organization. However, if the definitions are taken from or based on Zalta's Principia and supported by Isabelle, they can be posted in web pages shared by anybody and everybody. I'm sure that Stanford would support or collaborate with anybody who develops such a repository. Re Top-Level Ontologies: The TLOs are specified in very restricted formats in a tiny subset of first-order logic. Isabelle supports all of FOL plus much, much more. As I have been pointing out, FOL is much simpler, more general, easier to use, and faster than OWL2. The availability of Isabelle with all of the definitions in Zalta's tome would make a strong argument for OWL3 (the same notation for the hierarchy as OWL2, but full FOL as an upgrade). There is a question about Common Logic, which is a different extension to FOL, I believe that the HeTS tools could be extended to support mappings of CL to and from Isabelle. There are various issues about the mappings, and HeTS is sufficiently general that it could be extended to support them. Any TLO specified in OWL can be automatically mapped to FOL (the extension to OWL3) and used with anything specified in the Zalta's system. Although Isabelle supports HOL, a large majority of the structures defined by Zalta use only the FOL subset. Common Logic is a different extension to FOL. The HeTS tools and the OMG standard for DOL could be used to do the mappings. Since Isabelle also supports FOL as a subset, it would be a good tool for supporting OWL3. That's another argument for getting rid of the limitations and exceptions of OWL2 and move to OWL3. Existing applications that use OWL2 would not be affected. But new applications could use the simpler OWL3 notation. For more about Isabelle and links to resources, see the Wikipedia page: https://en.wikipedia.org/wiki/Isabelle_(proof_assistant) . John _______________________________________ From: "alex.shkotin" <alex.shkotin(a)gmail.com> Colleagues, Edward N. Zalta is well known for to be co-principal editor of https://plato.stanford.edu/info.html . I just got the URL to his axiomatic theory https://mally.stanford.edu/principia.pdf may be interesting to compare with TLOs. It is not only axiomatic but formalized one using Isabelle/HOL. Alex

9 months, 1 week

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Re: [ontolog-forum] Why ChatGPT does not trust itself as an author

by John F Sowa

Mike, I agree with your comments below. As I keep repeating, LLMs are an extremely valuable ADDITION to the AI toolkit. They have a wide variety of applications, especially for translating languages, natural and artificial. But they are an addition, not a replacement. Since "ontology' is the focus of this forum, I would emphasize the role of ontology in evaluating, testing, revising, and enhancing the output generated by LLMs. For reasoning, LLMs are an excellent method for abduction (guessing). But by themselves, they cannot do deduction, testing, and evaluation. They are able to find and apply certain patterns of deduction. and if their source data is limited to a single set of consistent statements, the results are usually correct. But "consistent" and "usually" are problematical. That is why we need methods that control how the results LLMs generate are tested, evaluated, and used. Kingsley does that. Wolfram does that. Our Permion.ai company does that. In fact, you do that when you work with LLM-based software and make your own decisions about what to use or ignore. There are many more options and combinations to explore. But it's important to remember that somebody or something must test and evaluate what to do with the LLM output. GOFAI (Good Old Fashioned AI) is not obsolete. John ________________________________________ From: "Mike Bergman" <mike(a)mkbergman.com> Hi All, In the sense of fairness by providing an alternative viewpoint, I prompted ChatGPT 4o (as of today) with the inverse question. I am not personally endorsing the practice, and I further believe any LLM used to support an academic (or other) manuscript should be disclosed as to how used, even if allowed by the publisher. Best, Mike

9 months, 2 weeks

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Why ChatGPT does not trust itself as an author

by John F Sowa

I recommend the following article as an appendix to the Communiqué on the recent Ontology Summit. It is a succinct summary of the issues. John ____________________________ Why ChatGPT Should Not Be Used to Write Academic Scientific Manuscripts for PublicationThe Annals of Family Medicine, March 2023, 2958; DOI: https://doi.org/10.1370/afm.2982 Editor Caroline Richardson asked Chat GPT-4, OpenAI’s advanced language processing tool, about authoring scientific manuscripts. Prompt: Write a short editorial about why ChatGPT should not be used to write academic scientific manuscripts for publication. Response: A language model trained by OpenAI, ChatGPT is a remarkable tool that can generate coherent and grammatically correct text on a wide range of topics. However, when it comes to writing academic scientific manuscripts for publication, ChatGPT falls short in several critical ways. First and foremost, ChatGPT lacks the domain-specific knowledge and expertise that is essential for producing high quality scientific manuscripts. While it may be able to generate text that sounds plausible and coherent, it cannot provide the kind of nuanced and sophisticated analysis that is required for scientific research. This is especially true for research that involves complex statistical analyses, experimental design, and interpretation of results. Furthermore, ChatGPT cannot ensure the accuracy, validity, and reliability of scientific claims and findings. Scientific research depends on the rigorous collection and analysis of data, and the careful interpretation of results. This requires not only expertise in the relevant field of study, but also a keen eye for detail and a commitment to the highest standards of scientific rigor. Another key limitation of ChatGPT is its inability to engage in the iterative and collaborative process that is characteristic of scientific research. Writing a scientific manuscript is not a one-time event, but rather a process of continuous refinement and improvement. This process involves multiple rounds of feedback and revision, as well as collaboration with colleagues and reviewers to ensure that the final product meets the highest standards of quality. In conclusion, while ChatGPT may be a useful tool for generating ideas and basic drafts of text, it should not be relied upon for writing academic scientific manuscripts for publication. The complex and nuanced nature of scientific research requires the expertise and input of human researchers who can ensure the accuracy, validity, and reliability of scientific claims and findings, as well as engage in the iterative and collaborative process that is essential for producing high quality scientific manuscripts. Submitted March 20, 2023; accepted March 20, 2023. Disclaimer: This is the only time a chatbot will be allowed or cited as an author in Annals of Family Medicine. See our Policy on Authorship.

9 months, 2 weeks

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