In the last months I was engaged with the topic of text-generating algorithms and the possible impact for a scientific discourse (some first notices to this discussion you can find here (https://www.uffmm.org/2023/08/24/homo-sapiens-empirical-and-sustained-empirical-theories-emotions-and-machines-a-sketch/)). In this context it is important to clarify the role and structure of human actors as well as the concept of Intelligence. Meanwhile I have abandoned the word Intelligence completely because the inflationary use in today mainstream pulverises any meaning. Even in one discipline — like psychology — you can find many different concepts. In this context I have read the book of Stanovich et.al to have a prominent example of using the concept of intelligence, there combined with the concept of rationality, which is no less vague.
The book “The Rationality Quotient” from 2016 represents not the beginning of a discourse but is a kind of summary of a long lasting discourse with many publications before. This makes this book interesting, but also difficult to read in the beginning, because the book is using nearly on every page theoretical terms, which are assumed to be known to the reader and cites other publications without giving sufficient explanations why exactly these cited publications are important. This is no argument against this book but sheds some light on the reader, who has to learn a lot to understand the text.
A text with the character of summing up its subject is good, because it has a confirmed meaning about the subject which enables a kind of clarity which is typical for that state of elaborated point of view.
In the following review it is not the goal to give a complete account of every detail of this book but only to present the main thesis and then to analyze the used methods and the applied epistemological framework.
Main Thesis of the Book
The reviewing starts with the basic assumptions and the main thesis.
FIGURE 1 : The beginning. Note: the number ‘2015’ has to be corrected to ‘2016’.
FIGURE 2 : First outline of cognition. Note: the number ‘2015’ has to be corrected to ‘2016’.
As mentioned in the introduction you will in the book not find a real overview about the history of psychological research dealing with the concept of Intelligence and also no overview about the historical discourse to the concept of Rationality, whereby the last concept has also a rich tradition in Philosophy. Thus, somehow you have to know it.
There are some clear warnings with regard to the fuzziness of the concept rationality (p.3) as well as to the concept of intelligence (p.15). From a point of view of Philosophy of Science it could be interesting to know what the circumstances are which are causing such a fuzziness, but this is not a topic of the book. The book talks within its own selected conceptual paradigm. Being in the dilemma, of what kind of intelligence paradigm one wants to use, the book has decided to work with the Cattell-Horn-Carroll (CTC) paradigm, which some call a theory. 
Directly from the beginning it is explained that the discussion of Intelligence is missing a clear explanation of the full human model of cognition (p.15) and that intelligence tests therefore are mostly measuring only parts of human cognitive functions. (p.21)
Thus let us have a more detailed look to the scenario.
 For a first look to the Cattell–Horn–Carroll theory see: https://en.wikipedia.org/wiki/Cattell%E2%80%93Horn%E2%80%93Carroll_theory, a first overview.
Which point of View?
The book starts with a first characterization of the concept of Rationality within a point of view which is not really clear. From different remarks one gets some hints to modern Cognitive Science (4,6), to Decision Theory (4) and Probability Calculus (9), but a clear description is missing.
And it is declared right from the beginning, that the main aim of the book is the Construction of a rational Thinking Test (4), because for the authors the used Intelligence Tests — later reduced to the Carroll-Horn-Carroll (CHC) type of intelligence test (16) — are too narrow in what they are measuring (15, 16, 21).
Related to the term Rationality the book characterizes some requirements which the term rationality should fulfill (e.g. ‘Rationality as a continuum’ (4), ’empirically based’ (4), ‘operationally grounded’ (4), a ‘strong definition’ (5), a ‘normative one’ (5), ‘normative model of optimum judgment’ (5)), but it is more or less open, what these requirements imply and what tacit assumptions have to be fulfilled, that this will work.
The two requirements ’empirically based’ as well as ‘operationally grounded’ point in the direction of an tacitly assumed concept of an empirical theory, but exactly this concept — and especially in association with the term cognitive science — isn’t really clear today.
Because the authors make in the next pages a lot of statements which claim to be serious, it seems to be important for the discussion in this review text to clarify the conditions of the ‘meaning of language expressions’ and of being classified as ‘being true’.
If we assume — tentatively — that the authors assume a scientific theory to be primarily a text whose expressions have a meaning which can transparently be associated with an empirical fact and if this is the case, then the expression will be understood as being grounded and classified as true, then we have characterized a normal text which can be used in everyday live for the communication of meanings which can become demonstrated as being true.
Is there a difference between such a ‘normal text’ and a ‘scientific theory’? And, especially here, where the context should be a scientific theory within the discipline of cognitive science: what distinguishes a normal text from a ‘scientific theory within cognitive science’?
Because the authors do not explain their conceptual framework called cognitive science we recur here to a most general characterization [2,3] which tells us, that cognitive science is not a single discipline but an interdisciplinary study which is taking from many different disciplines. It has not yet reached a state where all used methods and terms are embedded in one general coherent framework. Thus the relationship of the used conceptual frameworks is mostly fuzzy, unclear. From this follows directly, that the relationship of the different terms to each other — e.g. like ‘underlying preferences’ and ‘well ordered’ — is within such a blurred context rather unclear.
Even the simple characterization of an expression as ‘having an empirical meaning’ is unclear: what are the kinds of empirical subjects and the used terms? According to the list of involved disciplines the disciplines linguistics , psychology  or neuroscience  — besides others — are mentioned. But every of these disciplines is itself today a broad field of methods, not integrated, dealing with a multifaceted subject.
Using an Auxiliary Construction as a Minimal Point of Reference
Instead of becoming somehow paralyzed from these one-and-all characterizations of the individual disciplines one can try to step back and taking a look to basic assumptions about empirical perspectives.
If we take a group of HumanObservers which shall investigate these subjects we could make the following assumptions:
Empirical Linguistics is dealing with languages, spoken as well as written by human persons, within certain environments, and these can be observed as empirical entities.
Empirical Psychology is dealing with the behavior of human persons (a kind of biological systems) within certain environments, and these can be observed.
Empirical Neuroscience is dealing with the brain as part of a body which is located in some environment, and this all can be observed.
The empirical observations of certain kinds of empirical phenomena can be used to define more abstract concepts, relations, and processes. These more abstract concepts, relations, and processes have ‘as such’ no empirical meaning! They constitute a formal framework which has to become correlated with empirical facts to get some empirical meaning. As it is known from philosophy of science  the combination of empirical concepts within a formal framework of abstracts terms can enable ‘abstract meanings’ which by logical conclusions can produce statements which are — in the moment of stating them — not empirically true, because ‘real future’ has not yet happened. And on account of the ‘generality’ of abstract terms compared to the finiteness and concreteness of empirical facts it can happen, that the inferred statements never will become true. Therefore the mere usage of abstract terms within a text called scientific theory does not guarantee valid empirical statements.
And in general one has to state, that a coherent scientific theory including e.g. linguistics, psychology and neuroscience, is not yet in existence.
To speak of cognitive science as if this represents a clearly defined coherent discipline seems therefore to be misleading.
This raises questions about the project of a constructing a coherent rational thinking test (CART).
‘CART’ TEST FRAMEWORK – A Reconstruction from the point of View of Philosophy of Science
Before I will dig deeper into the theory I try to understand the intended outcome of this theory as some point of reference. The following figure 3 gives some hints.
FIGURE 3 : Outline of the Test Framework based on the Appendix in Stanovich et.al 2016. This Outline is a Reconstruction by the author of this review.
It seems to be important to distinguish at least three main parts of the whole scientific endeavor:
The group of scientists which has decided to process a certain problem.
The generated scientific theory as a text.
The description of a CART Test, which describes a procedure, how the abstract terms of the theory can be associated with real facts.
From the group of scientists (Stanovich et al.) we know that they understand themselves as cognitive scientists (without having a clear characterization, what this means concretely).
The intended scientific theory as a text is here assumed to be realized in the book, which is here the subject of a review.
The description of a CART Test is here taken from the appendix of the book.
To understand the theory it is interesting to see, that in the real test the test system (assumed here as a human person) has to read (and hear?) a instruction, how to proceed with a task form, and then the test system (a human person) has to process the test form in the way it has understood the instructions and the test form as it is.
The result is a completed test form.
And it is then this completed test form which will be rated according to the assumed CART theory.
This complete paradigm raises a whole bunch of questions which to answer here in full is somehow out of range.
Mix-Up of Abstract Terms
Because the Test Scenario presupposes a CART theory and within this theory some kind of a model of intended test users it can be helpful to have a more closer look to this assumed CART model, which is located in a person.
FIGURE 4 : General outline of the logic behind CART according to Stanovich et al. (2016).
The presented cognitive architecture shall present a framework for the CART (Comprehensive Assessment of Rational Thinking), whereby this framework is including a model. The model is not only assumed to contextualize and classify heuristics and tasks, but it also presents Rationality in a way that one can deduce mental characteristics included in rationality.(cf. 37)
Because the term Rationality is not an individual empirical fact but an abstract term of a conceptual framework, this term has as such no meaning. The meaning of this abstract term has to be arranged by relations to other abstract terms which themselves are sufficiently related to concrete empirical statements. And these relations between abstract terms and empirical facts (represented as language expressions) have to be represented in a manner, that it is transparent how the the measured facts are related to the abstract terms.
Here Stanovich et al. is using another abstract term Mind, which is associated with characteristics called mental characteristics: Reflective mind, Algorithmic Level, and Mindware.
And then the text tells that Rationality is presenting mental characteristics. What does this mean? Is rationality different from the mind, who has some characteristics, which can be presented from rationality using somehow the mind, or is rationality nevertheless part of the mind and manifests themself in these mental characteristics? But what kind of the meaning could this be for an abstract term like rationality to be part of the mind? Without an explicit model associated with the term Mind which arranges the other abstract term Rationality within this model there exists no meaning which can be used here.
These considerations are the effect of a text, which uses different abstract terms in a way, which is rather unclear. In a scientific theory this should not be the case.
Measuring Degrees of Rationality
In the beginning of chapter 4 Stanovich et al. are looking back to chapter 1. Here they built up a chain of arguments which illustrate some general perspective (cf. 63):
Rationality has degrees.
These degrees of rationality can be measured.
Measurement is realized by experimental methods of cognitive science.
The measuring is based on the observable behavior of people.
The observable behavior can manifest whether the individual actor (a human person) follows assumed preferences related to an assumed axiom of choice.
Observable behavior which is classified as manifesting assumed internal preferences according to an assumed internal axiom of choice can show descriptive and procedural invariance.
Based on these deduced descriptive and procedural invariance, it can be inferred further, that these actors are behaving as if they are maximizing utility.
It is difficult to assess utility maximization directly.
It is much easier to assess whether one of the axioms of rational choice is being violated.
These statements characterize the Logic of the CART according to Stanovich et al. (cf.64)
A major point in this argumentation is the assumption, that observable behavior is such, that one can deduce from the properties of this behavior those attributes/ properties, which point (i) to an internal model of an axiom of choice, (ii) to internal processes, which manifest the effects of this internal model, (iii) to certain characteristics of these internal processes which allow the deduction of the property of maximizing utility or not.
These are very strong assumptions.
If one takes further into account the explanations from the pages 7f about the required properties for an abstract term axiom of choice (cf. figure 1) then these assumptions appear to be very demanding.
Can it be possible to extract the necessary meaning out of observable behavior in a way, which is clear enough by empirical standards, that this behavior shows property A and not property B ?
As we know from the description of the CART in the appendix of the book (cf. figure 3) the real behavior assumed for an CART is the (i) reading (or hearing?) of an instruction communicated by ordinary English, and then (ii) a behavior deduced from the understanding of the instruction, which (iii) manifests themself in the reading of a form with a text and filling out this form in predefined positions in a required language.
This described procedure is quite common throughout psychology and similar disciplines. But it is well known, that the understanding of language instructions is very error-prone. Furthermore, the presentation of a task as a text is inevitably highly biased and additionally too very error-prone with regard to the understanding (this is a reason why in usability testing purely text-based tests are rather useless).
The point is, that the empirical basis is not given as a protocol of observations of language free behavior but of a behavior which is nearly completely embedded in the understanding and handling of texts. This points to the underlying processes of text understanding which are completelyinternal to the actor. There exists no prewired connection between the observable strings of signs constituting a text and the possible meaning which can be organized by the individual processes of text understanding.
Having reached this point of reading and trying to understand I decided to stop here: to many questions on all levels of a scientific discourse and the relationships between main concepts and terms appear in the book of Stanovich et al. to be not clear enough. I feel therefore confirmed in my working hypothesis from the beginning, that the concept of intelligence today is far too vague, too ambiguous to contain any useful kernel of meaning any more. And concepts like Rationality, Mind (and many others) seem to do not better.
Chatting with chatGPT4
Since April 2023 I have started to check the ability of chatGPT4 to contribute to a philosophical and scientific discourse. The working hypothesis is, that chatGPT4 is good in summarizing the common concepts, which are used in public texts, but chatGPT is not able for critical evaluations, not for really new creative ideas and in no case for systematic analysis of used methods, used frameworks, their interrelations, their truth-conditons and much more, what it cannot. Nevertheless, it is a good ‘common sense check’. Until now I couldn’t learn anything new from these chats.
If you have read this review with all the details and open questions you will be perhaps a little bit disappointed about the answers from chatGPT4. But keep calm: it is a bit helpful.
Time: Oct 18, 2023 — Oct 24, 2023) Author: Gerd Doeben-Henisch Email: firstname.lastname@example.org
This post is part of the uffmm science blog. It is a translation from the German source: https://www.cognitiveagent.org/2023/10/18/schmerz-ersetzt-nicht-die-wahrheit/. For the translation I have used chatGPT4 and deepl.com. Because in the text the word ‘hamas’ is occurring, chatGPT didn’t translate a long paragraph with this word. Thus the algorithm is somehow ‘biased’ by a certain kind of training. This is really bad because the following text is offers some reflections about a situation where someone ‘hates’ others. This is one of our biggest ‘disease’ today.
The Hamas terrorist attack on Israeli citizens on October 7, 2023, has shaken the world. For years, terrorist acts have been shaking our world. In front of our eyes, a is attempting, since 2022 (actually since 2014), to brutally eradicate the entire Ukrainian population. Similar events have been and are taking place in many other regions of the world…
… Pain does not replace the truth …
Truth is not automatic. Making truth available requires significantly more effort than remaining in a state of partial truth.
The probability that a person knows the truth or seeks the truth is smaller than staying in a state of partial truth or outright falsehood.
Whether in a democracy, falsehood or truth predominates depends on how a democracy shapes the process of truth-finding and the communication of truth. There is no automatic path to truth.
In a dictatorship, the likelihood of truth being available is extremely dependent on those who exercise centralized power. Absolute power, however, has already fundamentally broken with the truth (which does not exclude the possibility that this power can have significant effects).
The course of human history on planet Earth thus far has shown that there is evidently no simple, quick path that uniformly leads all people to a state of happiness. This must have to do with humans themselves—with us.
The interest in seeking truth, in cultivating truth, in a collective process of truth, has never been strong enough to overcome the everyday exclusions, falsehoods, hostilities, atrocities…
One’s own pain is terrible, but it does not help us to move forward…
Who even wants a future for all of us?????
 There is an overview article by the author from 2018, in which he presents 15 major texts from the blog “Philosophie Jetzt” ( “Philosophy Now”) ( “INFORMAL COSMOLOGY. Part 3a. Evolution – Truth – Society. Synopsis of previous contributions to truth in this blog” ( https://www.cognitiveagent.org/2018/03/20/informelle-kosmologie-teil-3a-evolution-wahrheit-gesellschaft-synopse-der-bisherigen-beitraege-zur-wahrheit-in-diesem-blog/ )), in which the matter of truth is considered from many points of view. In the 5 years since, society’s treatment of truth has continued to deteriorate dramatically.
Hate cancels the truth
Truth is related to knowledge. However, in humans, knowledge most often is subservient to emotions. Whatever we may know or wish to know, when our emotions are against it, we tend to suppress that knowledge.
One form of emotion is hatred. The destructive impact of hatred has accompanied human history like a shadow, leaving a trail of devastation everywhere it goes: in the hater themselves and in their surroundings.
The event of the inhumane attack on October 7, 2023 in Israel, claimed by Hamas, is unthinkable without hatred.
If one traces the history of Hamas since its founding in 1987 [1,2], then one can see that hatred is already laid down as an essential moment in its founding. This hatred is joined by the moment of a religious interpretation, which calls itself Islamic, but which represents a special, very radicalized and at the same time fundamentalist form of Islam.
The history of the state of Israel is complex, and the history of Judaism is no less so. And the fact that today’s Judaism also contains strong components that are clearly fundamentalist and to which hatred is not alien, this also leads within many other factors at the core to a constellation of fundamentalist antagonisms on both sides that do not in themselves reveal any approaches to a solution. The many other people in Israel and Palestine ‘around’ are part of these ‘fundamentalist force fields’, which simply evaporate humanity and truth in their vicinity. By the trail of blood one can see this reality.
Both Judaism and Islam have produced wonderful things, but what does all this mean in the face of a burning hatred that pushes everything aside, that sees only itself.
 Jeffrey Herf, Sie machen den Hass zum Weltbild, FAZ 20.Okt. 23, S.11 (Abriss der Geschichte der Hamas und ihr Weltbild, als Teil der größeren Geschichte) (Translation:They make hatred their worldview, FAZ Oct. 20, 23, p.11 (outlining the history of Hamas and its worldview, as part of the larger story)).
 Joachim Krause, Die Quellen des Arabischen Antisemitismus, FAZ, 23.10.2023,p.8 (This text “The Sources of Arab Anti-Semitism” complements the account by Jeffrey Herf. According to Krause, Arab anti-Semitism has been widely disseminated in the Arab world since the 1920s/ 30s via the Muslim Brotherhood, founded in 1928).
A society in decline
When truth diminishes and hatred grows (and, indirectly, trust evaporates), a society is in free fall. There is no remedy for this; the use of force cannot heal it, only worsen it.
The mere fact that we believe that lack of truth, dwindling trust, and above all, manifest hatred can only be eradicated through violence, shows how seriously we regard these phenomena and at the same time, how helpless we feel in the face of these attitudes.
In a world whose survival is linked to the availability of truth and trust, it is a piercing alarm signal to observe how difficult it is for us as humans to deal with the absence of truth and face hatred.
Is Hatred Incurable?
When we observe how tenaciously hatred persists in humanity, how unimaginably cruel actions driven by hatred can be, and how helpless we humans seem in the face of hatred, one might wonder if hatred is ultimately not a kind of disease—one that threatens the hater themselves and, particularly, those who are hated with severe harm, ultimately death.
With typical diseases, we have learned to search for remedies that can free us from the illness. But what about a disease like hatred? What helps here? Does anything help? Must we, like in earlier times with people afflicted by deadly diseases (like the plague), isolate, lock away, or send away those who are consumed by hatred to some no man’s land? … but everyone knows that this isn’t feasible… What is feasible? What can combat hatred?
After approximately 300.000 years of Homo sapiens on this planet, we seem strangely helpless in the face of the disease of hatred.
What’s even worse is that there are other people who see in every hater a potential tool to redirect that hatred toward goals they want to damage or destroy, using suitable manipulation. Thus, hatred does not disappear; on the contrary, it feels justified, and new injustices fuel the emergence of new hatred… the disease continues to spread.
One of the greatest events in the entire known universe—the emergence of mysterious life on this planet Earth—has a vulnerable point where this life appears strangely weak and helpless. Throughout history, humans have demonstrated their capability for actions that endure for many generations, that enable more people to live fulfilling lives, but in the face of hatred, they appear oddly helpless… and the one consumed by hatred is left incapacitated, incapable of anything else… plummeting into their dark inner abyss…
Instead of hatred, we need (minimally and in outline):
Water: To sustain human life, along with the infrastructure to provide it, and individuals to maintain that infrastructure. These individuals also require everything they need for their own lives to fulfill this task.
Food: To sustain human life, along with the infrastructure for its production, storage, processing, transportation, distribution, and provision. Individuals are needed to oversee this infrastructure, and they, too, require everything they need for their own lives to fulfill this task.
Shelter: To provide a living environment, including the infrastructure for its creation, provisioning, maintenance, and distribution. Individuals are needed to manage this provision, and they, too, require everything they need for their own lives to fulfill this task.
Energy: For heating, cooling, daily activities, and life itself, along with the infrastructure for its generation, provisioning, maintenance, and distribution. Individuals are needed to oversee this infrastructure, and they, too, require everything they need for their own lives to fulfill this task.
Authorization and Participation: To access water, food, shelter, and energy. This requires an infrastructure of agreements, and individuals to manage these agreements. These individuals also require everything they need for their own lives to fulfill this task.
Education: To be capable of undertaking and successfully completing tasks in real life. This necessitates individuals with enough experience and knowledge to offer and conduct such education. These individuals also require everything they need for their own lives to fulfill this task.
Medical Care: To help with injuries, accidents, and illnesses. This requires individuals with sufficient experience and knowledge to offer and provide medical care, as well as the necessary facilities and equipment. These individuals also require everything they need for their own lives to fulfill this task.
Communication Facilities: So that everyone can receive helpful information needed to navigate their world effectively. This requires suitable infrastructure and individuals with enough experience and knowledge to provide such information. These individuals also require everything they need for their own lives to fulfill this task.
Transportation Facilities: So that people and goods can reach the places they need to go. This necessitates suitable infrastructure and individuals with enough experience and knowledge to offer such infrastructure. These individuals also require everything they need for their own lives to fulfill this task.
Decision Structures: To mediate the diverse needs and necessary services in a way that ensures most people have access to what they need for their daily lives. This requires suitable infrastructure and individuals with enough experience and knowledge to offer such infrastructure. These individuals also require everything they need for their own lives to fulfill this task.
Law Enforcement: To ensure disruptions and damage to the infrastructure necessary for daily life are resolved without creating new disruptions. This requires suitable infrastructure and individuals with enough experience and knowledge to offer such services. These individuals also require everything they need for their own lives to fulfill this task.
Sufficient Land: To provide enough space for all these requirements, along with suitable soil (for water, food, shelter, transportation, storage, production, etc.).
A functioning ecosystem.
A capable scientific community to explore and understand the world.
Suitable technology to accomplish everyday tasks and support scientific endeavors.
Knowledge in the minds of people to understand daily events and make responsible decisions.
Goal orientations (preferences, values, etc.) in the minds of people to make informed decisions.
Ample time and peace to allow these processes to occur and produce results.
Strong and lasting relationships with other population groups pursuing the same goals.
Sufficient commonality among all population groups on Earth to address their shared needs where they are affected.
A sustained positive and constructive competition for those goal orientations that make life possible and viable for as many people on this planet (in this solar system, in this galaxy, etc.) as possible.
The freedom present within the experiential world, included within every living being, especially within humans, should be given as much room as possible, as it is this freedom that can overcome false ideas from the past in the face of a constantly changing world, enabling us to potentially thrive in the world of the future.
In a first reading one can get the impression that the task of securing safety critical systems seems to be more and more unsolvable. The complexity of the task seems to be is beyond all paradigms we know today. Is this the end of modern technology? Nancy and John exclude explicitly that a ‘solution’ could be based on ‘more AI’ only; without human persons it will not work. What does this mean?
My personal judgment: We have to go back to ‘start’. We have to consider this challenge from scratch in a new way: What do we really need? What methods are known? What is still missing?
A personal guess: We have to think about the human factor more radically: the human factor is not only one more ‘factor’ besides others in the scenario; the human factor is indeed an ‘object’ but at the same time also the ‘author’ inducing implicitly all the conditions of thinking, the medium of communications, as well as the decisions. To be silent about this causes hiding many important factors which are effective.
Translation: This text is a translation from the German Version into English with the aid of the software deepL.com as well as with chatGPT4, moderated by the author. The style of the two translators is different. The author is not good enough to classify which translator is ‘better’.
Collective human-machine intelligence and text generation. A transdisciplinary analysis.
Based on the conference theme “AI – Text and Validity. How do AI text generators change scientific discourse?” as well as the special topic “Collective human-machine intelligence using the example of text generation”, the possible interaction relationship between text generators and a scientific discourse will be played out in a transdisciplinary analysis. For this purpose, the concept of scientific discourse will be specified on a case-by-case basis using the text types empirical theory as well as sustained empirical theory in such a way that the role of human and machine actors in these discourses can be sufficiently specified. The result shows a very clear limitation of current text generators compared to the requirements of scientific discourse. This leads to further fundamental analyses on the example of the dimension of time with the phenomenon of the qualitatively new as well as on the example of the foundations of decision-making to the problem of the inherent bias of the modern scientific disciplines. A solution to the inherent bias as well as the factual disconnectedness of the many individual disciplines is located in the form of a new service of transdisciplinary integration by re-activating the philosophy of science as a genuine part of philosophy. This leaves the question open whether a supervision of the individual sciences by philosophy could be a viable path? Finally, the borderline case of a world in which humans no longer have a human counterpart is pointed out.
AUDIO: Keyword Sound
This text takes its starting point from the conference topic “AI – Text and Validity. How do AI text generators change scientific discourses?” and adds to this topic the perspective of a Collective Human-Machine Intelligence using the example of text generation. The concepts of text and validity, AI text generators, scientific discourse, and collective human-machine intelligence that are invoked in this constellation represent different fields of meaning that cannot automatically be interpreted as elements of a common conceptual framework.
In order to be able to let the mentioned terms appear as elements in a common conceptual framework, a meta-level is needed from which one can talk about these terms and their possible relations to each other. This approach is usually located in the philosophy of science, which can have as its subject not only single terms or whole propositions, but even whole theories that are compared or possibly even united. The term transdisciplinary  , which is often used today, is understood here in this philosophy of science understanding as an approach in which the integration of different concepts is redeemed by introducing appropriate meta-levels. Such a meta-level ultimately always represents a structure in which all important elements and relations can gather.
 Jürgen Mittelstraß paraphrases the possible meaning of the term transdisciplinarity as a “research and knowledge principle … that becomes effective wherever a solely technical or disciplinary definition of problem situations and problem solutions is not possible…”. Article Methodological Transdisciplinarity, in LIFIS ONLINE, www.leibniz-institut.de, ISSN 1864-6972, p.1 (first published in: Technology Assessment – Theory and Practice No.2, 14.Jg., June 2005, 18-23). In his text Mittelstrass distinguishes transdisciplinarity from the disciplinary and from the interdisciplinary. However, he uses only a general characterization of transdisciplinarity as a research guiding principle and scientific form of organization. He leaves the concrete conceptual formulation of transdisciplinarity open. This is different in the present text: here the transdisciplinary theme is projected down to the concreteness of the related terms and – as is usual in philosophy of science (and meta-logic) – realized by means of the construct of meta-levels.
SETTING UP A STRUCTURE
Here the notion of scientific discourse is assumed as a basic situation in which different actors can be involved. The main types of actors considered here are humans, who represent a part of the biological systems on planet Earth as a kind of Homo sapiens, and text generators, which represent a technical product consisting of a combination of software and hardware.
It is assumed that humans perceive their environment and themselves in a species-typical way, that they can process and store what they perceive internally, that they can recall what they have stored to a limited extent in a species-typical way, and that they can change it in a species-typical way, so that internal structures can emerge that are available for action and communication. All these elements are attributed to human cognition. They are working partially consciously, but largely unconsciously. Cognition also includes the subsystem language, which represents a structure that on the one hand is largely species-typically fixed, but on the other hand can be flexibly mapped to different elements of cognition.
In the terminology of semiotics  the language system represents a symbolic level and those elements of cognition, on which the symbolic structures are mapped, form correlates of meaning, which, however, represent a meaning only insofar as they occur in a mapping relation – also called meaning relation. A cognitive element as such does not constitute meaning in the linguistic sense. In addition to cognition, there are a variety of emotional factors that can influence both cognitive processes and the process of decision-making. The latter in turn can influence thought processes as well as action processes, consciously as well as unconsciously. The exact meaning of these listed structural elements is revealed in a process model  complementary to this structure.
 See, for example, Winfried Nöth: Handbuch der Semiotik. 2nd, completely revised edition. Metzler, Stuttgart/Weimar, 2000
 Such a process model is presented here only in partial aspects.
SYMBOLIC COMMUNICATION SUB-PROCESS
What is important for human actors is that they can interact in the context of symbolic communication with the help of both spoken and written language. Here it is assumed – simplistically — that spoken language can be mapped sufficiently accurately into written language, which in the standard case is called text. It should be noted that texts only represent meaning if the text producers involved, as well as the text recipients, have a meaning function that is sufficiently similar. For texts by human text producers it is generally true that, with respect to concrete situations, statements as part of texts can be qualified under agreed conditions as now matching the situation (true) or as now not now matching the situation (false). However, a now-true can become a now-not-true again in the next moment and vice versa.
This dynamic fact refers to the fact that a punctual occurrence or non-occurrence of a statement is to be distinguished from a structural occurrence/ non-occurrence of a statement, which speaks about occurrence/ non-occurrence in context. This refers to relations which are only indirectly apparent in the context of a multitude of individual events, if one considers chains of events over many points in time. Finally, one must also consider that the correlates of meaning are primarily located within the human biological system. Meaning correlates are not automatically true as such, but only if there is an active correspondence between a remembered/thought/imagined meaning correlate and an active perceptual element, where an intersubjective fact must correspond to the perceptual element. Just because someone talks about a rabbit and the recipient understands what a rabbit is, this does not mean that there is also a real rabbit which the recipient can perceive.
When distinguishing between the two different types of actors – here biological systems of the type Homo sapiens and there technical systems of the type text-generators – a first fundamental asymmetry immediately strikes the eye: so-called text-generators are entities invented and built by humans; furthermore, it is humans who use them, and the essential material used by text-generators are furthermore texts, which are considered human cultural property, created and used by humans for a variety of discourse types, here restricted to scientific discourse.
In the case of text generators, let us first note that we are dealing with machines that have input and output, a minimal learning capability, and whose input and output can process text-like objects. Insofar as text generators can process text-like objects as input and process them again as output, an exchange of texts between humans and text generators can take place in principle.
At the current state of development (September 2023), text generators do not yet have an independent real-world perception within the scope of their input, and the entire text generator system does not yet have such processes as those that enable species-typical cognitions in humans. Furthermore, a text generator does not yet have a meaning function as it is given with humans.
From this fact it follows automatically that text generators cannot decide about selective or structural correctness/not correctness in the case of statements of a text. In general, they do not have their own assignment of meaning as with humans. Texts generated by text generators only have a meaning if a human as a recipient automatically assigns a meaning to a text due to his species-typical meaning relation, because this is the learned behavior of a human. In fact, the text generator itself has never assigned any meaning to the generated text. Salopp one could also formulate that a technical text generator works like a parasite: it collects texts that humans have generated, rearranges them combinatorially according to formal criteria for the output, and for the receiving human a meaning event is automatically triggered by the text in the human, which does not exist anywhere in the text generator. Whether this very restricted form of text generation is now in any sense detrimental or advantageous for the type of scientific discourse (with texts), that is to be examined in the further course.
There is no clear definition for the term scientific discourse. This is not surprising, since an unambiguous definition presupposes that there is a fully specified conceptual framework within which terms such as discourse and scientific can be clearly delimited. However, in the case of a scientific enterprise with a global reach, broken down into countless individual disciplines, this does not seem to be the case at present (Sept 2023). For the further procedure, we will therefore fall back on core ideas of the discussion in philosophy of science since the 20th century and we will introduce working hypotheses on the concept of empirical theory as well as sustainable empirical theory, so that a working hypothesis on the concept of scientific discourse will be possible, which has a minimal sharpness.
 A good place to start may be: F. Suppe, Editor. The Structure of Scientific Theories. University of Illinois Press, Urbana, 2 edition, 1979.
The following assumptions are made for the notion of an empirical theory:
an empirical theory is basically a text, written in a language that all participants understand.
one part of the theory contains a description of an initial situation, the statements of which can be qualified by the theory users as now matching (true) or now not matching (false).
another part of the theory contains a text that lists all changes that, to the knowledge of the participants, occur in the context of the initial situation and can change parts of the initial situation.
changes in the initial situation are expressed by replacing certain statements of the initial situation with other statements. The resulting new text replaces the previous text.
through the possibility of generating new initial situations, there is the possibility of making predictions (forming expectations) by applying rules of change to an applicable initial situation several times (at least once) in succession. The texts that are generated in each case induce in the minds of the participants, on the basis of the available meaning functions, the idea of a situation that is demanded to become qualified as now matching with intersubjective reality, should it occur. In the case of occurrence, the situation must correspond to the conception in the mind via perception. Whether such a correspondence can be established, after how long time and to what extent, this is basically open in the presence of the prediction making (the formation of an expectation) (autonomy of the object!).
SCIENTIFIC DISCOURSE NEW
Even before the introduction of the notion of a sustainable empirical theory, the notion of an empirical theory just introduced allows for initial statements about what a scientific discourse could or should be. It is assumed here that the participants in a scientific discourse exchange their opinions about the world in the form of texts, all of which meet the requirements of an empirical theory.
Insofar as the participants in the discourse are human beings, we know that they have basic species-typical abilities to generate subjective meanings by means of language, which can be decided as now true or now not true by reference to a real situation.
WITH TEXT GENERATORS
Insofar as the discourse participants would be text generators lacking these elementary skills (meaning, reality reference), they drop out as independent actors for a scientific discourse. To illustrate this, here is a self-assessment of chatGPT4, a current text generator:
Yes, your summary of our previous dialogue is accurate based on the points you’ve listed:
I cannot read a book in the same way a human does, meaning I don’t interpret or comprehend in the same manner.
My knowledge is up to September 2021, which means that I might not be aware of events, advancements, or publications after that point.
My “learning” is different from human learning. I don’t learn from new experiences or information after my last update. Instead, I generate responses based on patterns in the data I was trained on.
I don’t cite individual sources in my responses. I provide information based on the patterns recognized during my training.
Responses I give can vary slightly even if the same question is posed multiple times.
I can’t directly attribute my statements to specific, individual sources.
The specifics about my training data and the processes that determine my responses are not transparently provided in each interaction.
You’ve captured the essence of our prior conversation well. If you have further questions or need clarifications on any of these points, please let me know. 
 This text is from a chat experiment conducted by the author with chatGPT4 on August 31, 2023. The citation can be found in: Gerd Doeben-Henisch, CONTINUE EXPERIMENT WITH CHATGPT4: Science, Empirical Science, Philosophy of Science, uffmm, Integrating Engineering and the Human Factor, eJournal uffmm.org ISSN 2567-6458, 8/31/2023 in [https://www.uffmm.org/2023/08/31/continue-experiment-with-chatgpt4-science-empirical-science-philosophy-of-science/ ] (accessed 9/27/2023).
The question then arises whether (current) text generators, despite their severely limited capabilities, could nevertheless contribute to scientific discourse, and what this contribution means for human participants. Since text generators fail for the hard scientific criteria (decidable reality reference, reproducible predictive behavior, separation of sources), one can only assume a possible contribution within human behavior: since humans can understand and empirically verify texts, they would in principle be able to rudimentarily classify a text from a text generator within their considerations.
For hard theory work, these texts would not be usable, but due to their literary-associative character across a very large body of texts, the texts of text generators could – in the positive case – at least introduce thoughts into the discourse through texts as stimulators via the detour of human understanding, which would stimulate the human user to examine these additional aspects to see if they might be important for the actual theory building after all. In this way, the text generators would not participate independently in the scientific discourse, but they would indirectly support the knowledge process of the human actors as aids to them.
 A detailed illustration of this associative role of a text generator can also be found in (Doeben-Henisch, 2023) on the example of the term philosophy of science and on the question of the role of philosophy of science.
The application of an empirical theory can – in the positive case — enable an expanded picture of everyday experience, in that, related to an initial situation, possible continuations (possible futures) are brought before one’s eyes. For people who have to shape their own individual processes in their respective everyday life, however, it is usually not enough to know only what one can do.Rather, everyday life requires deciding in each case which continuation to choose, given the many possible continuations. In order to be able to assert themselves in everyday life with as little effort as possible and with – at least imagined – as little risk as possible, people have adopted well-rehearsed behavior patterns for as many everyday situations as possible, which they follow spontaneously without questioning them anew each time. These well-rehearsed behavior patterns include decisions that have been made. Nevertheless, there are always situations in which the ingrained automatisms have to be interrupted in order to consciously clarify the question for which of several possibilities one wants to decide.
The example of an individual decision-maker can also be directly applied to the behavior of larger groups. Normally, even more individual factors play a role here, all of which have to be integrated in order to reach a decision. However, the characteristic feature of a decision situation remains the same: whatever knowledge one may have at the time of decision, when alternatives are available, one has to decide for one of many alternatives without any further, additional knowledge at this point. Empirical science cannot help here : it is an indisputable basic ability of humans to be able to decide.
So far, however, it remains rather hidden in the darkness of not knowing oneself, which ultimately leads to deciding for one and not for the other. Whether and to what extent the various cultural patterns of decision-making aids in the form of religious, moral, ethical or similar formats actually form or have formed a helpful role for projecting a successful future appears to be more unclear than ever.
 No matter how much detail she can contribute about the nature of decision-making processes.
 This topic is taken up again in the following in a different context and embedded there in a different solution context.
SUSTAINABLE EMPIRICAL THEORY
Through the newly flared up discussion about sustainability in the context of the United Nations, the question of prioritizing action relevant to survival has received a specific global impulse. The multitude of aspects that arise in this discourse context  are difficult, if not impossible, to classify into an overarching, consistent conceptual framework.
 For an example see the 17 development goals: [https://unric.org/de/17ziele/] (Accessed: September 27, 2023)
A rough classification of development goals into resource-oriented and actor-oriented can help to make an underlying asymmetry visible: a resource problem only exists if there are biological systems on this planet that require a certain configuration of resources (an ecosystem) for their physical existence. Since the physical resources that can be found on planet Earth are quantitatively limited, it is possible, in principle, to determine through thought and science under what conditions the available physical resources — given a prevailing behavior — are insufficient. Added to this is the factor that biological systems, by their very existence, also actively alter the resources that can be found.
So, if there should be a resource problem, it is exclusively because the behavior of the biological systems has led to such a biologically caused shortage. Resources as such are neither too much, nor too little, nor good, nor bad. If one accepts that the behavior of biological systems in the case of the species Homo sapiens can be controlled by internal states, then the resource problem is primarily a cognitive and emotional problem: Do we know enough? Do we want the right thing? And these questions point to motivations beyond what is currently knowable. Is there a dark spot in the human self-image here?
On the one hand, this questioning refers to the driving forces for a current decision beyond the possibilities of the empirical sciences (trans-empirical, meta-physical, …), but on the other hand, this questioning also refers to the center/ core of human competence. This motivates to extend the notion of empirical theory to the notion of a sustainable empirical theory. This does not automatically solve the question of the inner mechanism of a value decision, but it systematically classifies the problem. The problem thus has an official place. The following formulation is suggested as a characterization for the concept of a sustainable empirical theory:
a sustainable empirical theory contains an empirical theory as its core.
besides the parts of initial situation, rules of change and application of rules of change, a sustainable theory also contains a text with a list of such situations, which are considered desirable for a possible future (goals, visions, …).
under the condition of goals, it is possible to minimally compare each current situation with the available goals and thereby indicate the degree of goal achievement.
Stating desired goals says nothing about how realistic or promising it is to pursue those goals. It only expresses that the authors of this theory know these goals and consider them optimal at the time of theory creation.  The irrationality of chosen goals is in this way officially included in the domain of thought of the theory creators and in this way facilitates the extension of the rational to the irrational without already having a real solution. Nobody can exclude that the phenomenon of bringing forth something new, respectively of preferring a certain point of view in comparison to others, can be understood further and better in the future.
 Something can only be classified as optimal if it can be placed within an overarching framework, which allows for positioning on a scale. This refers to a minimal cognitive model as an expression of rationality. However, the decision itself takes place outside of such a rational model; in this sense, the decision as an independent process is pre-rational.
EXTENDED SCIENTIFIC DISCOURSE
If one accepts the concept of a sustainable empirical theory, then one can extend the concept of a scientific discourse in such a way that not only texts that represent empirical theories can be introduced, but also those texts that represent sustainable empirical theories with their own goals. Here too, one can ask whether the current text generators (September 2023) can make a constructive contribution. Insofar as a sustainable empirical theory contains an empirical theory as a hard core, the preceding observations on the limitations of text generators apply. In the creative part of the development of an empirical theory, they can contribute text fragments through their associative-combinatorial character based on a very large number of documents, which may inspire the active human theory authors to expand their view. But what about that part that manifests itself in the selection of possible goals? At this point, one must realize that it is not about any formulations, but about those that represent possible solution formulations within a systematic framework; this implies knowledge of relevant and verifiable meaning structures that could be taken into account in the context of symbolic patterns. Text generators fundamentally do not have these abilities. But it is – again – not to be excluded that their associative-combinatorial character based on a very large number of documents can still provide one or the other suggestion.
In retrospect of humanity’s history of knowledge, research, and technology, it is suggested that the great advances were each triggered by something really new, that is, by something that had never existed before in this form. The praise for Big Data, as often heard today, represents – colloquially speaking — exactly the opposite: The burial of the new by cementing the old.
 A prominent example of the naive fixation on the old as a standard for what is right can be seen, for example, in the book by Seth Stephens-Davidowitz, Don’t Trust Your Gut. Using Data Instead of Instinct To Make Better Choices, London – Oxford New York et al., 2022.
EXISTENTIALLY NEW THROUGH TIME
The concept of an empirical theory inherently contains the element of change, and even in the extended concept of a sustainable empirical theory, in addition to the fundamental concept of change, there is the aspect of a possible goal. A possible goal itself is not a change, but presupposes the reality of changes! The concept of change does not result from any objects but is the result of a brain performance, through which a current present is transformed into a partially memorable state (memory contents) by forming time slices in the context of perception processes – largely unconsciously. These produced memory contents have different abstract structures, are networked differently with each other, and are assessed in different ways. In addition, the brain automatically compares current perceptions with such stored contents and immediately reports when a current perception has changed compared to the last perception contents. In this way, the phenomenon of change is a fundamental cognitive achievement of the brain, which thus makes the character of a feeling of time available in the form of a fundamental process structure. The weight of this property in the context of evolution is hardly to be overestimated, as time as such is in no way perceptible.
 The modern invention of machines that can generate periodic signals (oscillators, clocks) has been successfully integrated into people’s everyday lives. However, the artificially (technically) producible time has nothing to do with the fundamental change found in reality. Technical time is a tool that we humans have invented to somehow structure the otherwise amorphous mass of a phenomenon stream. Since structure itself shows in the amorphous mass, which manifest obviously for all, repeating change cycles (e.g., sunrise and sunset, moonrise and moonset, seasons, …), a correlation of technical time models and natural time phenomena was offered. From the correlations resulting here, however, one should not conclude that the amorphous mass of the world phenomenon stream actually behaves according to our technical time model. Einstein’s theory of relativity at least makes us aware that there can be various — or only one? — asymmetries between technical time and world phenomenon stream.
Assuming this fundamental sense of time in humans, one can in principle recognize whether a current phenomenon, compared to all preceding phenomena, is somehow similar or markedly different, and in this sense indicates something qualitatively new.
 Ultimately, an individual human only has its individual memory contents available for comparison, while a collective of people can in principle consult the set of all records. However, as is known, only a minimal fraction of the experiential reality is symbolically transformed.
By presupposing the concept of directed time for the designation of qualitatively new things, such a new event is assigned an information value in the Shannonian sense, as well as the phenomenon itself in terms of linguistic meaning, and possibly also in the cognitive area: relative to a spanned knowledge space, the occurrence of a qualitatively new event can significantly strengthen a theoretical assumption. In the latter case, the cognitive relevance may possibly mutate to a sustainable relevance if the assumption marks a real action option that could be important for further progress. In the latter case, this would provoke the necessity of a decision: should we adopt this action option or not? Humans can accomplish the finding of qualitatively new things. They are designed for it by evolution. But what about text generators?
Text generators so far do not have a sense of time comparable to that of humans. Their starting point would be texts that are different, in such a way that there is at least one text that is the most recent on the timeline and describes real events in the real world of phenomena. Since a text generator (as of September 2023) does not yet have the ability to classify texts regarding their applicability/non-applicability in the real world, its use would normally end here. Assuming that there are people who manually perform this classification for a text generator  (which would greatly limit the number of possible texts), then a text generator could search the surface of these texts for similar patterns and, relative to them, for those that cannot be compared. Assuming that the text generator would find a set of non-comparable patterns in acceptable time despite a massive combinatorial explosion, the problem of semantic qualification would arise again: which of these patterns can be classified as an indication of something qualitatively new? Again, humans would have to become active.
 Such support of machines by humans in the field of so-called intelligent algorithms has often been applied (and is still being applied today, see: [https://www.mturk.com/] (Accessed: September 27, 2023)), and is known to be very prone to errors.
As before, the verdict is mixed: left to itself, a text generator will not be able to solve this task, but in cooperation with humans, it may possibly provide important auxiliary services, which could ultimately be of existential importance to humans in search of something qualitatively new despite all limitations.
THE IMMANENT PREJUDICE OF THE SCIENCES
A prejudice is known to be the assessment of a situation as an instance of a certain pattern, which the judge assumes applies, even though there are numerous indications that the assumed applicability is empirically false. Due to the permanent requirement of everyday life that we have to make decisions, humans, through their evolutionary development, have the fundamental ability to make many of their everyday decisions largely automatically. This offers many advantages, but can also lead to conflicts.
Daniel Kahneman introduced in this context in his book  the two terms System 1 and System 2 for a human actor. These terms describe in his concept of a human actor two behavioral complexes that can be distinguished based on some properties. System 1 is set by the overall system of human actor and is characterized by the fact that the actor can respond largely automatically to requirements by everyday life. The human actor has automatic answers to certain stimuli from his environment, without having to think much about it. In case of conflicts within System 1 or from the perspective of System 2, which exercises some control over the appropriateness of System 1 reactions in a certain situation in conscious mode, System 2 becomes active. This does not have automatic answers ready, but has to laboriously work out an answer to a given situation step by step. However, there is also the phenomenon that complex processes, which must be carried out frequently, can be automated to a certain extent (bicycling, swimming, playing a musical instrument, learning language, doing mental arithmetic, …). All these processes are based on preceding decisions that encompass different forms of preferences. As long as these automated processes are appropriate in the light of a certain rational model, everything seems to be OK. But if the corresponding model is distorted in any sense, then it would be said that these models carry a prejudice.
 Daniel Kahnemann, Thinking Fast and Slow, Pinguin Boooks Random House, UK, 2012 (zuerst 2011)
 See the following Chapter 1 in Part 1 of (Kahnemann, 2012, pages 19-30).
In addition to the countless examples that Kahneman himself cites in his book to show the susceptibility of System 1 to such prejudices, it should be pointed out here that the model of Kahneman himself (and many similar models) can carry a prejudice that is of a considerably more fundamental nature. The division of the behavioral space of a human actor into a System 1 and 2, as Kahneman does, obviously has great potential to classify many everyday events. But what about all the everyday phenomena that fit neither the scheme of System 1 nor the scheme of System 2?
In the case of making a decision, System 1 comments that people – if available – automatically call up and execute an available answer. Only in the case of conflict under the control of System 2 can there be lengthy operations that lead to other, new answers.
In the case of decisions, however, it is not just about reacting at all, but there is also the problem of choosing between known possibilities or even finding something new because the known old is unsatisfactory.
Established scientific disciplines have their specific perspectives and methods that define areas of everyday life as a subject area. Phenomena that do not fit into this predefined space do not occur for the relevant discipline – methodically conditioned. In the area of decision-making and thus the typical human structures, there are not a few areas that have so far not found official entry into a scientific discipline. At a certain point in time, there are ultimately many, large phenomenon areas that really exist, but methodically are not present in the view of individual sciences. For a scientific investigation of the real world, this means that the sciences, due to their immanent exclusions, are burdened with a massive reservation against the empirical world. For the task of selecting suitable sustainable goals within the framework of sustainable science, this structurally conditioned fact can be fatal. Loosely formulated: under the banner of actual science, a central principle of science – the openness to all phenomena – is simply excluded, so as not to have to change the existing structure.
For this question of a meta-reflection on science itself, text generators are again only reduced to possible abstract text delivery services under the direction of humans.
SUPERVISION BY PHILOSOPHY
The just-described fatal dilemma of all modern sciences is to be taken seriously, as without an efficient science, sustainable reflection on the present and future cannot be realized in the long term. If one agrees that the fatal bias of science is caused by the fact that each discipline works intensively within its discipline boundaries, but does not systematically organize communication and reflection beyond its own boundaries with a view to other disciplines as meta-reflection, the question must be answered whether and how this deficit can be overcome.
There is only one known answer to this question: one must search for that conceptual framework within which these guiding concepts can meaningfully interact both in their own right and in their interaction with other guiding concepts, starting from those guiding concepts that are constitutive for the individual disciplines.
This is genuinely the task of philosophy, concretized by the example of the philosophy of science. However, this would mean that each individual science would have to use a sufficiently large part of its capacities to make the idea of the one science in maximum diversity available in a real process.
For the hard conceptual work hinted at here, text generators will hardly be able to play a central role.
Since so far there is no concept of intelligence in any individual science that goes beyond a single discipline, it makes little sense at first glance to apply the term intelligence to collectives. However, looking at the cultural achievements of humanity as a whole, and here not least with a view to the used language, it is undeniable that a description of the performance of an individual person, its individual performance, is incomplete without reference to the whole.
So, if one tries to assign an overarching meaning to the letter combination intelligence, one will not be able to avoid deciphering this phenomenon of the human collective in the form of complex everyday processes in a no less complex dynamic world, at least to the extent that one can identify a somewhat corresponding empirical something for the letter combination intelligence, with which one could constitute a comprehensible meaning.
Of course, this term should be scalable for all biological systems, and one would have to have a comprehensible procedure that allows the various technical systems to be related to this collective intelligence term in such a way that direct performance comparisons between biological and technical systems would be possible.
 The often quoted and popular Turing Test (See: Alan M. Turing: Computing Machinery and Intelligence. In: Mind. Volume LIX, No. 236, 1950, 433–460, [doi:10.1093/mind/LIX.236.433] (Accessed: Sept 29, 2023) in no way meets the methodological requirements that one would have to adhere to if one actually wanted to come to a qualified performance comparison between humans and machines. Nevertheless, the basic idea of Turing in his meta-logical text from 1936, published in 1937 (see: A. M. Turing: On Computable Numbers, with an Application to the Entscheidungsproblem. In: Proceedings of the London Mathematical Society. s2-42. Volume, No. 1, 1937, 230–265 [doi:10.1112/plms/s2-42.1.230] (Accessed: Sept 29, 2023) seems to be a promising starting point, since he, in trying to present an alternative formulation to Kurt Gödel’s (1931) proof on the undecidability of arithmetic, leads a meta-logical proof, and in this context Turing introduces the concept of a machine that was later called Universal Turing Machine.
Already in this proof approach, it can be seen how Turing transforms the phenomenon of a human bookkeeper at a meta-level into a theoretical concept, by means of which he can then meta-logically examine the behavior of this bookkeeper in a specific behavioral space. His meta-logical proof not only confirmed Gödel’s meta-logical proof, but also indirectly indicates how ultimately any phenomenal complexes can be formalized on a meta-level in such a way that one can then argue formally demanding with it.
The idea of philosophical supervision of the individual sciences with the goal of a concrete integration of all disciplines into an overall conceptual structure seems to be fundamentally possible from a philosophy of science perspective based on the previous considerations. From today’s point of view, specific phenomena claimed by individual disciplines should no longer be a fundamental obstacle for a modern theory concept. This would clarify the basics of the concept of Collective Intelligence and it would surely be possible to more clearly identify interactions between human collective intelligence and interactive machines. Subsequently, the probability would increase that the supporting machines could be further optimized, so that they could also help in more demanding tasks.
Attempting to characterize the interactive role of text generators in a human-driven scientific discourse, assuming a certain scientific model, appears to be somewhat clear from a transdisciplinary (and thus structural) perspective. However, such scientific discourse represents only a sub-space of the general human discourse space. In the latter, the reception of texts from the perspective of humans inevitably also has a subjective view : People are used to suspecting a human author behind a text. With the appearance of technical aids, texts have increasingly become products, which increasingly gaining formulations that are not written down by a human author alone, but by the technical aids themselves, mediated by a human author. With the appearance of text generators, the proportion of technically generated formulations increases extremely, up to the case that ultimately the entire text is the direct output of a technical aid. It becomes difficult to impossible to recognize to what extent a controlling human share can still be spoken of here. The human author thus disappears behind a text; the sign reality which does not prevent an existential projection of the inner world of the human reader into a potential human author, but threatens to lose itself or actually loses itself in the real absence of a human author in the face of a chimeric human counterpart. What happens in a world where people no longer have human counterparts?
 There is an excellent analysis on this topic by Hannes Bajohr titled “Artifizielle und postartifizielle Texte. Über Literatur und Künstliche Intelligenz” (Artificial and Post-Artificial Texts: On Literature and Artificial Intelligence). It was the Walter-Höllerer-Lecture 2022, delivered on December 8, 2022, at the Technical University of Berlin. The lecture can be accessed here [ https://hannesbajohr.de/wp-content/uploads/2022/12/Hoellerer-Vorlesung-2022.pdf ] (Accessed: September 29, 2023). The reference to this lecture was provided to me by Jennifer Becker.
Integrating Engineering and the Human Factor (email@example.com) eJournal uffmm.org ISSN 2567-6458