TRANSLATION: The following text is a translation from a German version into English. For the translation I am using the software @chatGPT4o with manual modifications.
This is another interim reflection aimed at further clarifying the structural terminology of ‘intelligence’ and ‘learning.’ This and the preceding interim reflections are intended as a ‘supplement’ to the main thread of the text project What is Life?
The initial analysis of the concept of intelligence made it clear that, within the framework of an intelligence test, only those responses can be given by the test subject that are currently available to them. Apart from innate reflexes and physically conditioned properties, these responses stem from internal structures that have emerged through learning processes. The structures acquired through learning will here be referred to as knowledge or experience.
The distinction between the terms intelligence, learning, knowledge, and experience will be further elaborated in the following text. This will reveal a general structure of Homo sapiens (HS) as part of the overarching Life (L), equipped with the capacity to potentially secure its survival on this planet (P). However, as a part of life, Homo sapiens can also use its abilities to contribute to the survival of all life, which ultimately ensures its own existence. Aside from an unpredictable future, Homo sapiens remains entirely dependent on the totality of life on this planet.
IMAGE 1: Basic constellation of Planet (P), Life (L), and Homo sapiens (HS) as a subpopulation of Life. All components (P, L, HS) interact both within themselves and with one another.
INTELLIGENCE – LEARNING – KNOWLEDGE – MATERIAL CONDITIONS
Starting Point
The starting point of the further considerations is illustrated in the following diagram:
IMAGE 2: A dynamic perspective of the Planet-Life-Homo sapiens constellation, assuming a fictional observer.
From the perspective of a fictional observer (see IMAGE 2), the structure of the Planet – Life – Homo sapiens (PLHS) constellation, with its extensive interactions, can be seen as embedded within a temporal structure (T), which is understood here as a process with time slices (see Text No. 4).
In the diagram, there is a fixed reference point, the present (NOW). Before the present, there is a presumed history (HISTORY), and after the present, in the potentially emerging new presents (POSSIBLE FUTURES), further instances of the present may arise. However, at the current moment, in the present, these future instances are not yet known.
IMAGE 3: Assuming the presence of a brain that, through a body, can perceive properties of the world, later remember them, and subsequently organize them in an appropriate way, a certain dynamic structure of Planet, Life, and Homo sapiens becomes recognizable.
The fictional observer, however, exists only in the mind of a human being—provided that they can think, that they can remember, and that their memory has been shaped by actual perceptions. Since perception, memory, and thinking are all prone to error, the perspective of the fictional observer is always either more correct or more incorrect.
Knowledge and Material Embedding
The thinking human is equipped with a body (Material Embedding 1) that is always situated in a real situation—or a real state (Material Embedding 2). This situation is always part of something larger. Here, we assume that this larger context is normally Planet Earth (which itself is embedded in almost arbitrarily vast contexts within the universe), along with the population of Homo sapiens and the totality of life (Material Embedding 3).
Over time, humans have discovered that, in the present situation, numerous traces (TRACES) can be found—traces that point to events related to the planet, events of life on the planet, and events of Homo sapiens as part of life on the planet.
These observable traces (observable TRACES) originate from different time periods in history and thus reveal patterns of change (CH). If changes exhibit regularities, they can serve as a basis for generating possible future states (POSSIBLE FUTURES) through a form of prediction logic.
Knowledge
For the human perspective on the planet, life, and itself, the decisive factor is that humans possess the following abilities: (i) Perceive everything around them in a way that allows them to (ii) Store the contents of perception so that they can (iii) Recall these contents later.
(iv) As we now understand, perception does not occur as a one-to-one representation but involves various simplifications/abstractions that allow a connection to current perception during recall.
(v) Due to the nature of stored content, (vi) Thinking can structure these contents in different ways and (vii) detect changes by comparing before and after.
If these changes exhibit sufficient regularity, (viii) thinking can generate limited predictions.
(ix) The process of generating predictions falls broadly under the concept of logic or logical inference (although modern formal logic captures only partial aspects of this real phenomenon).
One could formulate this idea as follows: Knowledge is a specific form of “resonance” of the material conditions under which knowledge emerges.
While material conditions can influence the emergence and structure of knowledge, they cannot:
Generate knowledge itself, nor
Generate solely from within themselvesthat which knowledge reflects.
Thus, the resonance in the format of knowledge points to something beyond knowledge itself.
In traditional philosophical language, this would be referred to as transcendence: with the availability of knowledge—arising through a true resonance—knowledge transcends (exceeds) the human being and their material limitations.
Material Embedding of Knowledge
Today, we understand that the material embedding of knowledge reveals numerous factors that can be either constructive or destructive in the process of knowledge formation, as well as in its failure to emerge.
The effects of these factors become particularly evident when people suffer from illnesses that weaken and torment them, possibly confining them to a specific place. The lack of water, food, or the absence of proper shelter to protect against cold, heat, and rain also plays a critical role. There is the threat posed by other humans or other life forms (animals, insects, plants, etc.). Emotions such as fear, hatred, intense agitation, or strong emotional dependencies can also interfere. Sensory impairments—in vision, hearing, smell, etc.—can further complicate knowledge formation. Poor communication due to language barriers, social exclusion, persecution, or being on the run because of natural disasters can also hinder this process—and much more.
Even one’s own body and existing knowledge can turn into obstacles: A person who holds specific knowledge at a given moment may be emotionally resistant to changing that knowledge. In such cases, their current understanding of the world and themselves freezes—it becomes rigid, and the dynamic nature of life dissipates. The life in their mind turns to stone.
Knowledge and Society; AI
If we recognize how complex and fragile the formation of knowledge is within the mind of a single person, we might begin to grasp the enormous challenge of enabling an entire population to jointly generate the knowledge necessary for securing a good survival in a possible future.
Since at least 2022, technical tools marketed under the misleading label of Artificial Intelligence (AI) have become available. These tools may indeed serve as a tremendous aid—provided that humanity cultivates its own knowledge accordingly (with AI as part of it).
However, as long as humans do not manage to properly integrate the material embedding of their knowledge, so too will so-called AI remain stagnant. The material embedding of humans is not merely a potential obstacle; it is simultaneously the enabler of world-relevant and life-relevant knowledge.
AI does not and cannot possess these two dimensions—not only today but fundamentally. Biological systems embody a potential that can truly transcend inanimate matter and transform it into something new.
Machines—designed, built, and programmed by humans—are, compared to biological systems, merely very simple tools. However, through symbiosis with biological systems, they may generate effects far beyond what we can currently imagine.
Attention: This text has been translated from a German source by using the software deepL for nearly 97 – 99% of the text! The diagrams of the German version have been left out.
CONTEXT
This text represents the outline of a talk given at the conference “AI – Text and Validity. How do AI text generators change scientific discourse?” (August 25/26, 2023, TU Darmstadt). [1] A publication of all lectures is planned by the publisher Walter de Gruyter by the end of 2023/beginning of 2024. This publication will be announced here then.
Start of the Lecture
Dear Auditorium,
This conference entitled “AI – Text and Validity. How do AI text generators change scientific discourses?” is centrally devoted to scientific discourses and the possible influence of AI text generators on these. However, the hot core ultimately remains the phenomenon of text itself, its validity.
In this conference many different views are presented that are possible on this topic.
TRANSDISCIPLINARY
My contribution to the topic tries to define the role of the so-called AI text generators by embedding the properties of ‘AI text generators’ in a ‘structural conceptual framework’ within a ‘transdisciplinary view’. This helps the specifics of scientific discourses to be highlighted. This can then result further in better ‘criteria for an extended assessment’ of AI text generators in their role for scientific discourses.
An additional aspect is the question of the structure of ‘collective intelligence’ using humans as an example, and how this can possibly unite with an ‘artificial intelligence’ in the context of scientific discourses.
‘Transdisciplinary’ in this context means to span a ‘meta-level’ from which it should be possible to describe today’s ‘diversity of text productions’ in a way that is expressive enough to distinguish ‘AI-based’ text production from ‘human’ text production.
HUMAN TEXT GENERATION
The formulation ‘scientific discourse’ is a special case of the more general concept ‘human text generation’.
This change of perspective is meta-theoretically necessary, since at first sight it is not the ‘text as such’ that decides about ‘validity and non-validity’, but the ‘actors’ who ‘produce and understand texts’. And with the occurrence of ‘different kinds of actors’ – here ‘humans’, there ‘machines’ – one cannot avoid addressing exactly those differences – if there are any – that play a weighty role in the ‘validity of texts’.
TEXT CAPABLE MACHINES
With the distinction in two different kinds of actors – here ‘humans’, there ‘machines’ – a first ‘fundamental asymmetry’ immediately strikes the eye: so-called ‘AI text generators’ are entities that have been ‘invented’ and ‘built’ by humans, it are furthermore humans who ‘use’ them, and the essential material used by so-called AI generators are again ‘texts’ that are considered a ‘human cultural property’.
In the case of so-called ‘AI-text-generators’, we shall first state only this much, that we are dealing with ‘machines’, which have ‘input’ and ‘output’, plus a minimal ‘learning ability’, and whose input and output can process ‘text-like objects’.
BIOLOGICAL — NON-BIOLOGICAL
On the meta-level, then, we are assumed to have, on the one hand, such actors which are minimally ‘text-capable machines’ – completely human products – and, on the other hand, actors we call ‘humans’. Humans, as a ‘homo-sapiens population’, belong to the set of ‘biological systems’, while ‘text-capable machines’ belong to the set of ‘non-biological systems’.
BLANK INTELLIGENCE TERM
The transformation of the term ‘AI text generator’ into the term ‘text capable machine’ undertaken here is intended to additionally illustrate that the widespread use of the term ‘AI’ for ‘artificial intelligence’ is rather misleading. So far, there exists today no general concept of ‘intelligence’ in any scientific discipline that can be applied and accepted beyond individual disciplines. There is no real justification for the almost inflationary use of the term AI today other than that the term has been so drained of meaning that it can be used anytime, anywhere, without saying anything wrong. Something that has no meaning can be neither true’ nor ‘false’.
PREREQUISITES FOR TEXT GENERATION
If now the homo-sapiens population is identified as the original actor for ‘text generation’ and ‘text comprehension’, it shall now first be examined which are ‘those special characteristics’ that enable a homo-sapiens population to generate and comprehend texts and to ‘use them successfully in the everyday life process’.
VALIDITY
A connecting point for the investigation of the special characteristics of a homo-sapiens text generation and a text understanding is the term ‘validity’, which occurs in the conference topic.
In the primary arena of biological life, in everyday processes, in everyday life, the ‘validity’ of a text has to do with ‘being correct’, being ‘appicable’. If a text is not planned from the beginning with a ‘fictional character’, but with a ‘reference to everyday events’, which everyone can ‘check’ in the context of his ‘perception of the world’, then ‘validity in everyday life’ has to do with the fact that the ‘correctness of a text’ can be checked. If the ‘statement of a text’ is ‘applicable’ in everyday life, if it is ‘correct’, then one also says that this statement is ‘valid’, one grants it ‘validity’, one also calls it ‘true’. Against this background, one might be inclined to continue and say: ‘If’ the statement of a text ‘does not apply’, then it has ‘no validity’; simplified to the formulation that the statement is ‘not true’ or simply ‘false’.
In ‘real everyday life’, however, the world is rarely ‘black’ and ‘white’: it is not uncommon that we are confronted with texts to which we are inclined to ascribe ‘a possible validity’ because of their ‘learned meaning’, although it may not be at all clear whether there is – or will be – a situation in everyday life in which the statement of the text actually applies. In such a case, the validity would then be ‘indeterminate’; the statement would be ‘neither true nor false’.
ASYMMETRY: APPLICABLE- NOT APPLICABLE
One can recognize a certain asymmetry here: The ‘applicability’ of a statement, its actual validity, is comparatively clear. The ‘not being applicable’, i.e. a ‘merely possible’ validity, on the other hand, is difficult to decide.
With this phenomenon of the ‘current non-decidability’ of a statement we touch both the problem of the ‘meaning’ of a statement — how far is at all clear what is meant? — as well as the problem of the ‘unfinishedness of our everyday life’, better known as ‘future’: whether a ‘current present’ continues as such, whether exactly like this, or whether completely different, depends on how we understand and estimate ‘future’ in general; what some take for granted as a possible future, can be simply ‘nonsense’ for others.
MEANING
This tension between ‘currently decidable’ and ‘currently not yet decidable’ additionally clarifies an ‘autonomous’ aspect of the phenomenon of meaning: if a certain knowledge has been formed in the brain and has been made usable as ‘meaning’ for a ‘language system’, then this ‘associated’ meaning gains its own ‘reality’ for the scope of knowledge: it is not the ‘reality beyond the brain’, but the ‘reality of one’s own thinking’, whereby this reality of thinking ‘seen from outside’ has something like ‘being virtual’.
If one wants to talk about this ‘special reality of meaning’ in the context of the ‘whole system’, then one has to resort to far-reaching assumptions in order to be able to install a ‘conceptual framework’ on the meta-level which is able to sufficiently describe the structure and function of meaning. For this, the following components are minimally assumed (‘knowledge’, ‘language’ as well as ‘meaning relation’):
KNOWLEDGE: There is the totality of ‘knowledge’ that ‘builds up’ in the homo-sapiens actor in the course of time in the brain: both due to continuous interactions of the ‘brain’ with the ‘environment of the body’, as well as due to interactions ‘with the body itself’, as well as due to interactions ‘of the brain with itself’.
LANGUAGE: To be distinguished from knowledge is the dynamic system of ‘potential means of expression’, here simplistically called ‘language’, which can unfold over time in interaction with ‘knowledge’.
MEANING RELATIONSHIP: Finally, there is the dynamic ‘meaning relation’, an interaction mechanism that can link any knowledge elements to any language means of expression at any time.
Each of these mentioned components ‘knowledge’, ‘language’ as well as ‘meaning relation’ is extremely complex; no less complex is their interaction.
FUTURE AND EMOTIONS
In addition to the phenomenon of meaning, it also became apparent in the phenomenon of being applicable that the decision of being applicable also depends on an ‘available everyday situation’ in which a current correspondence can be ‘concretely shown’ or not.
If, in addition to a ‘conceivable meaning’ in the mind, we do not currently have any everyday situation that sufficiently corresponds to this meaning in the mind, then there are always two possibilities: We can give the ‘status of a possible future’ to this imagined construct despite the lack of reality reference, or not.
If we would decide to assign the status of a possible future to a ‘meaning in the head’, then there arise usually two requirements: (i) Can it be made sufficiently plausible in the light of the available knowledge that the ‘imagined possible situation’ can be ‘transformed into a new real situation’ in the ‘foreseeable future’ starting from the current real situation? And (ii) Are there ‘sustainable reasons’ why one should ‘want and affirm’ this possible future?
The first requirement calls for a powerful ‘science’ that sheds light on whether it can work at all. The second demand goes beyond this and brings the seemingly ‘irrational’ aspect of ’emotionality’ into play under the garb of ‘sustainability’: it is not simply about ‘knowledge as such’, it is also not only about a ‘so-called sustainable knowledge’ that is supposed to contribute to supporting the survival of life on planet Earth — and beyond –, it is rather also about ‘finding something good, affirming something, and then also wanting to decide it’. These last aspects are so far rather located beyond ‘rationality’; they are assigned to the diffuse area of ’emotions’; which is strange, since any form of ‘usual rationality’ is exactly based on these ’emotions’.[2]
SCIENTIFIC DISCOURSE AND EVERYDAY SITUATIONS
In the context of ‘rationality’ and ’emotionality’ just indicated, it is not uninteresting that in the conference topic ‘scientific discourse’ is thematized as a point of reference to clarify the status of text-capable machines.
The question is to what extent a ‘scientific discourse’ can serve as a reference point for a successful text at all?
For this purpose it can help to be aware of the fact that life on this planet earth takes place at every moment in an inconceivably large amount of ‘everyday situations’, which all take place simultaneously. Each ‘everyday situation’ represents a ‘present’ for the actors. And in the heads of the actors there is an individually different knowledge about how a present ‘can change’ or will change in a possible future.
This ‘knowledge in the heads’ of the actors involved can generally be ‘transformed into texts’ which in different ways ‘linguistically represent’ some of the aspects of everyday life.
The crucial point is that it is not enough for everyone to produce a text ‘for himself’ alone, quite ‘individually’, but that everyone must produce a ‘common text’ together ‘with everyone else’ who is also affected by the everyday situation. A ‘collective’ performance is required.
Nor is it a question of ‘any’ text, but one that is such that it allows for the ‘generation of possible continuations in the future’, that is, what is traditionally expected of a ‘scientific text’.
From the extensive discussion — since the times of Aristotle — of what ‘scientific’ should mean, what a ‘theory’ is, what an ’empirical theory’ should be, I sketch what I call here the ‘minimal concept of an empirical theory’.
The starting point is a ‘group of people’ (the ‘authors’) who want to create a ‘common text’.
This text is supposed to have the property that it allows ‘justifiable predictions’ for possible ‘future situations’, to which then ‘sometime’ in the future a ‘validity can be assigned’.
The authors are able to agree on a ‘starting situation’ which they transform by means of a ‘common language’ into a ‘source text’ [A].
It is agreed that this initial text may contain only ‘such linguistic expressions’ which can be shown to be ‘true’ ‘in the initial situation’.
In another text, the authors compile a set of ‘rules of change’ [V] that put into words ‘forms of change’ for a given situation.
Also in this case it is considered as agreed that only ‘such rules of change’ may be written down, of which all authors know that they have proved to be ‘true’ in ‘preceding everyday situations’.
The text with the rules of change V is on a ‘meta-level’ compared to the text A about the initial situation, which is on an ‘object-level’ relative to the text V.
The ‘interaction’ between the text V with the change rules and the text A with the initial situation is described in a separate ‘application text’ [F]: Here it is described when and how one may apply a change rule (in V) to a source text A and how this changes the ‘source text A’ to a ‘subsequent text A*’.
The application text F is thus on a next higher meta-level to the two texts A and V and can cause the application text to change the source text A.
The moment a new subsequent text A* exists, the subsequent text A* becomes the new initial text A.
If the new initial text A is such that a change rule from V can be applied again, then the generation of a new subsequent text A* is repeated.
This ‘repeatability’ of the application can lead to the generation of many subsequent texts <A*1, …, A*n>.
A series of many subsequent texts <A*1, …, A*n> is usually called a ‘simulation’.
Depending on the nature of the source text A and the nature of the change rules in V, it may be that possible simulations ‘can go quite differently’. The set of possible scientific simulations thus represents ‘future’ not as a single, definite course, but as an ‘arbitrarily large set of possible courses’.
The factors on which different courses depend are manifold. One factor are the authors themselves. Every author is, after all, with his corporeality completely himself part of that very empirical world which is to be described in a scientific theory. And, as is well known, any human actor can change his mind at any moment. He can literally in the next moment do exactly the opposite of what he thought before. And thus the world is already no longer the same as previously assumed in the scientific description.
Even this simple example shows that the emotionality of ‘finding good, wanting, and deciding’ lies ahead of the rationality of scientific theories. This continues in the so-called ‘sustainability discussion’.
SUSTAINABLE EMPIRICAL THEORY
With the ‘minimal concept of an empirical theory (ET)’ just introduced, a ‘minimal concept of a sustainable empirical theory (NET)’ can also be introduced directly.
While an empirical theory can span an arbitrarily large space of grounded simulations that make visible the space of many possible futures, everyday actors are left with the question of what they want to have as ‘their future’ out of all this? In the present we experience the situation that mankind gives the impression that it agrees to destroy the life beyond the human population more and more sustainably with the expected effect of ‘self-destruction’.
However, this self-destruction effect, which can be predicted in outline, is only one variant in the space of possible futures. Empirical science can indicate it in outline. To distinguish this variant before others, to accept it as ‘good’, to ‘want’ it, to ‘decide’ for this variant, lies in that so far hardly explored area of emotionality as root of all rationality.[2]
If everyday actors have decided in favor of a certain rationally lightened variant of possible future, then they can evaluate at any time with a suitable ‘evaluation procedure (EVAL)’ how much ‘percent (%) of the properties of the target state Z’ have been achieved so far, provided that the favored target state is transformed into a suitable text Z.
In other words, the moment we have transformed everyday scenarios into a rationally tangible state via suitable texts, things take on a certain clarity and thereby become — in a sense — simple. That we make such transformations and on which aspects of a real or possible state we then focus is, however, antecedent to text-based rationality as an emotional dimension.[2]
MAN-MACHINE
After these preliminary considerations, the final question is whether and how the main question of this conference, “How do AI text generators change scientific discourse?” can be answered in any way?
My previous remarks have attempted to show what it means for humans to collectively generate texts that meet the criteria for scientific discourse that also meets the requirements for empirical or even sustained empirical theories.
In doing so, it becomes apparent that both in the generation of a collective scientific text and in its application in everyday life, a close interrelation with both the shared experiential world and the dynamic knowledge and meaning components in each actor play a role.
The aspect of ‘validity’ is part of a dynamic world reference whose assessment as ‘true’ is constantly in flux; while one actor may tend to say “Yes, can be true”, another actor may just tend to the opposite. While some may tend to favor possible future option X, others may prefer future option Y. Rational arguments are absent; emotions speak. While one group has just decided to ‘believe’ and ‘implement’ plan Z, the others turn away, reject plan Z, and do something completely different.
This unsteady, uncertain character of future-interpretation and future-action accompanies the Homo Sapiens population from the very beginning. The not understood emotional complex constantly accompanies everyday life like a shadow.
Where and how can ‘text-enabled machines’ make a constructive contribution in this situation?
Assuming that there is a source text A, a change text V and an instruction F, today’s algorithms could calculate all possible simulations faster than humans could.
Assuming that there is also a target text Z, today’s algorithms could also compute an evaluation of the relationship between a current situation as A and the target text Z.
In other words: if an empirical or a sustainable-empirical theory would be formulated with its necessary texts, then a present algorithm could automatically compute all possible simulations and the degree of target fulfillment faster than any human alone.
But what about the (i) elaboration of a theory or (ii) the pre-rational decision for a certain empirical or even sustainable-empirical theory ?
A clear answer to both questions seems hardly possible to me at the present time, since we humans still understand too little how we ourselves collectively form, select, check, compare and also reject theories in everyday life.
My working hypothesis on the subject is: that we will very well need machines capable of learning in order to be able to fulfill the task of developing useful sustainable empirical theories for our common everyday life in the future. But when this will happen in reality and to what extent seems largely unclear to me at this point in time.[2]
COMMENTS
[1] https://zevedi.de/en/topics/ki-text-2/
[2] Talking about ’emotions’ in the sense of ‘factors in us’ that move us to go from the state ‘before the text’ to the state ‘written text’, that hints at very many aspects. In a small exploratory text “State Change from Non-Writing to Writing. Working with chatGPT4 in parallel” ( https://www.uffmm.org/2023/08/28/state-change-from-non-writing-to-writing-working-with-chatgpt4-in-parallel/ ) the author has tried to address some of these aspects. While writing it becomes clear that very many ‘individually subjective’ aspects play a role here, which of course do not appear ‘isolated’, but always flash up a reference to concrete contexts, which are linked to the topic. Nevertheless, it is not the ‘objective context’ that forms the core statement, but the ‘individually subjective’ component that appears in the process of ‘putting into words’. This individual subjective component is tentatively used here as a criterion for ‘authentic texts’ in comparison to ‘automated texts’ like those that can be generated by all kinds of bots. In order to make this difference more tangible, the author decided to create an ‘automated text’ with the same topic at the same time as the quoted authentic text. For this purpose he used chatGBT4 from openAI. This is the beginning of a philosophical-literary experiment, perhaps to make the possible difference more visible in this way. For purely theoretical reasons, it is clear that a text generated by chatGBT4 can never generate ‘authentic texts’ in origin, unless it uses as a template an authentic text that it can modify. But then this is a clear ‘fake document’. To prevent such an abuse, the author writes the authentic text first and then asks chatGBT4 to write something about the given topic without chatGBT4 knowing the authentic text, because it has not yet found its way into the database of chatGBT4 via the Internet.
Chatbots as Interfaces to the Human Knowledge Cloud?
Already at the end of the documented chat No.4 I had the impression, that an interaction with the chatbot chatGBT is somehow different compared to what most people until know have reported or stated in some reflected way about chatGBT.
In a first — and still a little bit vague — conclusion I have stated: “Apropos ‘rational’: that is a point which did surprise me really: as a kind of a summary it came out “that human rationality is composed of emotions, spiritual experience as well as conscious as well as unconscious cognitive processes. This is clearly not what most philosophers today would say. But it follows from the ‘richness of the facts’ which came as a resonance out of this chat. Not that the chatbot would have given this summary in advance as an important characterization of rationality, but as a human counterpart I could summarize all this properties out of the different separated statements [of chatGBT].”
And, indeed, the millions, if not yet billions, of documents in the world wide web are reflecting fragments of knowledge generated by humans which as a whole form a ‘cloud of knowledge’. The reflected echo of the real world through the medium of human brains is distributed in libraries and in the digital space. No individual person as such can make use of it; it is by far too big to be comprehensible.
Meanwhile search-algorithms can help us to make fragments of this ‘human knowledge cloud’ visible, but the search-results so far are ‘transformed’ in a way which is only of very limited use.
My encounter with chatGBT revealed some new glimpses of a possible new paradigm which perhaps wasn’t intended by openai themselves, but which seems now to be in reach: the individual brain has a limited capacity for ‘many documents’, but it has still an incredible ability to ‘transform’ billions of events into complex abstract patterns, inducing complex networks of relations, complex models, even complex theories.
If one looks to a chatbot like chatGBT as an ‘interface’ between a single human person and the ‘human knowledge cloud’, and this by only using ‘everyday language’, then — depending from the quality of the chatbot — this individual human can only with some ideas and questions ‘trigger’ those documents in the huge ‘human knowledge cloud’ which somehow are ‘fitting’ the triggering words. Thus this individual human person is step-wise encountering those fragments of the ‘human knowledge cloud’ which are in ‘resonance’ with his own words. In a bad case this interaction resembles those ‘echo chambers’ of the internet and the real world where people only get back what they put in.
But in a creative case the input of the individual human person can trigger fragments which are somehow confirming, but somehow non-confirming. This can be a trigger onto the the individual human person to generate some new ideas to feed back again the chatbot interface. While the ‘human knowledge cloud’ at a certain moment of time as such is ‘given’ and will not create ‘something new’, an individual person as an ever running learning process (in principle) could generate new aspects and therefore transform the feedbacks out of the ‘human knowledge cloud’ into some ‘new configurations’.
This principle as such is not new. The human brains are working since millions of years with a mechanisms, where the billions of abstract-concrete cognitive entities derived from perceptions and thinking are ‘stored’ in a more or less ‘unconscious space’ called memory, and only if some actual perception or thinking is ‘touching’ some of these stored entities by some ‘kind of similarity’ these will be re-activated and will become part of the actual perceiving and actual thinking. Thus ‘actual new things’ can become merged with ‘past stored things’ forming some new-past construct. This inherent duality of ‘new-past’ is the basis for that important human capability to be able to transcend the almighty presence and escape its clasp.
If we switch to the level of the human population then the ‘Human knowledge cloud’ plays the role of the ‘unconscious memory system’ collecting ‘past things’, and some kinds of chatbots (if they would be good enough) could play the role of the ‘merger mechanism’ bringing together the ‘past’ (given in the ‘Human knowledge cloud’ ) and the ‘presence’ (given in the actual living human person) and it is then up to the human person to generate some useful new-past patterns of thoughts and possible actions.
In case of the chat No.4 the human actor triggered lots of questions and suggestions to get ‘out of the human knowledge cloud’ those fragments which are somehow corresponding to the triggering words. And after about 8000 signs in the chat the chat ended up in these paragraphs:
Human: OK, this all sums up to the conclusion that human rationality is composed of emotions, spiritual experience as well as conscious as well as unconscious cognitive processes. Do I miss something important?
Machine (citing human documents): No, you have accurately summed up the components of human rationality. Additionally, human rationality is also composed of social and cultural influences, which can shape and inform our understanding of the world and our decisions. Additionally, human rationality is often impacted by our values and beliefs, which can provide a moral foundation for our behavior and decision making. Finally, human rationality is also impacted by our environment, which can provide a source of information and insight.
By itself the chatbot would not have been able to start with such a characterization. He can ‘reach out’ in the ‘Human knowledge cloud’, but the bot algorithm as such has not — as far as research can see at the moment — any kind of ‘creative rational power’ to transform the ‘given knowledge’ into some new ‘helpful’ knowledge. But at the other side, the human persons would not be able too to make use of ‘all the available knowledge’.
In the course of interaction the human questions could ‘drive’ the bot to collect ‘interesting facts’which could then become ‘accepted’ by the bot because they haven become ‘part of the chat’. Thus at the end of the chat the bot could accept that human rationality is composed of emotions, spiritual experience as well as conscious as well as unconscious cognitive processes. A human person ‘helped him’ to state this. This bot algorithm as such does not know anything and he cannot understand anything. Because chatbots — until now — do not possess real emotions, no real mystical experience, no unconscious or conscious human-like cognitive processes, they have no intelligence in the human format.
It is an open question what kind of ‘intelligence’ they have at all. Until know there is great number of ‘definitions’ around. No one is accepted as ‘that’ definition, especially the relationship between the ‘collection of machine intelligence definitions’ and the possible — also not really existing — collection of ‘human intelligence definitions’ is more or less unclear. Thus we are somehow ‘dreaming’ of intelligence, but nobody can really explain what it is …. We could seriously try, if we want …. but who wants it?
The context for this text is the whole block dedicated to the AAI (Actor-Actor Interaction) paradigm. The aim of this text is to give the big picture of all dimensions and components of this subject as it shows up during April 2019.
The first dimension introduced is the historical dimension, because this allows a first orientation in the course of events which lead to the actual situation. It starts with the early days of real computers in the thirties and forties of the 20 century.
The second dimension is the engineering dimension which describes the special view within which we are looking onto the overall topic of interactions between human persons and computers (or machines or technology or society). We are interested how to transform a given problem into a valuable solution in a methodological sound way called engineering.
The third dimension is the whole of society because engineering happens always as some process within a society. Society provides the resources which can be used and spends the preferences (values) what is understood as ‘valuable’, as ‘good’.
The fourth dimension is Philosophy as that kind of thinking which takes everything into account which can be thought and within thinking Philosophy clarifies conditions of thinking, possible tools of thinking and has to clarify when some symbolic expression becomes true.
HISTORY
In history we are looking back in the course of events. And this looking back is in a first step guided by the concepts of HCI (Human-Computer Interface) and HMI (Human-Machine Interaction).
It is an interesting phenomenon how the original focus of the interface between human persons and the early computers shifted to the more general picture of interaction because the computer as machine developed rapidly on account of the rapid development of the enabling hardware (HW) the enabling software (SW).
Within the general framework of hardware and software the so-called artificial intelligence (AI) developed first as a sub-topic on its own. Since the last 10 – 20 years it became in a way productive that it now seems to become a normal part of every kind of software. Software and smart software seem to be interchangeable. Thus the new wording of augmented or collective intelligence is emerging intending to bridge the possible gap between humans with their human intelligence and machine intelligence. There is some motivation from the side of society not to allow the impression that the smart (intelligent) machines will replace some day the humans. Instead one is propagating the vision of a new collective shape of intelligence where human and machine intelligence allows a symbiosis where each side gives hist best and receives a maximum in a win-win situation.
What is revealing about the actual situation is the fact that the mainstream is always talking about intelligence but not seriously about learning! Intelligence is by its roots a static concept representing some capabilities at a certain point of time, while learning is the more general dynamic concept that a system can change its behavior depending from actual external stimuli as well as internal states. And such a change includes real changes of some of its internal states. Intelligence does not communicate this dynamics! The most demanding aspect of learning is the need for preferences. Without preferences learning is impossible. Today machine learning is a very weak example of learning because the question of preferences is not a real topic there. One assumes that some reward is available, but one does not really investigate this topic. The rare research trying to do this job is stating that there is not the faintest idea around how a general continuous learning could happen. Human society is of no help for this problem while human societies have a clash of many, often opposite, values, and they have no commonly accepted view how to improve this situation.
ENGINEERING
Engineering is the art and the science to transform a given problem into a valuable and working solution. What is valuable decides the surrounding enabling society and this judgment can change during the course of time. Whether some solution is judged to be working can change during the course of time too but the criteria used for this judgment are more stable because of their adherence to concrete capabilities of technical solutions.
While engineering was and is always a kind of an art and needs such aspects like creativity, innovation, intuition etc. it is also and as far as possible a procedure driven by defined methods how to do things, and these methods are as far as possible backed up by scientific theories. The real engineer therefore synthesizes art, technology and science in a unique way which can not completely be learned in the schools.
In the past as well as in the present engineering has to happen in teams of many, often many thousands or even more, people which coordinate their brains by communication which enables in the individual brains some kind of understanding, of emerging world pictures, which in turn guide the perception, the decisions, and the concrete behavior of everybody. And these cognitive processes are embedded — in every individual team member — in mixtures of desires, emotions, as well as motivations, which can support the cognitive processes or obstruct them. Therefore an optimal result can only be reached if the communication serves all necessary cognitive processes and the interactions between the team members enable the necessary constructive desires, emotions, and motivations.
If an engineering process is done by a small group of dedicated experts — usually triggered by the given problem of an individual stakeholder — this can work well for many situations. It has the flavor of a so-called top-down approach. If the engineering deals with states of affairs where different kinds of people, citizens of some town etc. are affected by the results of such a process, the restriction to a small group of experts can become highly counterproductive. In those cases of a widespread interest it seems promising to include representatives of all the involved persons into the executing team to recognize their experiences and their kinds of preferences. This has to be done in a way which is understandable and appreciative, showing esteem for the others. This manner of extending the team of usual experts by situative experts can be termed bottom-up approach. In this usage of the term bottom-up this is not the opposite to top-down but is reflecting the extend in which members of a society are included insofar they are affected by the results of a process.
SOCIETY
Societies in the past and the present occur in a great variety of value systems, organizational structures, systems of power etc. Engineering processes within a society are depending completely on the available resources of a society and of its value systems.
The population dynamics, the needs and wishes of the people, the real territories, the climate, housing, traffic, and many different things are constantly producing demands to be solved if life shall be able and continue during the course of time.
The self-understanding and the self-management of societies is crucial for their ability to used engineering to improve life. This deserves communication and education to a sufficient extend, appropriate public rules of management, otherwise the necessary understanding and the freedom to act is lacking to use engineering in the right way.
PHILOSOPHY
Without communication no common constructive process can happen. Communication happens according to many implicit rules compressed in the formula who when can speak how about what with whom etc. Communication enables cognitive processes of for instance understanding, explanations, lines of arguments. Especially important for survival is the ability to make true descriptions and the ability to decide whether a statement is true or not. Without this basic ability communication will break down, coordination will break down, life will break down.
The basic discipline to clarify the rules and conditions of true communication, of cognition in general, is called Philosophy. All the more modern empirical disciplines are specializations of the general scope of Philosophy and it is Philosophy which integrates all the special disciplines in one, coherent framework (this is the ideal; actually we are far from this ideal).
Thus to describe the process of engineering driven by different kinds of actors which are coordinating themselves by communication is primarily the task of philosophy with all their sub-disciplines.
Thus some of the topics of Philosophy are language, text, theory, verification of a theory, functions within theories as algorithms, computation in general, inferences of true statements from given theories, and the like.
In this text I apply Philosophy as far as necessary. Especially I am introducing a new process model extending the classical systems engineering approach by including the driving actors explicitly in the formal representation of the process. Learning machines are included as standard tools to improve human thinking and communication. You can name this Augmented Social Learning Systems (ASLS). Compared to the wording Augmented Intelligence (AI) (as used for instance by the IBM marketing) the ASLS concept stresses that the primary point of reference are the biological systems which created and create machine intelligence as a new tool to enhance biological intelligence as part of biological learning systems. Compared to the wording Collective Intelligence (CI) (as propagated by the MIT, especially by Thomas W.Malone and colleagues) the spirit of the CI concept seems to be similar, but perhaps only a weak similarity.
Integrating Engineering and the Human Factor (info@uffmm.org) eJournal uffmm.org ISSN 2567-6458
