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.
In text No. 4, “WHAT IS LIFE? … When Life is ‘More,’ ‘Much More’ …”, there is a central passage that should be recalled here. Following the revelation of the empirically strong acceleration in the development of complexity of life on this planet, it states:
“The curve tells the ‘historical reality’ that ‘classical biological systems’ up to Homo sapiens were able to generate with their ‘previous means.’ However, with the emergence of the ‘Homo’ type, and especially with the life form ‘Homo sapiens,’ entirely new properties come into play. With the sub-population of Homo sapiens, there is a life form that, through its ‘cognitive’ dimension and its novel ‘symbolic communication,’ can generate the foundations for action at an extremely faster and more complex level.”
Following this “overall picture,” much suggests that the emergence of Homo sapiens (that is, us) after approximately 3.5 billion years of evolution, preceded by about 400 million years of molecular development, does not occur randomly. It is hard to overlook that the emergence of Homo sapiens lies almost at the “center of the developmental trajectory.” This fact can—or must?—raise the question of whether a “special responsibility” for Homo sapiens derives from this, concerning the “future of all life” on this planet—or even beyond? This leads to the second quotation from text No. 4:
“How can a ‘responsibility for global life’ be understood by us humans, let alone practically implemented by individual human beings? How should humans, who currently live approximately 60–120 years, think about a development that must be projected millions or even more years into the future?”
Such “responsibility with a view toward the future” would—from the perspective of life as a whole—only make sense if Homo sapiens were indeed the “only currently existing life form” that possesses exactly those characteristics required for “assuming responsibility” in this current phase of life’s development.
PRELIMINARY NOTE
The following text will gradually explain how all these elements are interconnected. At this stage, references to relevant literature will be kept to a minimum, as each section would otherwise require countless citations. Nevertheless, occasional remarks will be made. If the perspective presented in the “What is Life” texts proves fundamentally viable, it would need to be further refined and embedded into current specialized knowledge in a subsequent iteration. This process could involve contributions from various perspectives. For now, the focus is solely on developing a new, complex working hypothesis, grounded in existing knowledge.
THE HOMO SAPIENS EVENT
In modern science fiction novels and films, extraterrestrials are a popular device used to introduce something extraordinary to planet Earth—whether futuristic advancements or adventurous developments from the future appearing on Earth. Of course, these are thought constructs, through which we humans tell ourselves stories, as storytelling has been an essential part of human culture since the very beginning.
Against this backdrop, it is remarkable that the Homo Sapiens Event (HSE) has not yet received a comparable level of empathic attention. Yet, the HSE possesses all the ingredients to surpass even the boldest science fiction novels and films known to us. The developmental timeline on planet Earth alone spans approximately 3.9 billion years.
If we open ourselves to the idea that the biological might be understood as the direct unfolding of properties inherently present in the non-biological—and thus ultimately in energy itself, from which the entire known universe emerged—then we are dealing with a maximal event whose roots are as old as the known universe.
Ultimately—since energy remains more unknown than known to us—the HSE, as a property of energy, could even be older than the known universe itself.
IMAGE 1: Homo Sapiens Event (HSE)
PHILOSOPHICAL APPROACH
In this text, the Homo Sapiens Event (HSE) is discussed or written about because this is the only way in which the author’s brain can exchange thoughts with the brains of readers. This means that—regardless of the content—without some form of communication, there can be no exchange between different brains.
For Homo sapiens, such communication has, from the very beginning, occurred through a symbolic language, embedded in a variety of actions, gestures, facial expressions, vocal tones, and more. Therefore, it makes sense to render this mechanism of symbolic language within a human communication process transparent enough to understand when and what kind of content can be exchanged via symbolic communication.
When attempting to explain this mechanism of symbolic communication, it becomes evident that certain preconditions must be made explicit in advance—without these, the subsequent explanation cannot function.
To encompass the broadest possible perspective on the symbolic communication occurring here, the author of this text adopts the term “philosophical perspective”—in the sense that it is intended to include all known and conceivable perspectives.
Three Isolated Perspectives (Within Philosophy)
In addition to the perspective of biology (along with many other supporting disciplines), which has been used to describe the development of the biological on planet Earth up to the Homo Sapiens Event (HSE), some additional perspectives will now be introduced. These perspectives, while grounded in the biological framework, can provide valuable insights:
Empirical Neuroscience: It is concerned with the description and analysis of observable processes in the human brain.
Phenomenology: A subdiscipline of both philosophy and psychology, it serves to describe and analyze subjective experiences.
Empirical Psychology: It focuses on the description and analysis of observable human behavior.
IMAGE 2: (Hand-drawn sketch, illustrating the developmental process) Philosophical Perspective with the subdisciplines ‘Phenomenology,’ ‘(Empirical) Psychology,’ and ‘Neuroscience’
If these three perspectives are arranged side by side, the phenomenological view includes only our own (subjective) experiences, without a direct connection to the body or the world outside the body. This is the perspective with which every human is born and which accompanies them throughout life as the “normal view of things.”
From the perspective of empirical psychology, observable behavior of humans is the central focus (other life forms can also be studied in this way, though this falls more under biology). However, the phenomena of subjective experience are not accessible within the framework of empirical psychology. While the observable properties of the brain as an empirical object, as well as those of the body, are in principle accessible to empirical psychology, the empirical properties of the brain are generally assigned to (empirical) neuroscience, and those of the body to (empirical) physiology.
From the perspective of (empirical) neuroscience, the observable properties of the brain are accessible, but not the phenomena of subjective experience or observable behavior (nor the observable properties of the body).
It becomes clear that in the chosen systematic approach to scientific perspectives, each discipline has its own distinct observational domain, which is completely separate from the observational domains of the other disciplines! This means that each of these three perspectives can develop views of its object that differ fundamentally from those of the others. Considering that all three perspectives deal with the same real object—concrete instances of Homo sapiens (HS)—one must ask: What status should we assign to these three fundamentally different perspectives, along with their partial representations of Homo sapiens? Must we, in the scientific view, divide one material object into three distinct readings of Homo sapiens (HS): the HS-Phenomenal, the HS-Behavioral, and the HS-Brain?
In scientific practice, researchers are, of course, aware that the contents of the individual observational perspectives interact with one another in some way. Science today knows that subjective experiences (Ph) strongly correlate with certain brain events (N). Similarly, it is known that certain behaviors (Vh) correlate both with subjective experiences (Ph) and with brain events (N). In order to at least observe these interactions between different domains (Ph-Vh, Ph-N, N-Vh), interdisciplinary collaborations have long been established, such as Neurophenomenology (N-Ph) and Neuropsychology (N-Vh). The relationship between psychology and phenomenology is less clear. Early psychology was heavily introspective and thus hardly distinguishable from pure phenomenology, while empirical psychology still struggles with theoretical clarity today. The term “phenomenological psychology (Ph-Vh)” appears occasionally, though without a clearly defined subject area.
While there are some interdisciplinary collaborations, a fully integrated perspective is still nowhere to be found.
The following section will attempt to present a sketch of the overall system, highlighting important subdomains and illustrating the key interactions between these areas.
Sketch of the Overall System
The following “sketch of the overall system” establishes a conceptual connection between the domains of subjective experiences (Ph), brain events (N), bodily events (BDY), the environment of the body (W), and the observable behavior (Vh) of the body in the world.
IMAGE 3: (Hand-drawn sketch, illustrating the developmental process) Depicting the following elements: (1) Subjective experiences (Ph), (2) Brain events (N), (3) Bodily events (BDY), (4) Observable behavior (Vh) of the body in the world, (5) The environment of the body (W). In the lower-left corner of the image, a concrete instance of Homo sapiens (HS) is indicated, observing the world (W) along with the various bodies (BDY) of other Homo sapiens individuals. This HS can document its observations in the form of a text, using language (L).
IMAGE 3b: Hand-drawn sketch, illustrating the developmental process – The core idea for the concept of ‘Contextual Consciousness (CCONSC)’
As can be seen, the different domains are numbered from (1) to (5), with number (1) assigned to the domain of subjective experiences (Ph). This is motivated by the fact that, due to the structure of the human body, we perceive ourselves and all other events in the form of such subjective experiences as phenomena. Where these phenomena originate—whether from the brain, the body, or the surrounding world—is not directly apparent from the phenomena themselves. They are our phenomena.
While philosophers like Kant—and all his contemporaries—were still limited to considering the possible world and themselves solely from the perspective of their own phenomena, empirical sciences since around 1900 have gradually uncovered the processes behind the phenomena, localized in the brain, allowing them to be examined more concretely. Over time, increasingly precise correlations in time between brain events (N) and subjective experiences (Ph) were discovered.
One significant breakthrough was the ability to establish a temporal relationship between subjective experiences (Ph) and brain events (N). This suggested that while our subjective experiences cannot be measured directly as experiences, their temporal relationships with brain events allow for the localization of specific areas in the brain whose functioning appears to be a prerequisite for our subjective experience. This also provided a first empirical concretization of the common concept of consciousness, which can be formulated as a working hypothesis:
What we refer to as consciousness (CONSC, 1) corresponds to subjective experiences (Ph) that are enabled by brain events (N) occurring in specific areas of the brain. How exactly this can be understood will be explained further below.
The brain events (N) localized in the brain (BRAIN, 2) form a complex event space that has been increasingly researched since around 1900. It is generally clear that this space is highly dynamic, manifesting in the fact that all events interact with each other in multiple ways. The brain is structurally distinct from the rest of the body, but at the same time, it maintains exchange processes with thebody (BDY, 3)and the brain’s event space (BRAIN, 2). This exchange occurs via interfaces that can (i) translate body events into brain events and (ii) translate brain events into bodily events.
Examples of (i) include our sensory organs (eyes, ears, smell), which transform light, sound, or airborne molecules into brain events. Examples of (ii) include brain events that, for instance, activate muscles, leading to movements, or regulate glandular secretions, which influence bodily processes in various ways.
The body space (BODY, 4)is approximately 450 times larger than the space of brain events. It consists of multiple regions known as organs, which have complex internal structures and interact in diverse ways. Bodily events also maintain a complex exchange with brain events.
With the surrounding world (W,5), there are two types of exchange relationships. First, (i) interfaces where bodily events appear as excretions in the event space of the world (W), and second, (ii) bodily events that are directly controlled by brain events (e.g., in the case of movements). Together, these two forms of events constitute the OUTPUT (4a) of the body into the surrounding world (W). Conversely, there is also an INPUT (4b) from the world into the body’s event space. Here, we can distinguish between (i) events of the world that directly enter the body (e.g., nutrition intake) and (ii) events of the world that, through sensory interfaces of the body, are translated into brain events (e.g., seeing, hearing).
Given this setup, an important question arises:
How does the brain distinguish among the vast number of brain events (N)—whether an event is (i) an N originating from within the brain itself, (ii) an N originating from bodily events (BDY), or (iii) an N originating—via the body—from the external world (W)?
In other words: How can the brain recognize whether a given brain event (N) is (i) N from N, (ii) N from BDY, or (iii) N from W?
This question will be addressed further with a proposed working hypothesis.
Concept of ‘Consciousness’; Basic Assumptions
In the preceding section, an initial working hypothesis was proposed to characterize the concept of consciousness: what we refer to as consciousness (CONSC, 1) pertains to subjective experiences (Ph) that are enabled by brain events (N) occurring in specific regions of the brain.
This working hypothesis will now be refined by introducing additional assumptions. While all of these assumptions are based on scientific and philosophical knowledge, which are supported by various forms of justification, many details remain unresolved, and a fully integrated theory is still lacking. The following additional assumptions apply:
Normally, all phenomena that we can explicitly experience subjectively are classified as part of explicit consciousness (ECONSC ⊆ CONSC). We then say that we are aware of something.
However, there is also a consciousness of something that is not directly correlated with any explicit phenomenon. These are situations in which we assume relationships between phenomena, even though these relationships themselves are not experienced as phenomena. Examples include:
Spoken sounds that refer to phenomena,
Comparative size relations between phenomena,
Partial properties of a phenomenon,
The relationship between current and remembered phenomena,
The relationship between perceived and remembered phenomena. This form of consciousness that exists in the context of phenomena but is not itself a phenomenon will be referred to here as contextual consciousness (CCONSC ⊆ CONSC). Here, too, we can say that we are aware of something, but in a somewhat different manner.
This distinction between explicit consciousness (ECONSC) and contextual consciousness (CCONSC) suggests that the ability to be aware of something is broader than what explicit consciousness alone implies. This leads to the working hypothesis that what we intuitively call consciousness (CONSC) is the result of the way our brain operates.
Basic Assumptions on the Relationship Between Brain Events and Consciousness
Given today’s neuroscientific findings, the brain appears as an exceedingly complex system. For the considerations in this text, the following highly simplified working hypotheses are formulated:
Empirical brain events are primarily generated and processed by specialized cells called neurons (N). A neuron can register events from many other neurons and generate exactly one event, which can then be transmitted to many other neurons. This output event can also be fed back as an input event to the generating neuron (direct feedback loops). Time and intensity also play a role in the generation and transmission of events.
The arrangement of neurons is both serial (an event can be transmitted from one neuron to the next, and so on, with modifications occurring along the way) and hierarchical (layers exist in which events from lower layers can be represented in a compressed or abstracted form in higher layers).
From this, the basic assumptions about the relationship between brain events and conscious events are as follows:
Some brain events become explicitly conscious phenomena (ECONSC).
Contextual consciousness (CCONSC) occurs when a network of neurons represents a relationship between different units. The relationship itself is then consciously known, but since a relationship is not an object (not an explicit phenomenon), we can know these relationships, but they do not appear explicitly as phenomena (e.g., the explicit phenomenon of a “red car” in text and the perceptual object of a “red car”—we can know the relationship between them, but it is not explicitly given).
The concept of consciousness (CONSC)thus consists at least of explicit phenomenal consciousness (ECONSC) and contextual consciousness (CCONSC). A more detailed analysis of both the phenomenal space (Ph) and the working processes of the brain (N) as the domain of all brain events will allow for further differentiation of these working hypotheses.
After these preliminary considerations regarding the different event spaces in which a Homo sapiens (HS) can participate through different access modalities (W – BDY – N(CONSC)), the following section will provide an initial sketch of the role of language (L) (with further elaborations to follow).
Descriptive Texts
As previously indicated, within each of the listed observational perspectives—observable behavior (Vh), subjective experiences (Ph), and brain events (N) (see IMAGE 2)—texts are created through which actors exchange their individual views. Naturally, these texts must be formulated in a language that all participants can understand and actively use.
Unlike everyday language, modern scientific discourse imposes minimal requirements on these texts. Some of these requirements can be described as follows:
For all linguistic expressions that refer to observable events within the domain of a given perspective, it must be clear how their empirical reference to a real objectcan be verified intersubjectively. In the verification process, it must be possible to determine at least one of the following: (i) It applies (is true), (ii) It does not apply (is false), (iii) A decision is not possible (undetermined)
It must also be clear: (i) Which linguistic expressions are not empirical but abstract, (ii) How these abstract expressions relate to other abstract expressions or empirical expressions, (iii) To what extent expressions that are themselves not empirical can still be evaluated in terms of truth or falsehood through their relationships to other expressions
How these requirements are practically implemented remains, in principle, open—as long as they function effectively among all participating actors.
While these requirements can, in principle, be fulfilled within the perspective of empirical psychology and neuroscience, a phenomenological perspective cannot fully meet at least the first requirement, since the subjective phenomena of an individual actor cannot be observed by other actors. This is only possible—and even then, only partially—through indirect means.
For example, if there is a subjective phenomenon (such as an optical stimulus, a smell, or a sound) that correlates with something another actor can also perceive, then one could say: “I see a red light,” and the other actor can assume that the speaker is seeing something similar to what they themselves are seeing.
However, if someone says, “I have a toothache,” the situation becomes more difficult—because the other person may never have experienced toothache before and therefore does not fully understand what the speaker means. With the vast range of bodily sensations, emotions, dreams, and other subjective states, it becomes increasingly challenging to synchronize perceptual content.
The Asymmetry Between Empirical and Non-Empirical Perspectives
This indicates a certain asymmetry between empirical and non-empirical perspectives. Using the example of empirical psychology and neuroscience, we can demonstrate that we can engage empirically with the reality surrounding us—yet, as actors, we remain irreversibly anchored in a phenomenological (subjective) perspective.
The key question arises: How can we realize a transition from the inherently built-in phenomenological perspective to an empirical perspective?
Where is the missing link? What constitutes the possible connection that we cannot directly perceive?
Referring to IMAGE 3, this question can be translated into the following format: How can the brain recognize whether a given brain event (N) originates from (i) another brain event (N from N), (ii) a bodily event (N from BDY), (iii) an external world event (N from W)?
This question will be explored further in the following sections.
Outlook
The following text will provide a more detailed explanation of the functioning of symbolic language, particularly in close cooperation with thinking. It will also illustrate that individual intelligence unfolds its true power only in the context of collective human communication and cooperation.
The whole text shows a dynamic, which induces many changes. Difficult to plan ‘in advance’.
Perhaps, some time, it will look like a ‘book’, at least ‘for a moment’.
I have started a ‘book project’ in parallel. This was motivated by the need to provide potential users of our new oksimo.R software with a coherent explanation of how the oksimo.R software, when used, generates an empirical theory in the format of a screenplay. The primary source of the book is in German and will be translated step by step here in the uffmm.blog.
INTRODUCTION
In a rather foundational paper about an idea, how one can generalize ‘systems engineering’ [*1] to the art of ‘theory engineering’ [1] a new conceptual framework has been outlined for a ‘sustainable applied empirical theory (SAET)’. Part of this new framework has been the idea that the classical recourse to groups of special experts (mostly ‘engineers’ in engineering) is too restrictive in the light of the new requirement of being sustainable: sustainability is primarily based on ‘diversity’ combined with the ‘ability to predict’ from this diversity probable future states which keep life alive. The aspect of diversity induces the challenge to see every citizen as a ‘natural expert’, because nobody can know in advance and from some non-existing absolut point of truth, which knowledge is really important. History shows that the ‘mainstream’ is usually to a large degree ‘biased’ [*1b].
With this assumption, that every citizen is a ‘natural expert’, science turns into a ‘general science’ where all citizens are ‘natural members’ of science. I will call this more general concept of science ‘sustainable citizen science (SCS)’ or ‘Citizen Science 2.0 (CS2)’. The important point here is that a sustainable citizen science is not necessarily an ‘arbitrary’ process. While the requirement of ‘diversity’ relates to possible contents, to possible ideas, to possible experiments, and the like, it follows from the other requirement of ‘predictability’/ of being able to make some useful ‘forecasts’, that the given knowledge has to be in a format, which allows in a transparent way the construction of some consequences, which ‘derive’ from the ‘given’ knowledge and enable some ‘new’ knowledge. This ability of forecasting has often been understood as the business of ‘logic’ providing an ‘inference concept’ given by ‘rules of deduction’ and a ‘practical pattern (on the meta level)’, which defines how these rules have to be applied to satisfy the inference concept. But, looking to real life, to everyday life or to modern engineering and economy, one can learn that ‘forecasting’ is a complex process including much more than only cognitive structures nicely fitting into some formulas. For this more realistic forecasting concept we will use here the wording ‘common logic’ and for the cognitive adventure where common logic is applied we will use the wording ‘common science’. ‘Common science’ is structurally not different from ‘usual science’, but it has a substantial wider scope and is using the whole of mankind as ‘experts’.
The following chapters/ sections try to illustrate this common science view by visiting different special views which all are only ‘parts of a whole’, a whole which we can ‘feel’ in every moment, but which we can not yet completely grasp with our theoretical concepts.
CONTENT
Language (Main message: “The ordinary language is the ‘meta language’ to every special language. This can be used as a ‘hint’ to something really great: the mystery of the ‘self-creating’ power of the ordinary language which for most people is unknown although it happens every moment.”)
Concrete Abstract Statements (Main message: “… you will probably detect, that nearly all words of a language are ‘abstract words’ activating ‘abstract meanings’. …If you cannot provide … ‘concrete situations’ the intended meaning of your abstract words will stay ‘unclear’: they can mean ‘nothing or all’, depending from the decoding of the hearer.”)
True False Undefined (Main message: “… it reveals that ’empirical (observational) evidence’ is not necessarily an automatism: it presupposes appropriate meaning spaces embedded in sets of preferences, which are ‘observation friendly’.“
Beyond Now (Main message: “With the aid of … sequences revealing possible changes the NOW is turned into a ‘moment’ embedded in a ‘process’, which is becoming the more important reality. The NOW is something, but the PROCESS is more.“)
Playing with the Future (Main message: “In this sense seems ‘language’ to be the master tool for every brain to mediate its dynamic meaning structures with symbolic fix points (= words, expressions) which as such do not change, but the meaning is ‘free to change’ in any direction. And this ‘built in ‘dynamics’ represents an ‘internal potential’ for uncountable many possible states, which could perhaps become ‘true’ in some ‘future state’. Thus ‘future’ can begin in these potentials, and thinking is the ‘playground’ for possible futures.(but see [18])”)
Forecasting – Prediction: What? (This chapter explains the cognitive machinery behind forecasting/ predictions, how groups of human actors can elaborate shared descriptions, and how it is possible to start with sequences of singularities to built up a growing picture of the empirical world which appears as a radical infinite and indeterministic space. )
!!! From here all the following chapters have to be re-written !!!
Boolean Logic (Explains what boolean logic is, how it enables the working of programmable machines, but that it is of nearly no help for the ‘heart’ of forecasting.)
/* Often people argue against the usage of the wikipedia encyclopedia as not ‘scientific’ because the ‘content’ of an entry in this encyclopedia can ‘change’. This presupposes the ‘classical view’ of scientific texts to be ‘stable’, which presupposes further, that such a ‘stable text’ describes some ‘stable subject matter’. But this view of ‘steadiness’ as the major property of ‘true descriptions’ is in no correspondence with real scientific texts! The reality of empirical science — even as in some special disciplines like ‘physics’ — is ‘change’. Looking to Aristotle’s view of nature, to Galileo Galilei, to Newton, to Einstein and many others, you will not find a ‘single steady picture’ of nature and science, and physics is only a very simple strand of science compared to the live-sciences and many others. Thus wikipedia is a real scientific encyclopedia give you the breath of world knowledge with all its strengths and limits at once. For another, more general argument, see In Favour for Wikipedia */
[*1] Meaning operator ‘…’ : In this text (and in nearly all other texts of this author) the ‘inverted comma’ is used quite heavily. In everyday language this is not common. In some special languages (theory of formal languages or in programming languages or in meta-logic) the inverted comma is used in some special way. In this text, which is primarily a philosophical text, the inverted comma sign is used as a ‘meta-language operator’ to raise the intention of the reader to be aware, that the ‘meaning’ of the word enclosed in the inverted commas is ‘text specific’: in everyday language usage the speaker uses a word and assumes tacitly that his ‘intended meaning’ will be understood by the hearer of his utterance as ‘it is’. And the speaker will adhere to his assumption until some hearer signals, that her understanding is different. That such a difference is signaled is quite normal, because the ‘meaning’ which is associated with a language expression can be diverse, and a decision, which one of these multiple possible meanings is the ‘intended one’ in a certain context is often a bit ‘arbitrary’. Thus, it can be — but must not — a meta-language strategy, to comment to the hearer (or here: the reader), that a certain expression in a communication is ‘intended’ with a special meaning which perhaps is not the commonly assumed one. Nevertheless, because the ‘common meaning’ is no ‘clear and sharp subject’, a ‘meaning operator’ with the inverted commas has also not a very sharp meaning. But in the ‘game of language’ it is more than nothing 🙂
[*1b] That the main stream ‘is biased’ is not an accident, not a ‘strange state’, not a ‘failure’, it is the ‘normal state’ based on the deeper structure how human actors are ‘built’ and ‘genetically’ and ‘cultural’ ‘programmed’. Thus the challenge to ‘survive’ as part of the ‘whole biosphere’ is not a ‘partial task’ to solve a single problem, but to solve in some sense the problem how to ‘shape the whole biosphere’ in a way, which enables a live in the universe for the time beyond that point where the sun is turning into a ‘red giant’ whereby life will be impossible on the planet earth (some billion years ahead)[22]. A remarkable text supporting this ‘complex view of sustainability’ can be found in Clark and Harvey, summarized at the end of the text. [23]
[*2] The meaning of the expression ‘normal’ is comparable to a wicked problem. In a certain sense we act in our everyday world ‘as if there exists some standard’ for what is assumed to be ‘normal’. Look for instance to houses, buildings: to a certain degree parts of a house have a ‘standard format’ assuming ‘normal people’. The whole traffic system, most parts of our ‘daily life’ are following certain ‘standards’ making ‘planning’ possible. But there exists a certain percentage of human persons which are ‘different’ compared to these introduced standards. We say that they have a ‘handicap’ compared to this assumed ‘standard’, but this so-called ‘standard’ is neither 100% true nor is the ‘given real world’ in its properties a ‘100% subject’. We have learned that ‘properties of the real world’ are distributed in a rather ‘statistical manner’ with different probabilities of occurrences. To ‘find our way’ in these varying occurrences we try to ‘mark’ the main occurrences as ‘normal’ to enable a basic structure for expectations and planning. Thus, if in this text the expression ‘normal’ is used it refers to the ‘most common occurrences’.
[*3] Thus we have here a ‘threefold structure’ embracing ‘perception events, memory events, and expression events’. Perception events represent ‘concrete events’; memory events represent all kinds of abstract events but they all have a ‘handle’ which maps to subsets of concrete events; expression events are parts of an abstract language system, which as such is dynamically mapped onto the abstract events. The main source for our knowledge about perceptions, memory and expressions is experimental psychology enhanced by many other disciplines.
[*4] Characterizing language expressions by meaning – the fate of any grammar: the sentence ” … ‘words’ (= expressions) of a language which can activate such abstract meanings are understood as ‘abstract words’, ‘general words’, ‘category words’ or the like.” is pointing to a deep property of every ordinary language, which represents the real power of language but at the same time the great weakness too: expressions as such have no meaning. Hundreds, thousands, millions of words arranged in ‘texts’, ‘documents’ can show some statistical patterns’ and as such these patterns can give some hint which expressions occur ‘how often’ and in ‘which combinations’, but they never can give a clue to the associated meaning(s). During more than three-thousand years humans have tried to describe ordinary language in a more systematic way called ‘grammar’. Due to this radically gap between ‘expressions’ as ‘observable empirical facts’ and ‘meaning constructs’ hidden inside the brain it was all the time a difficult job to ‘classify’ expressions as representing a certain ‘type’ of expression like ‘nouns’, ‘predicates’, ‘adjectives’, ‘defining article’ and the like. Without regressing to the assumed associated meaning such a classification is not possible. On account of the fuzziness of every meaning ‘sharp definitions’ of such ‘word classes’ was never and is not yet possible. One of the last big — perhaps the biggest ever — project of a complete systematic grammar of a language was the grammar project of the ‘Akademie der Wissenschaften der DDR’ (‘Academy of Sciences of the GDR’) from 1981 with the title “Grundzüge einer Deutschen Grammatik” (“Basic features of a German grammar”). A huge team of scientists worked together using many modern methods. But in the preface you can read, that many important properties of the language are still not sufficiently well describable and explainable. See: Karl Erich Heidolph, Walter Flämig, Wolfgang Motsch et al.: Grundzüge einer deutschen Grammatik. Akademie, Berlin 1981, 1028 Seiten.
[*5] Differing opinions about a given situation manifested in uttered expressions are a very common phenomenon in everyday communication. In some sense this is ‘natural’, can happen, and it should be no substantial problem to ‘solve the riddle of being different’. But as you can experience, the ability of people to solve the occurrence of different opinions is often quite weak. Culture is suffering by this as a whole.
[1] Gerd Doeben-Henisch, 2022, From SYSTEMS Engineering to THEORYEngineering, see: https://www.uffmm.org/2022/05/26/from-systems-engineering-to-theory-engineering/(Remark: At the time of citation this post was not yet finished, because there are other posts ‘corresponding’ with that post, which are too not finished. Knowledge is a dynamic network of interwoven views …).
[1d] ‘usual science’ is the game of science without having a sustainable format like in citizen science 2.0.
[2] Science, see e.g. wkp-en: https://en.wikipedia.org/wiki/Science
Citation = “In modern science, the term “theory” refers to scientific theories, a well-confirmed type of explanation of nature, made in a way consistent with the scientific method, and fulfilling the criteria required by modern science. Such theories are described in such a way that scientific tests should be able to provide empirical support for it, or empirical contradiction (“falsify“) of it. Scientific theories are the most reliable, rigorous, and comprehensive form of scientific knowledge,[1] in contrast to more common uses of the word “theory” that imply that something is unproven or speculative (which in formal terms is better characterized by the word hypothesis).[2] Scientific theories are distinguished from hypotheses, which are individual empirically testableconjectures, and from scientific laws, which are descriptive accounts of the way nature behaves under certain conditions.”
[2b] History of science in wkp-en: https://en.wikipedia.org/wiki/History_of_science#Scientific_Revolution_and_birth_of_New_Science
[3] Theory, see wkp-en: https://en.wikipedia.org/wiki/Theory#:~:text=A%20theory%20is%20a%20rational,or%20no%20discipline%20at%20all.
Citation = “A theory is a rational type of abstract thinking about a phenomenon, or the results of such thinking. The process of contemplative and rational thinking is often associated with such processes as observational study or research. Theories may be scientific, belong to a non-scientific discipline, or no discipline at all. Depending on the context, a theory’s assertions might, for example, include generalized explanations of how nature works. The word has its roots in ancient Greek, but in modern use it has taken on several related meanings.”
Citation = “In modern science, the term “theory” refers to scientific theories, a well-confirmed type of explanation of nature, made in a way consistent with the scientific method, and fulfilling the criteria required by modern science. Such theories are described in such a way that scientific tests should be able to provide empirical support for it, or empirical contradiction (“falsify“) of it. Scientific theories are the most reliable, rigorous, and comprehensive form of scientific knowledge,[1] in contrast to more common uses of the word “theory” that imply that something is unproven or speculative (which in formal terms is better characterized by the word hypothesis).[2] Scientific theories are distinguished from hypotheses, which are individual empirically testableconjectures, and from scientific laws, which are descriptive accounts of the way nature behaves under certain conditions.”
[4b] Empiricism in wkp-en: https://en.wikipedia.org/wiki/Empiricism
[4c] Scientific method in wkp-en: https://en.wikipedia.org/wiki/Scientific_method
Citation =”The scientific method is an empirical method of acquiring knowledge that has characterized the development of science since at least the 17th century (with notable practitioners in previous centuries). It involves careful observation, applying rigorous skepticism about what is observed, given that cognitive assumptions can distort how one interprets the observation. It involves formulating hypotheses, via induction, based on such observations; experimental and measurement-based statistical testing of deductions drawn from the hypotheses; and refinement (or elimination) of the hypotheses based on the experimental findings. These are principles of the scientific method, as distinguished from a definitive series of steps applicable to all scientific enterprises.[1][2][3] [4c]
and
Citation = “The purpose of an experiment is to determine whether observations[A][a][b] agree with or conflict with the expectations deduced from a hypothesis.[6]: Book I, [6.54] pp.372, 408 [b] Experiments can take place anywhere from a garage to a remote mountaintop to CERN’s Large Hadron Collider. There are difficulties in a formulaic statement of method, however. Though the scientific method is often presented as a fixed sequence of steps, it represents rather a set of general principles.[7] Not all steps take place in every scientific inquiry (nor to the same degree), and they are not always in the same order.[8][9]”
[5] Gerd Doeben-Henisch, “Is Mathematics a Fake? No! Discussing N.Bourbaki, Theory of Sets (1968) – Introduction”, 2022, https://www.uffmm.org/2022/06/06/n-bourbaki-theory-of-sets-1968-introduction/
[6] Logic, see wkp-en: https://en.wikipedia.org/wiki/Logic
[7] W. C. Kneale, The Development of Logic, Oxford University Press (1962)
[8] Set theory, in wkp-en: https://en.wikipedia.org/wiki/Set_theory
[9] N.Bourbaki, Theory of Sets , 1968, with a chapter about structures, see: https://en.wikipedia.org/wiki/%C3%89l%C3%A9ments_de_math%C3%A9matique
[10] = [5]
[11] Ludwig Josef Johann Wittgenstein ( 1889 – 1951): https://en.wikipedia.org/wiki/Ludwig_Wittgenstein
[12] Ludwig Wittgenstein, 1953: Philosophische Untersuchungen [PU], 1953: Philosophical Investigations [PI], translated by G. E. M. Anscombe /* For more details see: https://en.wikipedia.org/wiki/Philosophical_Investigations */
[13] Wikipedia EN, Speech acts: https://en.wikipedia.org/wiki/Speech_act
[14] While the world view constructed in a brain is ‘virtual’ compared to the ‘real word’ outside the brain (where the body outside the brain is also functioning as ‘real world’ in relation to the brain), does the ‘virtual world’ in the brain function for the brain mostly ‘as if it is the real world’. Only under certain conditions can the brain realize a ‘difference’ between the triggering outside real world and the ‘virtual substitute for the real world’: You want to use your bicycle ‘as usual’ and then suddenly you have to notice that it is not at that place where is ‘should be’. …
[15] Propositional Calculus, see wkp-en: https://en.wikipedia.org/wiki/Propositional_calculus#:~:text=Propositional%20calculus%20is%20a%20branch,of%20arguments%20based%20on%20them.
[16] Boolean algebra, see wkp-en: https://en.wikipedia.org/wiki/Boolean_algebra
[17] Boolean (or propositional) Logic: As one can see in the mentioned articles of the English wikipedia, the term ‘boolean logic’ is not common. The more logic-oriented authors prefer the term ‘boolean calculus’ [15] and the more math-oriented authors prefer the term ‘boolean algebra’ [16]. In the view of this author the general view is that of ‘language use’ with ‘logic inference’ as leading idea. Therefore the main topic is ‘logic’, in the case of propositional logic reduced to a simple calculus whose similarity with ‘normal language’ is widely ‘reduced’ to a play with abstract names and operators. Recommended: the historical comments in [15].
[18] Clearly, thinking alone can not necessarily induce a possible state which along the time line will become a ‘real state’. There are numerous factors ‘outside’ the individual thinking which are ‘driving forces’ to push real states to change. But thinking can in principle synchronize with other individual thinking and — in some cases — can get a ‘grip’ on real factors causing real changes.
[19] This kind of knowledge is not delivered by brain science alone but primarily from experimental (cognitive) psychology which examines observable behavior and ‘interprets’ this behavior with functional models within an empirical theory.
[20] Predicate Logic or First-Order Logic or … see: wkp-en: https://en.wikipedia.org/wiki/First-order_logic#:~:text=First%2Dorder%20logic%E2%80%94also%20known,%2C%20linguistics%2C%20and%20computer%20science.
[21] Gerd Doeben-Henisch, In Favour of Wikipedia, https://www.uffmm.org/2022/07/31/in-favour-of-wikipedia/, 31 July 2022
[22] The sun, see wkp-ed https://en.wikipedia.org/wiki/Sun (accessed 8 Aug 2022)
[23] By Clark, William C., and Alicia G. Harley – https://doi.org/10.1146/annurev-environ-012420-043621, Clark, William C., and Alicia G. Harley. 2020. “Sustainability Science: Toward a Synthesis.” Annual Review of Environment and Resources 45 (1): 331–86, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=109026069
[24] Sustainability in wkp-en: https://en.wikipedia.org/wiki/Sustainability#Dimensions_of_sustainability
[27] SDG 4 in wkp-en: https://en.wikipedia.org/wiki/Sustainable_Development_Goal_4
[28] Thomas Rid, Rise of the Machines. A Cybernetic History, W.W.Norton & Company, 2016, New York – London
[29] Doeben-Henisch, G., 2006, Reducing Negative Complexity by a Semiotic System In: Gudwin, R., & Queiroz, J., (Eds). Semiotics and Intelligent Systems Development. Hershey et al: Idea Group Publishing, 2006, pp.330-342
[30] Döben-Henisch, G., Reinforcing the global heartbeat: Introducing the planet earth simulator project, In M. Faßler & C. Terkowsky (Eds.), URBAN FICTIONS. Die Zukunft des Städtischen. München, Germany: Wilhelm Fink Verlag, 2006, pp.251-263
[29] The idea that individual disciplines are not good enough for the ‘whole of knowledge’ is expressed in a clear way in a video of the theoretical physicist and philosopher Carlo Rovell: Carlo Rovelli on physics and philosophy, June 1, 2022, Video from the Perimeter Institute for Theoretical Physics. Theoretical physicist, philosopher, and international bestselling author Carlo Rovelli joins Lauren and Colin for a conversation about the quest for quantum gravity, the importance of unlearning outdated ideas, and a very unique way to get out of a speeding ticket.
[] By Azote for Stockholm Resilience Centre, Stockholm University – https://www.stockholmresilience.org/research/research-news/2016-06-14-how-food-connects-all-the-sdgs.html, CC BY 4.0, https://commons.wikimedia.org/w/index.php?curid=112497386
[] Sierra Club in wkp-en: https://en.wikipedia.org/wiki/Sierra_Club
[] Herbert Bruderer, Where is the Cradle of the Computer?, June 20, 2022, URL: https://cacm.acm.org/blogs/blog-cacm/262034-where-is-the-cradle-of-the-computer/fulltext (accessed: July 20, 2022)
[] UN. Secretary-General; World Commission on Environment and Development, 1987, Report of the World Commission on Environment and Development : note / by the Secretary-General., https://digitallibrary.un.org/record/139811 (accessed: July 20, 2022) (A more readable format: https://sustainabledevelopment.un.org/content/documents/5987our-common-future.pdf )
/* Comment: Gro Harlem Brundtland (Norway) has been the main coordinator of this document */
[] Chaudhuri, S.,et al.Neurosymbolic programming. Foundations and Trends in Programming Languages 7, 158-243 (2021).
[] Nello Cristianini, Teresa Scantamburlo, James Ladyman, The social turn of artificial intelligence, in: AI & SOCIETY, https://doi.org/10.1007/s00146-021-01289-8
[] Carl DiSalvo, Phoebe Sengers, and Hrönn Brynjarsdóttir, Mapping the landscape of sustainable hci, In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’10, page 1975–1984, New York, NY, USA, 2010. Association for Computing Machinery.
[] Claude Draude, Christian Gruhl, Gerrit Hornung, Jonathan Kropf, Jörn Lamla, Jan Marco Leimeister, Bernhard Sick, Gerd Stumme, Social Machines, in: Informatik Spektrum, https://doi.org/10.1007/s00287-021-01421-4
[] EU: High-Level Expert Group on AI (AI HLEG), A definition of AI: Main capabilities and scientific disciplines, European Commission communications published on 25 April 2018 (COM(2018) 237 final), 7 December 2018 (COM(2018) 795 final) and 8 April 2019 (COM(2019) 168 final). For our definition of Artificial Intelligence (AI), please refer to our document published on 8 April 2019: https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=56341
[] EU: High-Level Expert Group on AI (AI HLEG), Policy and investment recommendations for trustworthy Artificial Intelligence, 2019, https://digital-strategy.ec.europa.eu/en/library/policy-and-investment-recommendations-trustworthy-artificial-intelligence
[] European Union. Regulation 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC General Data Protection Regulation; http://eur-lex.europa.eu/eli/reg/2016/679/oj (Wirksam ab 25.Mai 2018) [26.2.2022]
[] C.S. Holling. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4(1):1–23, 1973
[] John P. van Gigch. 1991. System Design Modeling and Metamodeling. Springer US. DOI:https://doi.org/10.1007/978-1-4899-0676-2
[] Gudwin, R.R. (2003), On a Computational Model of the Peircean Semiosis, IEEE KIMAS 2003 Proceedings
[] J.A. Jacko and A. Sears, Eds., The Human-Computer Interaction Handbook. Fundamentals, Evolving Technologies, and emerging Applications. 1st edition, 2003.
[] LeCun, Y., Bengio, Y., & Hinton, G. Deep learning. Nature 521, 436-444 (2015).
[] Lenat, D. What AI can learn from Romeo & Juliet.Forbes (2019)
[] Pierre Lévy, Collective Intelligence. mankind’s emerging world in cyberspace, Perseus books, Cambridge (M A), 1997 (translated from the French Edition 1994 by Robert Bonnono)
[] Lexikon der Nachhaltigkeit, ‘Starke Nachhaltigkeit‘, https://www.nachhaltigkeit.info/artikel/schwache_vs_starke_nachhaltigkeit_1687.htm (acessed: July 21, 2022)
[] Michael L. Littman, Ifeoma Ajunwa, Guy Berger, Craig Boutilier, Morgan Currie, Finale Doshi-Velez, Gillian Hadfield, Michael C. Horowitz, Charles Isbell, Hiroaki Kitano, Karen Levy, Terah Lyons, Melanie Mitchell, Julie Shah, Steven Sloman, Shannon Vallor, and Toby Walsh. “Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report.” Stanford University, Stanford, CA, September 2021. Doc: http://ai100.stanford.edu/2021-report.
[] Kathryn Merrick. Value systems for developmental cognitive robotics: A survey. Cognitive Systems Research, 41:38 – 55, 2017
[] Illah Reza Nourbakhsh and Jennifer Keating, AI and Humanity, MIT Press, 2020 /* An examination of the implications for society of rapidly advancing artificial intelligence systems, combining a humanities perspective with technical analysis; includes exercises and discussion questions. */
[] Olazaran, M. , A sociological history of the neural network controversy. Advances in Computers37, 335-425 (1993).
[] Friedrich August Hayek (1945), The use of knowledge in society. The American Economic Review 35, 4 (1945), 519–530
[] Karl Popper, „A World of Propensities“, in: Karl Popper, „A World of Propensities“, Thoemmes Press, Bristol, (Vortrag 1988, leicht erweitert neu abgedruckt 1990, repr. 1995)
[] Karl Popper, „Towards an Evolutionary Theory of Knowledge“, in: Karl Popper, „A World of Propensities“, Thoemmes Press, Bristol, (Vortrag 1989, ab gedruckt in 1990, repr. 1995)
[] Karl Popper, „All Life is Problem Solving“, Artikel, ursprünglich ein Vortrag 1991 auf Deutsch, erstmalig publiziert in dem Buch (auf Deutsch) „Alles Leben ist Problemlösen“ (1994), dann in dem Buch (auf Englisch) „All Life is Problem Solving“, 1999, Routledge, Taylor & Francis Group, London – New York
[] A. Sears and J.A. Jacko, Eds., The Human-Computer Interaction Handbook. Fundamentals, Evolving Technologies, and emerging Applications. 2nd edition, 2008.
[] Skaburskis, Andrejs (19 December 2008). “The origin of “wicked problems””. Planning Theory & Practice. 9 (2): 277-280. doi:10.1080/14649350802041654. At the end of Rittel’s presentation, West Churchman responded with that pensive but expressive movement of voice that some may well remember, ‘Hmm, those sound like “wicked problems.”‘
[] Thoppilan, R., et al. LaMDA: Language models for dialog applications. arXiv 2201.08239 (2022).
[] Wurm, Daniel; Zielinski, Oliver; Lübben, Neeske; Jansen, Maike; Ramesohl, Stephan (2021) : Wege in eine ökologische Machine Economy: Wir brauchen eine ‘Grüne Governance der Machine Economy’, um das Zusammenspiel von Internet of Things, Künstlicher Intelligenz und Distributed Ledger Technology ökologisch zu gestalten, Wuppertal Report, No. 22, Wuppertal Institut für Klima, Umwelt, Energie, Wuppertal, https://doi.org/10.48506/opus-7828
[] Aimee van Wynsberghe, Sustainable AI: AI for sustainability and the sustainability of AI, in: AI and Ethics (2021) 1:213–218, see: https://doi.org/10.1007/s43681
[] R. I. Damper (2000), Editorial for the special issue on ‘Emergent Properties of Complex Systems’: Emergence and levels of abstraction. International Journal of Systems Science 31, 7 (2000), 811–818. DOI:https://doi.org/10.1080/002077200406543
[] Gerd Doeben-Henisch (2004), The Planet Earth Simulator Project – A Case Study in Computational Semiotics, IEEE AFRICON 2004, pp.417 – 422
[] Eric Bonabeau (2009), Decisions 2.0: The power of collective intelligence. MIT Sloan Management Review 50, 2 (Winter 2009), 45-52.
[] Jim Giles (2005), Internet encyclopaedias go head to head. Nature 438, 7070 (Dec. 2005), 900–901. DOI:https://doi.org/10.1038/438900a
[] T. Bosse, C. M. Jonker, M. C. Schut, and J. Treur (2006), Collective representational content for shared extended mind. Cognitive Systems Research 7, 2-3 (2006), pp.151-174, DOI:https://doi.org/10.1016/j.cogsys.2005.11.007
[] Romina Cachia, Ramón Compañó, and Olivier Da Costa (2007), Grasping the potential of online social networks for foresight. Technological Forecasting and Social Change 74, 8 (2007), oo.1179-1203. DOI:https://doi.org/10.1016/j.techfore.2007.05.006
[] Tom Gruber (2008), Collective knowledge systems: Where the social web meets the semantic web. Web Semantics: Science, Services and Agents on the World Wide Web 6, 1 (2008), 4–13. DOI:https://doi.org/10.1016/j.websem.2007.11.011
[] Luca Iandoli, Mark Klein, and Giuseppe Zollo (2009), Enabling on-line deliberation and collective decision-making through large-scale argumentation. International Journal of Decision Support System Technology 1, 1 (Jan. 2009), 69–92. DOI:https://doi.org/10.4018/jdsst.2009010105
[] Shuangling Luo, Haoxiang Xia, Taketoshi Yoshida, and Zhongtuo Wang (2009), Toward collective intelligence of online communities: A primitive conceptual model. Journal of Systems Science and Systems Engineering 18, 2 (01 June 2009), 203–221. DOI:https://doi.org/10.1007/s11518-009-5095-0
[] Dawn G. Gregg (2010), Designing for collective intelligence. Communications of the ACM 53, 4 (April 2010), 134–138. DOI:https://doi.org/10.1145/1721654.1721691
[] Rolf Pfeifer, Jan Henrik Sieg, Thierry Bücheler, and Rudolf Marcel Füchslin. 2010. Crowdsourcing, open innovation and collective intelligence in the scientific method: A research agenda and operational framework. (2010). DOI:https://doi.org/10.21256/zhaw-4094
[] Martijn C. Schut. 2010. On model design for simulation of collective intelligence. Information Sciences 180, 1 (2010), 132–155. DOI:https://doi.org/10.1016/j.ins.2009.08.006 Special Issue on Collective Intelligence
[] Dimitrios J. Vergados, Ioanna Lykourentzou, and Epaminondas Kapetanios (2010), A resource allocation framework for collective intelligence system engineering. In Proceedings of the International Conference on Management of Emergent Digital EcoSystems (MEDES’10). ACM, New York, NY, 182–188. DOI:https://doi.org/10.1145/1936254.1936285
[] Anita Williams Woolley, Christopher F. Chabris, Alex Pentland, Nada Hashmi, and Thomas W. Malone (2010), Evidence for a collective intelligence factor in the performance of human groups. Science 330, 6004 (2010), 686–688. DOI:https://doi.org/10.1126/science.1193147
[] Michael A. Woodley and Edward Bell (2011), Is collective intelligence (mostly) the General Factor of Personality? A comment on Woolley, Chabris, Pentland, Hashmi and Malone (2010). Intelligence 39, 2 (2011), 79–81. DOI:https://doi.org/10.1016/j.intell.2011.01.004
[] Joshua Introne, Robert Laubacher, Gary Olson, and Thomas Malone (2011), The climate CoLab: Large scale model-based collaborative planning. In Proceedings of the 2011 International Conference on Collaboration Technologies and Systems (CTS’11). 40–47. DOI:https://doi.org/10.1109/CTS.2011.5928663
[] Miguel de Castro Neto and Ana Espírtio Santo (2012), Emerging collective intelligence business models. In MCIS 2012 Proceedings. Mediterranean Conference on Information Systems. https://aisel.aisnet.org/mcis2012/14
[] Peng Liu, Zhizhong Li (2012), Task complexity: A review and conceptualization framework, International Journal of Industrial Ergonomics 42 (2012), pp. 553 – 568
[] Sean Wise, Robert A. Paton, and Thomas Gegenhuber. (2012), Value co-creation through collective intelligence in the public sector: A review of US and European initiatives. VINE 42, 2 (2012), 251–276. DOI:https://doi.org/10.1108/03055721211227273
[] Antonietta Grasso and Gregorio Convertino (2012), Collective intelligence in organizations: Tools and studies. Computer Supported Cooperative Work (CSCW) 21, 4 (01 Oct 2012), 357–369. DOI:https://doi.org/10.1007/s10606-012-9165-3
[] Sandro Georgi and Reinhard Jung (2012), Collective intelligence model: How to describe collective intelligence. In Advances in Intelligent and Soft Computing. Vol. 113. Springer, 53–64. DOI:https://doi.org/10.1007/978-3-642-25321-8_5
[] H. Santos, L. Ayres, C. Caminha, and V. Furtado (2012), Open government and citizen participation in law enforcement via crowd mapping. IEEE Intelligent Systems 27 (2012), 63–69. DOI:https://doi.org/10.1109/MIS.2012.80
[] Jörg Schatzmann & René Schäfer & Frederik Eichelbaum (2013), Foresight 2.0 – Definition, overview & evaluation, Eur J Futures Res (2013) 1:15 DOI 10.1007/s40309-013-0015-4
[] Sylvia Ann Hewlett, Melinda Marshall, and Laura Sherbin (2013), How diversity can drive innovation. Harvard Business Review 91, 12 (2013), 30–30
[] Tony Diggle (2013), Water: How collective intelligence initiatives can address this challenge. Foresight 15, 5 (2013), 342–353. DOI:https://doi.org/10.1108/FS-05-2012-0032
[] Hélène Landemore and Jon Elster. 2012. Collective Wisdom: Principles and Mechanisms. Cambridge University Press. DOI:https://doi.org/10.1017/CBO9780511846427
[] Jerome C. Glenn (2013), Collective intelligence and an application by the millennium project. World Futures Review 5, 3 (2013), 235–243. DOI:https://doi.org/10.1177/1946756713497331
[] Detlef Schoder, Peter A. Gloor, and Panagiotis Takis Metaxas (2013), Social media and collective intelligence—Ongoing and future research streams. KI – Künstliche Intelligenz 27, 1 (1 Feb. 2013), 9–15. DOI:https://doi.org/10.1007/s13218-012-0228-x
[] V. Singh, G. Singh, and S. Pande (2013), Emergence, self-organization and collective intelligence—Modeling the dynamics of complex collectives in social and organizational settings. In 2013 UKSim 15th International Conference on Computer Modelling and Simulation. 182–189. DOI:https://doi.org/10.1109/UKSim.2013.77
[] A. Kornrumpf and U. Baumöl (2014), A design science approach to collective intelligence systems. In 2014 47th Hawaii International Conference on System Sciences. 361–370. DOI:https://doi.org/10.1109/HICSS.2014.53
[] Michael A. Peters and Richard Heraud. 2015. Toward a political theory of social innovation: Collective intelligence and the co-creation of social goods. 3, 3 (2015), 7–23. https://researchcommons.waikato.ac.nz/handle/10289/9569
[] Juho Salminen. 2015. The Role of Collective Intelligence in Crowdsourcing Innovation. PhD dissertation. Lappeenranta University of Technology
[] Aelita Skarzauskiene and Monika Maciuliene (2015), Modelling the index of collective intelligence in online community projects. In International Conference on Cyber Warfare and Security. Academic Conferences International Limited, 313
[] AYA H. KIMURA and ABBY KINCHY (2016), Citizen Science: Probing the Virtues and Contexts of Participatory Research, Engaging Science, Technology, and Society 2 (2016), 331-361, DOI:10.17351/ests2016.099
[] Philip Tetlow, Dinesh Garg, Leigh Chase, Mark Mattingley-Scott, Nicholas Bronn, Kugendran Naidoo†, Emil Reinert (2022), Towards a Semantic Information Theory (Introducing Quantum Corollas), arXiv:2201.05478v1 [cs.IT] 14 Jan 2022, 28 pages
[] Melanie Mitchell, What Does It Mean to Align AI With Human Values?, quanta magazin, Quantized Columns, 19.Devember 2022, https://www.quantamagazine.org/what-does-it-mean-to-align-ai-with-human-values-20221213#
Comment by Gerd Doeben-Henisch:
[] Nick Bostrom. Superintelligence. Paths, Dangers, Strategies. Oxford University Press, Oxford (UK), 1 edition, 2014.
[] Scott Aaronson, Reform AI Alignment, Update: 22.November 2022, https://scottaaronson.blog/?p=6821
[] Andrew Y. Ng, Stuart J. Russell, Algorithms for Inverse Reinforcement Learning, ICML 2000: Proceedings of the Seventeenth International Conference on Machine LearningJune 2000 Pages 663–670
[] Pat Langley (ed.), ICML ’00: Proceedings of the Seventeenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., 340 Pine Street, Sixth Floor, San Francisco, CA, United States, Conference 29 June 2000- 2 July 2000, 29.June 2000
Abstract: Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations Daniel S. Brown * 1 Wonjoon Goo * 1 Prabhat Nagarajan 2 Scott Niekum 1 You can read in the abstract: “A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator. This is because IRL typically seeks a reward function that makes the demonstrator appear near-optimal, rather than inferring the underlying intentions of the demonstrator that may have been poorly executed in practice. In this paper, we introduce a novel reward-learning-from-observation algorithm, Trajectory-ranked Reward EXtrapolation (T-REX), that extrapolates beyond a set of (ap- proximately) ranked demonstrations in order to infer high-quality reward functions from a set of potentially poor demonstrations. When combined with deep reinforcement learning, T-REX outperforms state-of-the-art imitation learning and IRL methods on multiple Atari and MuJoCo bench- mark tasks and achieves performance that is often more than twice the performance of the best demonstration. We also demonstrate that T-REX is robust to ranking noise and can accurately extrapolate intention by simply watching a learner noisily improve at a task over time.”
In the abstract you can read: “For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than one percent of our agent’s interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any that have been previously learned from human feedback.
In the abstract you can read: “Conceptual abstraction and analogy-making are key abilities underlying humans’ abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite of a long history of research on constructing AI systems with these abilities, no current AI system is anywhere close to a capability of forming humanlike abstractions or analogies. This paper reviews the advantages and limitations of several approaches toward this goal, including symbolic methods, deep learning, and probabilistic program induction. The paper concludes with several proposals for designing challenge tasks and evaluation measures in order to make quantifiable and generalizable progress
In the abstract you can read: “Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.”
[] Stuart Russell, (2019), Human Compatible: AI and the Problem of Control, Penguin books, Allen Lane; 1. Edition (8. Oktober 2019)
In the preface you can read: “This book is about the past , present , and future of our attempt to understand and create intelligence . This matters , not because AI is rapidly becoming a pervasive aspect of the present but because it is the dominant technology of the future . The world’s great powers are waking up to this fact , and the world’s largest corporations have known it for some time . We cannot predict exactly how the technology will develop or on what timeline . Nevertheless , we must plan for the possibility that machines will far exceed the human capacity for decision making in the real world . What then ? Everything civilization has to offer is the product of our intelligence ; gaining access to considerably greater intelligence would be the biggest event in human history . The purpose of the book is to explain why it might be the last event in human history and how to make sure that it is not .”
[] David Adkins, Bilal Alsallakh, Adeel Cheema, Narine Kokhlikyan, Emily McReynolds, Pushkar Mishra, Chavez Procope, Jeremy Sawruk, Erin Wang, Polina Zvyagina, (2022), Method Cards for Prescriptive Machine-Learning Transparency, 2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN), CAIN’22, May 16–24, 2022, Pittsburgh, PA, USA, pp. 90 – 100, Association for Computing Machinery, ACM ISBN 978-1-4503-9275-4/22/05, New York, NY, USA, https://doi.org/10.1145/3522664.3528600
In the abstract you can read: “Specialized documentation techniques have been developed to communicate key facts about machine-learning (ML) systems and the datasets and models they rely on. Techniques such as Datasheets, AI FactSheets, and Model Cards have taken a mainly descriptive approach, providing various details about the system components. While the above information is essential for product developers and external experts to assess whether the ML system meets their requirements, other stakeholders might find it less actionable. In particular, ML engineers need guidance on how to mitigate po- tential shortcomings in order to fix bugs or improve the system’s performance. We propose a documentation artifact that aims to provide such guidance in a prescriptive way. Our proposal, called Method Cards, aims to increase the transparency and reproducibil- ity of ML systems by allowing stakeholders to reproduce the models, understand the rationale behind their designs, and introduce adap- tations in an informed way. We showcase our proposal with an example in small object detection, and demonstrate how Method Cards can communicate key considerations that help increase the transparency and reproducibility of the detection model. We fur- ther highlight avenues for improving the user experience of ML engineers based on Method Cards.”
[] John H. Miller, (2022), Ex Machina: Coevolving Machines and the Origins of the Social Universe, The SFI Press Scholars Series, 410 pages Paperback ISBN: 978-1947864429 , DOI: 10.37911/9781947864429
In the announcement of the book you can read: “If we could rewind the tape of the Earth’s deep history back to the beginning and start the world anew—would social behavior arise yet again? While the study of origins is foundational to many scientific fields, such as physics and biology, it has rarely been pursued in the social sciences. Yet knowledge of something’s origins often gives us new insights into the present. In Ex Machina, John H. Miller introduces a methodology for exploring systems of adaptive, interacting, choice-making agents, and uses this approach to identify conditions sufficient for the emergence of social behavior. Miller combines ideas from biology, computation, game theory, and the social sciences to evolve a set of interacting automata from asocial to social behavior. Readers will learn how systems of simple adaptive agents—seemingly locked into an asocial morass—can be rapidly transformed into a bountiful social world driven only by a series of small evolutionary changes. Such unexpected revolutions by evolution may provide an important clue to the emergence of social life.”
In the abstract you can read: “Analyzing the spatial and temporal properties of information flow with a multi-century perspective could illuminate the sustainability of human resource-use strategies. This paper uses historical and archaeological datasets to assess how spatial, temporal, cognitive, and cultural limitations impact the generation and flow of information about ecosystems within past societies, and thus lead to tradeoffs in sustainable practices. While it is well understood that conflicting priorities can inhibit successful outcomes, case studies from Eastern Polynesia, the North Atlantic, and the American Southwest suggest that imperfect information can also be a major impediment to sustainability. We formally develop a conceptual model of Environmental Information Flow and Perception (EnIFPe) to examine the scale of information flow to a society and the quality of the information needed to promote sustainable coupled natural-human systems. In our case studies, we assess key aspects of information flow by focusing on food web relationships and nutrient flows in socio-ecological systems, as well as the life cycles, population dynamics, and seasonal rhythms of organisms, the patterns and timing of species’ migration, and the trajectories of human-induced environmental change. We argue that the spatial and temporal dimensions of human environments shape society’s ability to wield information, while acknowledging that varied cultural factors also focus a society’s ability to act on such information. Our analyses demonstrate the analytical importance of completed experiments from the past, and their utility for contemporary debates concerning managing imperfect information and addressing conflicting priorities in modern environmental management and resource use.”
Integrating Engineering and the Human Factor (info@uffmm.org) eJournal uffmm.org ISSN 2567-6458
