A ‘text’ shall be written that speaks about the world, including all living beings, with ‘humans’ as the authors in the first instance. So far, we know of no cases where animals or plants write texts themselves: their view of life. We only know of humans who write from ‘their human perspective’ about life, animals, and plants. Much can be criticized about this approach. Upon further reflection, one might even realize that ‘humans writing about other humans and themselves’ is not so trivial either. Even humans writing ‘about themselves’ is prone to errors, can go completely ‘awry,’ can be entirely ‘wrong,’ which raises the question of what is ‘true’ or ‘false.’ Therefore, we should spend some thoughts on how we humans can talk about the world and ourselves in a way that gives us a chance not just to ‘fantasize,’ but to grasp something that is ‘real,’ something that describes what truly characterizes us as humans, as living beings, as inhabitants of this planet… but then the question pops up again, what is ‘real’? Are we caught in a cycle of questions with answers, where the answers themselves are again questions upon closer inspection?
First Steps
Life on Planet Earth
At the start of writing, we assume that there is a ‘Planet Earth’ and on this planet there is something we call ‘life,’ and we humans—belonging to the species Homo sapiens—are part of it.
Language
We also assume that we humans have the ability to communicate with each other using sounds. These sounds, which we use for communication, we call here ‘speech sounds’ to indicate that the totality of sounds for communication forms a ‘system’ which we ultimately call ‘language.’
Meaning
Since we humans on this planet can use completely different sounds for the ‘same objects’ in the same situation, it suggests that the ‘meaning’ of speech sounds is not firmly tied to the speech sounds themselves, but somehow has to do with what happens ‘in our minds.’ Unfortunately, we cannot look ‘into our minds.’ It seems a lot happens there, but this happening in the mind is ‘invisible.’ Nevertheless, in ‘everyday life,’ we experience that we can ‘agree’ with others whether it is currently ‘raining’ or if it smells ‘bad’ or if there is a trash bin on the sidewalk blocking the way, etc. So somehow, the ‘happenings in the mind’ seem to have certain ‘agreements’ among different people, so that not only I see something specific, but the other person does too, and we can even use the same speech sounds for it. And since a program like chatGPT can translate my German speech sounds, e.g., into English speech sounds, I can see that another person who does not speak German, instead of my word ‘Mülltonne,’ uses the word ‘trash bin’ and then nods in agreement: ‘Yes, there is a trash bin.’ Would that be a case for a ‘true statement’?
Changes and Memories
Since we experience daily how everyday life constantly ‘changes,’ we know that something that just found agreement may no longer find it the next moment because the trash bin is no longer there. We can only notice these changes because we have something called ‘memory’: we can remember that just now at a specific place there was a trash bin, but now it’s not. Or is this memory just an illusion? Can I trust my memory? If now everyone else says there was no trash bin, but I remember there was, what does that mean?
Concrete Body
Yes, and then my body: time and again I need to drink something, eat something, I’m not arbitrarily fast, I need some space, … my body is something very concrete, with all sorts of ‘sensations,’ ‘needs,’ a specific ‘shape,’ … and it changes over time: it grows, it ages, it can become sick, … is it like a ‘machine’?
Galaxies of Cells
Today we know that our human body resembles less a ‘machine’ and more a ‘galaxy of cells.’ Our body has about 37 trillion (10¹²) body cells with another 100 trillion cells in the gut that are vital for our digestive system, and these cells together form the ‘body system.’ The truly incomprehensible thing is that these approximately 140 trillion cells are each completely autonomous living beings, with everything needed for life. And if you know how difficult it is for us as humans to maintain cooperation among just five people over a long period, then you can at least begin to appreciate what it means that 140 trillion beings manage to communicate and coordinate actions every second—over many years, even decades—so that the masterpiece ‘human body’ exists and functions.
Origin as a Question
And since there is no ‘commander’ who constantly tells all the cells what to do, this ‘miracle of the human system’ expands further into the dimension of where the concept comes from that enables this ‘super-galaxy of cells’ to be as they are. How does this work? How did it arise?
Looking Behind Phenomena
In the further course, it will be important to gradually penetrate the ‘surface of everyday phenomena’ starting from everyday life, to make visible those structures that are ‘behind the phenomena,’ those structures that hold everything together and at the same time constantly move, change everything.
Fundamental Dimension of Time
All this implies the phenomenon ‘time’ as a basic category of all reality. Without time, there is also no ‘truth’…
[1] Specialists in brain research will of course raise their hand right away, and will want to say that they can indeed ‘look into the head’ by now, but let’s wait and see what this ‘looking into the head’ entails.
[2] If we assume for the number of stars in our home galaxy, the Milky Way, with an estimated 100 – 400 billion stars that there are 200 billion, then our body system would correspond to the scope of 700 galaxies in the format of the Milky Way, one cell for one star.
[3] Various disciplines of natural sciences, especially certainly evolutionary biology, have illuminated many aspects of this mega-wonder partially over the last approx. 150 years. One can marvel at the physical view of our universe, but compared to the super-galaxies of life on Planet Earth, the physical universe seems downright ‘boring’… Don’t worry: ultimately, both are interconnected: one explains the other…”
Telling Stories
Fragments of Everyday Life—Without Context
We constantly talk about something: the food, the weather, the traffic, shopping prices, daily news, politics, the boss, colleagues, sports events, music, … mostly, these are ‘fragments’ from the larger whole that we call ‘everyday life’. People in one of the many crisis regions on this planet, especially those in natural disasters or even in war…, live concretely in a completely different world, a world of survival and death.
These fragments in the midst of life are concrete, concern us, but they do not tell a story by themselves about where they come from (bombs, rain, heat,…), why they occur, how they are connected with other fragments. The rain that pours down is a single event at a specific place at a specific time. The bridge that must be closed because it is too old does not reveal from itself why this particular bridge, why now, why couldn’t this be ‘foreseen’? The people who are ‘too many’ in a country or also ‘too few’: Why is that? Could this have been foreseen? What can we do? What should we do?
The stream of individual events hits us, more or less powerfully, perhaps even simply as ‘noise’: we are so accustomed to it that we no longer even perceive certain events. But these events as such do not tell a ‘story about themselves’; they just happen, seemingly irresistibly; some say ‘It’s fate’.
Need for Meaning
It is notable that we humans still try to give the whole a ‘meaning’, to seek an ‘explanation’ for why things are the way they are. And everyday life shows that we have a lot of ‘imagination’ concerning possible ‘connections’ or ’causes’. Looking back into the past, we often smile at the various attempts at explanation by our ancestors: as long as nothing was known about the details of our bodies and about life in general, any story was possible. In our time, with science established for about 150 years, there are still many millions of people (possibly billions?) who know nothing about science and are willing to believe almost any story just because another person tells this story convincingly.
Liberation from the Moment through Words
Because of this ability, with the ‘power of imagination’ to pack things one experiences into a ‘story’ that suggests ‘possible connections’, through which events gain a ‘conceptual sense’, a person can try to ‘liberate’ themselves from the apparent ‘absoluteness of the moment’ in a certain way: an event that can be placed into a ‘context’ loses its ‘absoluteness’. Just by this kind of narrative, the experiencing person gains a bit of ‘power’: in narrating a connection, the narrator can make the experience ‘a matter’ over which they can ‘dispose’ as they see fit. This ‘power through the word’ can alleviate the ‘fear’ that an event can trigger. This has permeated the history of humanity from the beginning, as far as archaeological evidence allows.
Perhaps it is not wrong to first identify humans not as ‘hunters and gatherers’ or as ‘farmers’ but as ‘those who tell stories’.
[1] Such a magic word in Greek philosophy was the concept of ‘breath’ (Greek “pneuma”). The breath not only characterized the individually living but was also generalized to a life principle of everything that connected both body, soul, and spirit as well as permeated the entire universe. In the light of today’s knowledge, this ‘explanation’ could no longer be told, but about 2300 years ago, this belief was a certain ‘intellectual standard’ among all intellectuals, the prevailing ‘worldview’; it was ‘believed’. Anyone who thought differently was outside this ‘language game’.
Organization of an Order
Thinking Creates Relationships
As soon as one can ‘name’ individual events, things, processes, properties of things, and more through ‘language’, it is evident that humans have the ability to not only ‘name’ using language but to embed the ‘named’ through ‘arrangement of words in linguistic expression’ into ‘conceived relationships’, thereby connecting the individually named items not in isolation but in thought with others. This fundamental human ability to ‘think relationships in one’s mind’, which cannot be ‘seen’ but can indeed be ‘thought’ [1], is of course not limited to single events or a single relationship. Ultimately, we humans can make ‘everything’ a subject, and we can ‘think’ any ‘possible relationship’ in our minds; there are no fundamental restrictions here.
Stories as a Natural Force
Not only history is full of examples, but also our present day. Today, despite the incredible successes of modern science, almost universally, the wildest stories with ‘purely thought relationships’ are being told and immediately believed through all channels worldwide, which should give us pause. Our fundamental characteristic, that we can tell stories to break the absoluteness of the moment, obviously has the character of a ‘natural force’, deeply rooted within us, that we cannot ‘eradicate’; we might be able to ‘tame’ it, perhaps ‘cultivate’ it, but we cannot stop it. It is an ‘elemental characteristic’ of our thinking, that is: of our brain in the body.
Thought and Verified
The experience that we, the storytellers, can name events and arrange them into relationships—and ultimately without limit—may indeed lead to chaos if the narrated network of relationships is ultimately ‘purely thought’, without any real reference to the ‘real world around us’, but it is also our greatest asset. With it, humans can not only fundamentally free themselves from the apparent absoluteness of the present, but we can also create starting points with the telling of stories, ‘initially just thought relationships’, which we can then concretely ‘verify’ in our everyday lives.
A System of Order
When someone randomly sees another person who looks very different from what they are used to, all sorts of ‘assumptions’ automatically form in each person about what kind of person this might be. If one stops at these assumptions, these wild guesses can ‘populate the head’ and the ‘world in the head’ gets populated with ‘potentially evil people’; eventually, they might simply become ‘evil’. However, if one makes contact with the other, they might find that the person is actually nice, interesting, funny, or the like. The ‘assumptions in the head’ then transform into ‘concrete experiences’ that differ from what was initially thought. ‘Assumptions’ combined with ‘verification’ can thus lead to the formation of ‘reality-near ideas of relationships’. This gives a person the chance to transform their ‘spontaneous network of thought relationships’, which can be wrong—and usually are—into a ‘verified network of relationships’. Since ultimately the thought relationships as a network provide us with a ‘system of order’ in which everyday things are embedded, it appears desirable to work with as many ‘verified thought relationships’ as possible.
[1] The breath of the person opposite me, which for the Greeks connected my counterpart with the life force of the universe, which in turn is also connected with the spirit and the soul…
Hypotheses and Science
Challenge: Methodically Organized Guessing
The ability to think of possible relationships, and to articulate them through language, is innate [1], but the ‘use’ of this ability in everyday life, for example, to match thought relationships with the reality of everyday life, this ‘matching’/’verifying’ is not innate. We can do it, but we don’t have to. Therefore, it is interesting to realize that since the first appearance of Homo sapiens on this planet [2], 99.95% of the time has passed until the establishment of organized modern science about 150 years ago. This can be seen as an indication that the transition from ‘free guessing’ to ‘methodically organized systematic guessing’ must have been anything but easy. And if today still a large part of people—despite schooling and even higher education—[3] tend to lean towards ‘free guessing’ and struggle with organized verification, then there seems to be a not easy threshold that a person must overcome—and must continually overcome—to transition from ‘free’ to ‘methodically organized’ guessing.[4]
Starting Point for Science
The transition from everyday thinking to ‘scientific thinking’ is fluid. The generation of ‘thought relationships’ in conjunction with language, due to our ability of creativity/imagination, is ultimately also the starting point of science. While in everyday thinking we tend to spontaneously and pragmatically ‘verify’ ‘spontaneously thought relationships’, ‘science’ attempts to organize such verifications ‘systematically’ to then accept such ‘positively verified guesses’ as ’empirically verified guesses’ until proven otherwise as ‘conditionally true’. Instead of ‘guesses’, science likes to speak of ‘hypotheses’ or ‘working hypotheses’, but they remain ‘guesses’ through the power of our thinking and through the power of our imagination.[5]
[1] This means that the genetic information underlying the development of our bodies is designed so that our body with its brain is constructed during the growth phase in such a way that we have precisely this ability to ‘think of relationships’. It is interesting again to ask how it is possible that from a single cell about 13 trillion body cells (the approximately 100 trillion bacteria in the gut come ‘from outside’) can develop in such a way that they create the ‘impression of a human’ that we know.
[2] According to current knowledge, about 300,000 years ago in East Africa and North Africa, from where Homo sapiens then explored and conquered the entire world (there were still remnants of other human forms that had been there longer).
[3] I am not aware of representative empirical studies on how many people in a population tend to do this.
[4] Considering that we humans as the life form Homo sapiens only appeared on this planet after about 3.8 billion years, the 300,000 years of Homo sapiens make up roughly 0.008% of the total time since there has been life on planet Earth. Thus, not only are we as Homo sapiens a very late ‘product’ of the life process, but the ability to ‘systematically verify hypotheses’ also appears ‘very late’ in our Homo sapiens life process. Viewed across the entire life span, this ability seems to be extremely valuable, which is indeed true considering the incredible insights we as Homo sapiens have been able to gain with this form of thinking. The question is how we deal with this knowledge. This behavior of using systematically verified knowledge is not innate too.
[5] The ability of ‘imagination’ is not the opposite of ‘knowledge’, but is something completely different. ‘Imagination’ is a trait that ‘shows’ itself the moment we start to think, perhaps even in the fact ‘that’ we think at all. Since we can in principle think about ‘everything’ that is ‘accessible’ to our thinking, imagination is a factor that helps to ‘select’ what we think. In this respect, imagination is pre-posed to thinking.
(First: June 9, 2023 – Last change: June 10, 2023)
Comment: This post is a translation from a German text in my blog ‘cognitiveagent.org’ with the aid of the deepL software
CONTEXT
The current phase of my thinking continues to revolve around the question how the various states of knowledge relate to each other: the many individual scientific disciplines drift side by side; philosophy continues to claim supremacy, but cannot really locate itself convincingly; and everyday thinking continues to run its course unperturbed with the conviction that ‘everything is clear’, that you just have to look at it ‘as it is’. Then the different ‘religious views’ come around the corner with a very high demand and a simultaneous prohibition not to look too closely. … and much more.
INTENTION
In the following text three fundamental ways of looking at our present world are outlined and at the same time they are put in relation to each other. Some hitherto unanswered questions can possibly be answered better, but many new questions arise as well. When ‘old patterns of thinking’ are suspended, many (most? all?) of the hitherto familiar patterns of thinking have to be readjusted. All of a sudden they are simply ‘wrong’ or strongly ‘in need of repair’.
Unfortunately it is only a ‘sketch’.[1]
THOUGHTS IN EVERYDAY
FIG. 1: In everyday thinking, every human being (a ‘homo sapiens’ (HS)) assumes that what he knows of a ‘real world’ is what he ‘perceives’. That there is this real world with its properties, he is – more or less – ‘aware’ of, there is no need to discuss about it specially. That, what ‘is, is’.
… much could be said …
PHILOSOPHICAL THINKING
FIG. 2: Philosophical thinking starts where one notices that the ‘real world’ is not perceived by all people in ‘the same way’ and even less ‘imagined’ in the same way. Some people have ‘their ideas’ about the real world that are strikingly ‘different’ from other people’s ideas, and yet they insist that the world is exactly as they imagine it. From this observation in everyday life, many new questions can arise. The answers to these questions are as manifold as there were and are people who gave or still give themselves to these philosophical questions.
… famous examples: Plato’s allegory of the cave suggests that the contents of our consciousness are perhaps not ‘the things themselves’ but only the ‘shadows’ of what is ultimately ‘true’ … Descartes‘ famous ‘cogito ergo sum’ brings into play the aspect that the contents of consciousness also say something about himself who ‘consciously perceives’ such contents …. the ‘existence of the contents’ presupposes his ‘existence as thinker’, without which the existence of the contents would not be possible at all …what does this tell us? … Kant’s famous ‘thing in itself’ (‘Ding an sich’) can be referred to the insight that the concrete, fleeting perceptions can never directly show the ‘world as such’ in its ‘generality’. This lies ‘somewhere behind’, hard to grasp, actually not graspable at all? ….
… many things could be said …
EMPIRICAL-THEORETICAL THINKING
FIG. 3: The concept of an ’empirical theory’ developed very late in the documented history of man on this planet. On the one hand philosophically inspired, on the other hand independent of the widespread forms of philosophy, but very strongly influenced by logical and mathematical thinking, the new ’empirical theoretical’ thinking settled exactly at this breaking point between ‘everyday thinking’ and ‘theological’ as well as ‘strongly metaphysical philosophical thinking’. The fact that people could make statements about the world ‘with the chest tone of conviction’, although it was not possible to show ‘common experiences of the real world’, which ‘corresponded’ with the expressed statements, inspired individual people to investigate the ‘experiential (empirical) world’ in such a way that everyone else could have the ‘same experiences’ with ‘the same procedure’. These ‘transparent procedures’ were ‘repeatable’ and such procedures became what was later called ’empirical experiment’ or then, one step further, ‘measurement’. In ‘measuring’ one compares the ‘result’ of a certain experimental procedure with a ‘previously defined standard object’ (‘kilogram’, ‘meter’, …).
This procedure led to the fact that – at least the experimenters – ‘learned’ that our knowledge about the ‘real world’ breaks down into two components: there is the ‘general knowledge’ what our language can articulate, with terms that do not automatically have to have something to do with the ‘experiential world’, and such terms that can be associated with experimental experiences, and in such a way that other people, if they engage in the experimental procedure, can also repeat and thereby confirm these experiences. A rough distinction between these two kinds of linguistic expressions might be ‘fictive’ expressions with unexplained claims to experience, and ’empirical’ expressions with confirmed claims to experience.
Since the beginning of the new empirical-theoretical way of thinking in the 17th century, it took at least 300 years until the concept of an ’empirical theory’ was consolidated to such an extent that it became a defining paradigm in many areas of science. However, many methodological questions remained controversial or even ‘unsolved’.
DATA and THEORY
For many centuries, the ‘misuse of everyday language’ for enabling ’empirically unverifiable statements’ was directly chalked up to this everyday language and the whole everyday language was discredited as ‘source of untruths’. A liberation from this ‘ monster of everyday language’ was increasingly sought in formal artificial languages or then in modern axiomatized mathematics, which had entered into a close alliance with modern formal logic (from the end of the 19th century). The expression systems of modern formal logic or then of modern formal mathematics had as such (almost) no ‘intrinsic meaning’. They had to be introduced explicitly on a case-by-case basis. A ‘formal mathematical theory’ could be formulated in such a way that it allowed ‘logical inferences’ even without ‘explicit assignment’ of an ‘external meaning’, which allowed certain formal expressions to be called ‘formally true’ or ‘formally false’.
This seemed very ‘reassuring’ at first sight: mathematics as such is not a place of ‘false’ or ‘foisted’ truths.
The intensive use of formal theories in connection with experience-based experiments, however, then gradually made clear that a single measured value as such does not actually have any ‘meaning’ either: what is it supposed to ‘mean’ that at a certain ‘time’ at a certain ‘place’ one establishes an ‘experienceable state’ with certain ‘properties’, ideally comparable to a previously agreed ‘standard object’? ‘Expansions’ of bodies can change, ‘weight’ and ‘temperature’ as well. Everything can change in the world of experience, fast, slow, … so what can a single isolated measured value say?
It dawned to some – not only to the experience-based researchers, but also to some philosophers – that single measured values only get a ‘meaning’, a possible ‘sense’, if one can at least establish ‘relations’ between single measured values: Relations ‘in time’ (before – after), relations at/in place (higher – lower, next to each other, …), ‘interrelated quantities’ (objects – areas, …), and that furthermore the different ‘relations’ themselves again need a ‘conceptual context’ (single – quantity, interactions, causal – non-causal, …).
Finally, it became clear that single measured values needed ‘class terms’, so that they could be classified somehow: abstract terms like ‘tree’, ‘plant’, ‘cloud’, ‘river’, ‘fish’ etc. became ‘collection points’, where one could deliver ‘single observations’. With this, hundreds and hundreds of single values could then be used, for example, to characterize the abstract term ‘tree’ or ‘plant’ etc.
This distinction into ‘single, concrete’ and ‘abstract, general’ turns out to be fundamental. It also made clear that the classification of the world by means of such abstract terms is ultimately ‘arbitrary’: both ‘which terms’ one chooses is arbitrary, and the assignment of individual experiential data to abstract terms is not unambiguously settled in advance. The process of assigning individual experiential data to particular terms within a ‘process in time’ is itself strongly ‘hypothetical’ and itself in turn part of other ‘relations’ which can provide additional ‘criteria’ as to whether date X is more likely to belong to term A or more likely to belong to term B (biology is full of such classification problems).
Furthermore, it became apparent that mathematics, which comes across as so ‘innocent’, can by no means be regarded as ‘innocent’ on closer examination. The broad discussion of philosophy of science in the 20th century brought up many ‘artifacts’ which can at least easily ‘corrupt’ the description of a dynamic world of experience.
Thus it belongs to formal mathematical theories that they can operate with so-called ‘all- or particular statements’. Mathematically it is important that I can talk about ‘all’ elements of a domain/set. Otherwise talking becomes meaningless. If I now choose a formal mathematical system as conceptual framework for a theory which describes ’empirical facts’ in such a way that inferences become possible which are ‘true’ in the sense of the theory and thus become ‘predictions’ which assert that a certain fact will occur either ‘absolutely’ or with a certain probability X greater than 50%, then two different worlds unite: the fragmentary individual statements about the world of experience become embedded in ‘all-statements’ which in principle say more than empirical data can provide.
At this point it becomes visible that mathematics, which appears to be so ‘neutral’, does exactly the same job as ‘everyday language’ with its ‘abstract concepts’: the abstract concepts of everyday language always go beyond the individual case (otherwise we could not say anything at all in the end), but just by this they allow considerations and planning, as we appreciate them so much in mathematical theories.
Empirical theories in the format of formal mathematical theories have the further problem that they as such have (almost) no meanings of their own. If one wants to relate the formal expressions to the world of experience, then one has to explicitly ‘construct a meaning’ (with the help of everyday language!) for each abstract concept of the formal theory (or also for each formal relation or also for each formal operator) by establishing a ‘mapping’/an ‘assignment’ between the abstract constructs and certain provable facts of experience. What may sound so simple here at first sight has turned out to be an almost unsolvable problem in the course of the last 100 years. Now it does not follow that one should not do it at all; but it does draw attention to the fact that the choice of a formal mathematical theory need not automatically be a good solution.
… many things could still be said …
INFERENCE and TRUTH
A formal mathematical theory can derive certain statements as formally ‘true’ or ‘false’ from certain ‘assumptions’. This is possible because there are two basic assumptions: (i) All formal expressions have an ‘abstract truth value’ as ‘abstractly true’ or just as ‘abstractly not true’. Furthermore, there is a so-called ‘formal notion of inference’ which determines whether and how one can ‘infer’ other formal expressions from a given ‘set of formal expressions’ with agreed abstract truth values and a well-defined ‘form’. This ‘derivation’ consists of ‘operations over the signs of the formal expressions’. The formal expressions are here ‘objects’ of the notion of inference, which is located on a ‘level higher’, on a ‘meta-level 1’. The inference term is insofar a ‘formal theory’ of its own, which speaks about certain ‘objects of a deeper level’ in the same way as the abstract terms of a theory (or of everyday language) speak about concrete facts of experience. The interaction of the notion of inference (at meta-level 1) and the formal expressions as objects presupposes its own ‘interpretive relation’ (ultimately a kind of ‘mapping’), which in turn is located at yet another level – meta-level 2. This interpretive relation uses both the formal expressions (with their truth values!) and the inference term as ‘objects’ to install an interpretive relation between them. Normally, this meta-level 2 is handled by the everyday language, and the implicit interpretive relation is located ‘in the minds of mathematicians (actually, in the minds of logicians)’, who assume that their ‘practice of inference’ provides enough experiential data to ‘understand’ the ‘content of the meaning relation’.
It had been Kurt Gödel [2], who in 1930/31 tried to formalize the ‘intuitive procedure’ of meta-proofs itself (by means of the famous Gödelization) and thus made the meta-level 3 again a new ‘object’, which can be discussed explicitly. Following Gödel’s proof, there were further attempts to formulate this meta-level 3 again in a different ways or even to formalize a meta-level 4. But these approaches remained so far without clear philosophical result.
It seems to be clear only that the ability of the human brain to open again and again new meta-levels, in order to analyze and discuss with it previously formulated facts, is in principle unlimited (only limited by the finiteness of the brain, its energy supply, the time, and similar material factors).
An interesting special question is whether the formal inference concept of formal mathematics applied to experience facts of a dynamic empirical world is appropriate to the specific ‘world dynamics’ at all? For the area of the ‘apparently material structures’ of the universe, modern physics has located multiple phenomena which simply elude classical concepts. A ‘matter’, which is at the same time ‘energy’, tends to be no longer classically describable, and quantum physics is – despite all ‘modernity’ – in the end still a ‘classical thinking’ within the framework of a formal mathematics, which does not possess many properties from the approach, which, however, belong to the experienceable world.
This limitation of a formal-mathematical physical thinking shows up especially blatantly at the example of those phenomena which we call ‘life’. The experience-based phenomena that we associate with ‘living (= biological) systems’ are, at first sight, completely material structures, however, they have dynamic properties that say more about the ‘energy’ that gives rise to them than about the materiality by means of which they are realized. In this respect, implicit energy is the real ‘information content’ of living systems, which are ‘radically free’ systems in their basic structure, since energy appears as ‘unbounded’. The unmistakable tendency of living systems ‘out of themselves’ to always ‘enable more complexity’ and to integrate contradicts all known physical principles. ‘Entropy’ is often used as an argument to relativize this form of ‘biological self-dynamics’ with reference to a simple ‘upper bound’ as ‘limitation’, but this reference does not completely nullify the original phenomenon of the ‘living’.
It becomes especially exciting if one dares to ask the question of ‘truth’ at this point. If one locates the meaning of the term ‘truth’ first of all in the situation in which a biological system (here the human being) can establish a certain ‘correspondence’ between its abstract concepts and such concrete knowledge structures within its thinking, which can be related to properties of an experiential world through a process of interaction, not only as a single individual but together with other individuals, then any abstract system of expression (called ‘language’) has a ‘true relation to reality’ only to the extent that there are biological systems that can establish such relations. And these references further depend on the structure of perception and the structure of thought of these systems; these in turn depend on the nature of bodies as the context of brains, and bodies in turn depend on both the material structure and dynamics of the environment and the everyday social processes that largely determine what a member of a society can experience, learn, work, plan, and do. Whatever an individual can or could do, society either amplifies or ‘freezes’ the individual’s potential. ‘Truth’ exists under these conditions as a ‘free-moving parameter’ that is significantly affected by the particular process environment. Talk of ‘cultural diversity’ can be a dangerous ‘trivialization’ of massive suppression of ‘alternative processes of learning and action’ that are ‘withdrawn’ from a society because it ‘locks itself in’. Ignorance tends not to be a good advisor. However, knowledge as such does not guarantee ‘right’ action either. The ‘process of freedom’ on planet Earth is a ‘galactic experiment’, the seriousness and extent of which is hardly seen so far.
COMMENTS
[1] References are omitted here. Many hundreds of texts would have to be mentioned. No sketch can do that.
[2] See for the ‘incompleteness theorems’ of Kurt Gödel (1930, published 1931): https://en.wikipedia.org/wiki/Kurt_G%C3%B6del#Incompleteness_theorems
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.
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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
