This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Start: July 14, 2024, Last change: July 14, 2024)

Starting Point

We are still here on this planet. However, the fact that our ‘human system’ largely programs itself without our ‘voluntary participation’ can become our downfall within the framework of a democracy and in the age of the internet; not necessarily, but highly likely…

Self-programming

Unlike today’s machines, all living beings — including humans — are designed to ‘self-program’: whatever we perceive of ourselves, others, and the environment, is automatically transported into our interior and there it is also largely automatically structured, arranged, evaluated, and much more. No one can resist it. One can only control this ‘self-programming’ by shaping one’s environment in such a way that certain perceptions are less likely to occur, while others occur more. Educational processes assume this capacity for self-programming and they also decide what should happen during the education.

Dictatorship of the ‘Is There’

What has arrived within us is there for the time being. It forms our primary reality. When we want to act, we first rely on what is there. What is there is somehow ‘true’ for us, shaping our further perception and understanding. Something ‘around us’ that is ‘different’ is indeed ‘different’ and ‘does not fit’ with our inner truth.

Which points of comparison?

Suppose the majority of what is ‘inside us’, what we initially assume to be ‘true’, were ‘false’, ‘inappropriate’, ‘inaccurate’, etc., in the real world outside, we would have little chance of recognizing our own ‘untruth’ as long as most people around us share the same ‘untruth’.[1] Recognizing ‘untruth’ presupposes that one somehow has ‘examples of truth’ that are suitable for being ‘compared’ with the prevailing untruth. However, the presence of examples of truth does not guarantee the recognition of untruth, but only increases the likelihood that it might happen.[2]

[1] Throughout the known history of humanity, we can observe how certain ‘untruths’ were able to dominate entire peoples, not only in autocratic systems.

[2] Systems with state-promoted untruth can be identified, among other things, by the suppression of diversity, examples of truth, and the allowance of only certain forms of opinions.

Modern Untruths

Unlike autocratic systems, democratic systems officially have ‘freedom of speech’, which allows for great diversity. In democratic systems of the Democracy 1.0 format, it is assumed that this guaranteed freedom is not abused.[1]

With the advent of modern media, especially media in conjunction with the internet, it is possible to make money on a large scale with the distribution of media. The temptation is near to spread media over the internet in such a way that one can earn the maximum amount of money. A popular method is ‘advertising’: the longer and more often a user stays in front of content, the more advertising revenue flows. The temptation is great enough to offer the user only what automatically arouses his ‘automatic interest’. The fact that ‘automatic interest’ is a very strong motive and specifically correlates with content that does not require much thought is confirmed daily. It is now known and increasingly described that large parts of a population can be ‘specially programmed’ in this way.

In the ‘struggle of power-driven systems’, this possibility of external programming of people using the internet is massively exploited in the so-called ‘hybrid warfare’. While autocratic systems are ‘massively closed’, the modern democracies in the 1.0 format are almost an Eldorado for the application of hybrid warfare methods. Similar to the money-obsessed media industry, hybrid warfare also uses ‘light content’, mixing fragments of ‘true’ with fragments of ‘false’, particularly those that easily excite, and in a short time, the ‘flock of believers’ in these messages grows.[2] The ‘congregation of these propaganda believers’ cannot usually be influenced by ‘arguments’. These convictions are programmed in such a way that all sources that could represent critical alternatives are ‘outlawed’ from the start.[3]

And unfortunately, it is true that Democracies in the 1.0 format appear ‘weak’ and ‘helpless’ so far for this type of freedom use, although slowly the recognition is increasing that there is something like an abuse through ‘false programming of people’.

[1] Preventing systematic abuse of freedom in Democracies 1.0 is difficult to impossible without changing freedom of speech itself.

[2] The broad coverage of this propaganda can be easily recognized when one talks to people in different places in Germany (and abroad!) who do not know each other, but who tell more or less the same stories in a tone of conviction during the course of the conversation. Many (most of them?) even have higher education. This raises the question of how little an academic education apparently promotes a ‘critical spirit’.

[3] A popular term is ‘lying press’. Anything that could become ‘dangerous’ is ‘a lie’, although those who talk about lying press do not seriously engage with this press at all.

What is the probability of survival of truth?

Anyone who has ever delved deeply into the question of ‘truth’ in their life, and who knows that it takes various research, investigations, considerations, and even experiments to ‘look behind the apparent phenomena’, along with much communication with other people, sometimes in other languages, knows that truth is not automatic; truth does not just happen; truth cannot be obtained for ‘free’. The use of truth for beneficial technologies, new forms of agriculture, new transportation systems, etc., appears desirable in retrospect, at the end of a long journey, when it somehow becomes ‘obvious’ what all this is good for, but at the beginning of the path, this is almost unrecognizable to everyone. The pitiable state of the education system in many countries is a telling testament to the low regard for education as a training process for truth.

Given the rapid spread of unscrupulous internet media businesses accompanied by a worldwide surge in hybrid warfare, the survival probability of truth seems to be decreasing. Democracies, as the actual ‘bastions of truth’, are experiencing themselves as a place of accelerated ‘evaporation of truth’.

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Start: July 10, 2024, Last change: July 10, 2024)

Starting Point

As the texts of the book will gradually show, the term ‘collective knowledge’ represents a crucial keyword for a characteristic that deeply defines humans—the life form of ‘homo sapiens.’ For an individual, ‘collective knowledge’ is directly hardly perceivable, but without this collective knowledge, no single human would have any knowledge at all. Yes, this not only sounds like a paradox, it is a paradox. While a ‘contradiction’ between two different statements represents a factual incompatibility, a ‘paradox’ also conveys the impression of a ‘contradiction,’ but in fact, in terms of the matter, it is not an ‘incompatibility.’ The ‘knowledge of us individuals’ is real knowledge, but due to the finiteness of our bodies, our perception system, our memory, we can factually only gather a very small amount of knowledge ‘in us.’ However, the more people there are, the more ‘knowledge’ each person ‘produces’ daily—just analogously, or then also digitally—the greater grows the amount of knowledge that we humans ‘deposit’ in our world. Newspapers, books, libraries, databases can collect and sort this knowledge to a limited extent. But an individual can only find and ‘process’ small fractions of this ‘collected knowledge.’ The gap between the ‘available collected knowledge’ and the ‘individually processable knowledge’ is constantly growing. In such a situation, the availability of generative artificial intelligence in the format of chatbots (GAI-ChaBo) is almost an ‘evolutionary event’! This new technology does not solve all questions, but it can help the individual to ‘principally’ get a novel direct access to what we should call ‘collective knowledge of humanity.’

Before the digitalization of the world …

“Before the digitalization of the world, it was indeed laborious to convey thoughts and knowledge in a way that others could become aware of it: initially only through oral traditions, inscriptions in rocks, then parchment and papyrus, stones and clay with inscriptions. With the availability of paper, writing became easier (though there was the problem of durability); this led to the collection of texts, to books, and the first libraries with books (libraries existed even for cuneiform and clay tablets). Great libraries like the ‘Library of Alexandria’ became precious ‘collection points of knowledge,’ but they were also subjected to various destructive events during their existence, which could lead to great losses of recorded knowledge.

A ‘mechanized production of books’ has been around since the 8th century, and modern book printing began in the 15th century. The development of libraries, however, progressed slowly for a long time, often only on a private basis. It was not until the 19th century that there was a stronger development of the library system, now including public libraries.

Despite this development, it remained difficult for an individual to access knowledge through a library, and even if this (usually privileged) access existed, the availability of specific texts, their inspection, the making of notes—or later copies—was cumbersome and time-consuming. The access of the individual reader resembled small ‘sampling’ that even within the framework of scientific work remained very limited over the years. The language problem should not be overlooked: the proportion of ‘foreign-language books’ in the library of a country A was predominantly restricted to texts in the language of country A.

‘Acquisition of knowledge’ was therefore laborious, time-consuming, and very fragmented for an individual.

An increasingly important alternative to this hard-to-access field of library knowledge were modern magazines, journals, in many languages, with ever shorter ‘knowledge cycles.’ However, the more such journals there are, the more the natural individual limitations come into force, painfully felt in the face of the swelling journal knowledge. Currently (2024), it is hardly possible to estimate the exact number of scientific journals. In the field of computer science alone, approximately 2,000 journals are estimated with an average of about 25,000 (or more) articles per year. And scientific journals only in Chinese are stated to be over 10,000.[1]

[1] For more detailed information on the collection of Chinese journals, you can visit the East View page on China Academic Journals (CAJ) here.

With digitalization

Since the availability of the World Wide Web (WWW) in the 1990s, a unified information space has emerged that has continued to spread globally. Although we are currently witnessing an increasing ‘isolation’ of the WWW among countries, the development of a common information space is unstoppable.

Alongside this information space, technologies for ‘collecting,’ ‘storing,’ ‘retrieving,’ and ‘analyzing’ data have also evolved, making it increasingly possible to find ‘answers’ to ‘questions’ from ever more sources.

With the advent of so-called ‘Generative Artificial Intelligence in the format of Chatbots’ (GAI-ChaBo) since 2022, this ‘data utilization technology’ has reached a level that not only finds ‘raw data’ but also allows an individual user with their limited knowledge direct access to ‘collective human knowledge,’ provided it has been digitized.

For the ‘evolution of life on this planet,’ this availability of collective knowledge to the individual may be the most significant event since the appearance of Homo sapiens itself about 300,000 years ago. Why?

The next level?

The sustainability debate over the last approximately 50 years has contributed to the realization that, alongside the rather individual perspective of life, and strong regional or national interests and perspectives of success, there have gradually come into consciousness — not universally — perspectives that suggest — and are now substantiated by diverse data and models — that there are problems which exceed the event horizon of individual particular groups — and these can be entire nations. Many first think of ‘resources’ that are becoming scarce (e.g., fish stocks), or being polluted (world’s oceans), or dangerously reduced (forest systems, raw materials, life forms, etc.) or more. What has hardly been discussed so far, although it should be the most important topic, is the factor that produces all these problems: Homo sapiens himself, who by his behavior, yes, even just by his sheer numbers, causes all the known ‘problems.’ And this does not happen ‘automatically,’ but because the behavior of Homo sapiens on this planet is ‘controlled’ by his ‘inner states’ in such a way that he seems ‘incapable’ of changing his behavior because he does not have his ‘inner states’ under control.

These inner states, roughly considered, consists of needs, different emotions, and collected experiences linked with knowledge. Knowledge provides the ‘images’ of oneself, others, and the world as a Homo sapiens sees it. Needs and emotions can block, ‘bind,’ or change knowledge. The knowledge that is currently available, however, has great power: ultimately, a Homo sapiens can only do what his current knowledge tells him — if he listens to his knowledge and not to ‘others’ who mean something to him due to the life situation.

If one is now interested in a ‘possible future,’ or — even more specifically — in a ‘possible future that is as good as possible for as many people as possible,’ and sustainable, then the challenge arises as to how people in the situation of everyday life, a certain form of the present, can ‘mentally’ surpass this present in such a way that, despite the current present, they can ‘somehow’ think of a piece of ‘possible future.’

‘Generative Artificial Intelligence in the format of chatbots’ (GAI-ChaBo) can help make the (approximate) entirety of past knowledge — albeit only punctually based on questions — accessible, but the ‘knowledge of the past’ provides ‘nothing new’ out of itself and — above all —, the past does not necessarily have those ‘goals’ and ‘values’ that are necessary in the current present to precisely ‘want that possible future’ that will matter.

With this challenge, Homo sapiens collides ‘with himself’ full force, and he will not be able to ‘hide behind GAI-ChaBo.’ A GAI-ChaBo always only delivers what people have previously said and done, albeit in a breadth that an individual could not achieve, but ultimately a GAI-ChaBo only functions like a kind of ‘mirror of the human collective.’ A GAI-ChaBo cannot replace humanity itself. A GAI-ChaBo is the product of collective human intelligence and can make the entirety of this collective intelligence visible in outline (an incredibly great achievement), but no more.

For the next level, Homo sapiens must somehow manage to ‘get a grip on himself’ in a completely different way than before. There are hardly any usable role models in history. What will Homo sapiens do? GAI-ChaBo is an extraordinary success, but it is not the last level. We can be curious…

This text is part of the text “Rebooting Humanity”

The German version can be found HERE.

Author No. 1: Gerd Doeben-Henisch
Contact: cagent@cognitiveagent.org

(Start: June 5, 2024, Last updated: June 6, 2024)

Starting Point

In the broader spectrum of human activities, there are three ‘types’ of action patterns that are common in everyday life and crucial for shared communication and coordination: (i) writing texts, (ii) describing (calculating) quantitative relationships, and (iii) planning possible states in an assumed future. All three types have been present since the beginning of documented ‘cultural life’ of humans. The following attempts a rough typology along with known forms of implementation.

Types and their implementation formats

(a) MANUAL: We write texts ‘manually’ using writing tools and surfaces. Similarly, in quantitative matters, there is manual manipulation of objects that represent quantitative relationships. In planning, there is the problem of how to represent a ‘new’ state: if the ‘new’ is ‘already known’, one can revert to ‘images/symbols’ of the known; if it is ‘truly new’, it becomes difficult; there is no ‘automatism of the new’. How do you describe something that has never existed before? Added to this is the — often overlooked — problem that ‘objects of planning’ are usually ‘value and goal dependent’; needs, intentions, expectations, factual necessities can play a role. The latter can be ‘socially standardized’, but given the ‘radical openness of the future’, history has shown that ‘too strong standardizations’ can be a shortcut to failure.

(b) MANUAL SUPPORT: Skipping the phase of ‘partially mechanical’ support and moving on to the early phase of ‘computer support’, there are machines that can be ‘programmed’ using ‘programming languages’ so that both the writing instrument and the surface are represented by the programmed machine, which allows many additional functions (correcting texts, saving, multiple versions, automatic corrections, etc.). However, one still has to write oneself: letter by letter, word for word, etc. In ‘calculating’, writing down is still very laborious, but the ‘calculation’ then takes place partially ‘automatically’. Planning is similar to writing texts: the ‘writing down’ is supported (with all the additional functions), but ‘what’ one writes down is left to the user. Apart from ‘quantitative calculating’, a ‘projection’, a ‘prediction’ is generally not supported. An ‘evaluation’ is also not supported.

(c) LANGUAGE-BASED SUPPORT: The phase of ‘language-based support’ replaces manual input with speaking. For selected areas of texts, this is becoming increasingly successful. For ‘quantitative matters’ (calculating, mathematics, etc.), hardly at all. For planning also only very limited, where it concerns already formulated texts.

(d) ARTIFICIAL INTELLIGENCE ENVIRONMENTS: The Artificial Intelligence (AI) environment is considered here in the context of dialogue formats: The user can ask questions or send commands, and the system responds. The relevant formats of AI here are the so-called ‘generative AIs’ in the chatbot format. Under the condition of ‘existing knowledge’ of humans in the format ‘stored documents/images/…’ and under the condition of ‘dialogue formats’ of humans (also through explicit training), these generative AIs can process questions and orders in ‘formal proximity’ to the known material in the context of a dialogue so that one does not have to intervene oneself. Corrections and changes in detail are possible. Both in ‘text creation’ and in ‘calculating’, this can function reasonably well within the realm of the ‘known’. However, the actual accuracy in the ‘real world’ is never guaranteed. In ‘planning’, the specific problem remains that for the AI, ‘truly new’ is only limited possible within the framework of combinatorial matters. The truth reservation remains, but also applies in the ‘manual case’, where the human plans themselves. The evaluation problem is also limited to already known evaluation patterns: the future is not the same as the past; the future is more or less ‘different’. Where should the necessary evaluations come from?

An interesting question remains, in what sense the advancing support by generative AIs actually supports communication and coordination among people, and specifically, whether and to what extent the central challenge of ‘future planning’ can be additionally supported by it. The fact is that we humans struggle with future planning in the areas of social life, community life, and larger contexts such as counties, states, etc., ranging from difficult to very difficult. But, for a sustainable future, successful planning seems to be indispensable.

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Start: June 25, 2024, Last change: June 28, 2024)

Starting Point

The excerpt discusses the concept of ‘collective human intelligence (CHI)’ and reflects on the foundational schema of all life : reproduction of Generation 1, birth of Generation 2, growth of Generation 2, followed by the onset of Generation 2’s behaviors accompanied by learning processes, and then reproduction of Generation 2, etc. It highlights how genetic predispositions and ‘free adapting’, commonly referred to as ‘learning’, alternate in phases. While genetic guidelines enable structures with typical functionalities that open up ‘possible action spaces’, filling these spaces is not genetically determined. This makes sense because the real ‘biological carrier system’ is not isolated but exists in an ‘open environment’ whose specific configuration and dynamics constantly change. From a ‘sustainable survival’ perspective, it is crucial that the biological carrier system has the ability to not only grasp the nuances of the environment at specific moments but also to represent, combine, and test them in the context of space and time. These simple words point to a highly complex process that has become known as ‘learning’, but the simplicity of this term may overlook the fact that we are dealing with an ‘evolutionary miracle of the highest order’. The common concept of ‘evolution’ is too limited in this perspective; it only describes a fragment.

A ‘Logic of Life’?

Basic Pattern of All Life

The ‘basic pattern of all life’ provokes many considerations. It is striking how phases of genetic change, which imply new structures and functionality, ultimately transform the ‘initial space’ of genetic changes into new, significantly more complex spaces, not just once, but repeatedly, and the more often, the more complexity comes within reach.

The life form of ‘Homo sapiens’—us, who call ourselves ‘humans’—represents a provisional peak of complexity in the temporal view of history so far, but already suggests from within itself a possible ‘next evolutionary stage’.

Even viewed closely, the individual human—with his structured cell galaxy, with the possible functions here, with his individual learning—represents an extraordinary event—relative to the entire known universe—, but this ‘individual’ human in his current state is already fully designed for a ‘plurality of people’, for ‘collective behavior’, for ‘collective learning’, and certainly also for ‘collective achievements’.

[1] The world of ‘molecules’ is transformed into the world of ‘individual cells’; the world of ‘individual cells’ is transformed into the world of ‘many cells (cellular complexes)’; the world of ‘cell complexes’ is transformed into the world of ‘structured cell complexes’, …, the world of structured ‘cell galaxies’ is transformed into the world of ‘cooperating structured cell galaxies with individual and collective learning’, …

Temporal Classification

Not only have the last few millennia shown what many people can achieve together, but particularly the ‘modern engineering achievements’ involving the collaboration of many thousands, if not tens of thousands of experts, distributed globally, over extended periods (months, year, many years), simultaneously in many different languages, dealing with highly complex materials and production processes—processes in which meta-reflection and feedback loops are taken for granted –… These processes, which have been globally initiated since the great war in the mid-20th century, have since become more and more the everyday standard worldwide. [2] The invention of programmable machines, information networks, highly complex storage systems, and the provision of ever more ‘human-compatible interfaces’ (visual, acoustic, tactile, …), up to those formats that make it appear to the human user as if ‘behind the interface’ there is another living person (even if it is ‘just’ a machine), have all occurred within just about 70 years.

While it took a considerable amount of time from the first evidences of biological life on planet Earth (around -3.4 billion years ago) to the first proven appearance of Homo sapiens in North Africa (around -300,000 years ago), the development of the complex ‘mental’ and ‘communicative’ abilities of Homo sapiens starting around -300,000 years ago, was initially slow (invention of writing around -6000), but the development then accelerated significantly over the last approximately 150 years: the complex events are almost overwhelming. However, considering the entire time since the presumed formation of the entire universe about 13.7 billion years ago, there is a rough time schema:

After about 75% of the total time of the existence of the universe, the first signs of biological life.

After about 99.998% of the total time of the existence of the universe, the first signs of Homo sapiens.

After about 99.999998% of the total time of the existence of the universe, the first signs of complex collective human-technical intelligence achievements.

This means that, in relation to the total time, the periods for the ‘latest’ leaps in complexity are so ‘short’ that they can no longer be distinguished on a large scale. This can also be interpreted as ‘acceleration’. It raises the question of whether this ‘acceleration’ in the creation of increasingly complex collective intelligence achievements reveals a ‘logic of process’ that would enable further considerations?

[2] Here began the career of the modern form of ‘Systems Engineering’, a quasi-standard of problem solving, at least in the English-speaking world.

Complexity Level: Biological Cell

With the description of a ‘basic pattern of all life’, a pattern emerges that is describable at least onwards from the complexity level of a biological cell.

The complexity level preceding the biological cell is that of ‘molecules’, which can be involved in different process chains.

In the case of the biological cell, we have, among other things, the case where molecules of type 1 are used by molecules of type 2 as if the type 1 molecules were ‘strings’ that ‘represent’ molecules of type 3, which are then ‘produced’ through certain chemical processes. Put differently, there are material structures that interpret other material structures as ‘strings’, possessing a ‘meaning assignment’ that leads to the creation of new material structures.

Thus, biological cells demonstrate the use of ‘meaning assignment’, as we know structurally in the case of symbolic languages from complex cell galaxies. This is extremely astonishing: how can ‘ordinary molecules’ of type 2 have a ‘meaning assignment’ that allows them to interpret other molecules of type 1 as ‘strings’ in such a way that they—according to the meaning assignment—lead to the organization of other molecules of type 3, which ultimately form a structure with functional properties that cannot be derived ‘purely materially’ from the type 1 molecules.

… !! New text in preparation !!..

[3] In this context, the term ‘information’ (or ‘biological information’) is commonly used in the literature. If this usage refers to the terminology of Claude Shannon, then it would be difficult to apply, as in the specific case it is not about the transmission of ‘signal elements’ through a signal channel to ‘received signal elements’ (a structural 1-to-1 mapping), but about an assignment of ‘signs (= signal elements)’ to something ‘completely different’ than the original signal elements.

A ‘Logic’?

When the main title tentatively (‘hypothetically’) mentions a ‘Logic of Life’, it is important to clarify what specifically is meant by the term ‘logic’ as a possible concept.

The term ‘logic’ dates back to Aristotle, who introduced it around 2400 years ago in Greece. It was then translated back into the Latin of the Christian Middle Ages via the Islamic culture around 1000 AD, profoundly influencing the intellectual life of Europe until the late Middle Ages. In contrast to ‘modern formal logic’—from the late 19th century onwards—the ‘Aristotelian logic’ is also referred to as ‘classical logic’.

If one disregards many details, classical and modern logic differ fundamentally in one aspect: in classical logic, the ‘linguistic meaning’ of the expressions used plays an important role, whereas in modern logic, linguistic meaning is completely excluded. ‘Mutilated remnants’ of meaning can still be found in the concept of an ‘abstract truth’, which is reflected in ‘abstract truth values’, but their ‘meaning content’ is completely empty.

The concept of both classical and modern logic—despite all differences—is united by the concept of ‘logical reasoning’: Suppose one has a set of expressions that are deemed ‘somehow true’ by the users of logic, then there are ‘rules of application’ on how to generate other expressions from the set of ‘assumed true expressions’, which can then also be considered ‘true expressions’. This ‘generation’ of new expressions from existing ones is called ‘reasoning’ or ‘inference’, and the ‘result’ of the reasoning is then a ‘conclusion’ or ‘inference’.

A more modern—formulaically abbreviated—notation for this matter would be:

A ⊢_Tr B

Here, the symbol ‘A’ represents a set of expressions assumed to be true, ‘Tr’ stands for a set of transformation instructions (usually called ‘rules of inference or inference rules’), ‘B’ stands for a generated (derived) expression, and ‘⊢’ refers to an ‘action context’ within which users of logic use transformation rules to ‘generate B based on A’.

A ‘normal’ logician, in the case of the symbol ‘⊢’, does not speak of an ‘action context’ but usually just of a ‘concept of inference’ or—with an eye to the widespread use of computers—of an ‘inference mechanism’; however, this way of speaking should not obscure the fact that ‘what actually exists’ are once concrete ‘objects’ in the form of expressions ‘A’ and ‘B’, and also in the form of expressions ‘Tr’. These expressions as such have neither any ‘meaning’ nor can these expressions ‘generate anything by themselves’. For the concrete expressions ‘B’ to be classified as ‘inference’ from the expressions ‘A’, which are ‘really generated’ by means of ‘Tr’, a real ‘process’ must take place in which ‘B’ is ‘really generated’ from ‘A’ ‘in the sense of Tr’.

A process is a real event ‘in time’, in which there is a real state that contains the object ‘A’, and a real logic user who has a ‘concept = model’ of ‘logical reasoning’ in his head, in which the ‘expressions’ of the generation rules Tr are linked with concrete process steps (the meaning of the expressions Tr), so that the logic user can identify the expressions belonging to A as part of the generation rules in a way that the generation rules can assign a new expression B to the expressions A. If this assignment ‘in the mind of the logic user’ (commonly referred to as ‘thinking’) is successful, he can then write down a new expression B referring to the concrete expressions Tr in a ‘subsequent situation’. Another logic user will only accept this new expression ‘B’ if he also has a ‘concept = model’ of logical reasoning in his head that leads to the same result ‘B’ in his mind. If the other logic user comes to a different result than ‘B’, he will object.

–!! Not finished yet! —

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Last modified: June 14, 2024)

Starting Point

How can one philosophically conceive of artificial intelligence (AI) interacting with real people, an AI that learns real language with real meaning on its own? Prompted by an offer from Ars Electronica ’95 to introduce a philosophically inspired art project, I spent intense months with an ad hoc software team (the team was wonderful!) designing (and implemented by the software team) the interactive network version of a small artificial world based on philosophical considerations. In this world lived ‘blind Knowbots’ that could communicate with the outside world, using their other sensory experiences and basic bodily states as the basis for assigning meanings to their respective languages. Whatever language the users (mostly children!) used, they could link this language with their real-world experiences. This experiment has shaped me for many years, actually up to today.

Blind’s World One

(The text was copied from the Ars Electronica ’95 book since the text is no longer accessible)

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Start: June 14, 2024, Last Modification: June 14, 2024)

Starting Point

Since September 2015, I have been repeatedly trying—both theoretically and practically—to understand what sound art really is; what is sound? What does it do to us? One consideration led to another; between them were real experiments and live performances. There were also long periods of ‘standstill’…. At a sound art concert on June 11, 2024, at Mousonturm in Frankfurt, something clicked in my mind regarding a fundamental question, and suddenly the uniqueness of ‘collective human intelligence’ in confrontation with so-called ‘intelligent machines’ became somehow newly clearer to me.

XEROX EXOTIQUE #090 – IMPRESSIONS

This post on an associated blog is about people and machines that can generate sound.

The trigger was a sound art event at the Mousonturm in Frankfurt am Main on June 11, 2024.

Here comes the translation:

A Hint from a Friend

Following a tip from Tobias (PiC, Xerox Exotique, …), I made a trip yesterday to the sound art event #090, organized by Xerox Exotique at the Mousonturm in Frankfurt am Main.

Impressions

SKETCH: Mousonturm, a small event area to the right of the entrance with a small stage. Some participants are highlighted. Detailed information about the event can be found on the XEROX EXOTIQUE website (xeroxex.de).

What to Talk About?

A sound art event like this offers numerous starting points for discussion…

Since the beginning of Philosophy in Concert (PiC), I have been driven by the question of how to situate soundscapes in the life world of people so that they do not seem like ‘foreign bodies,’ somehow ‘detached’ from the process of humans on this planet, but as a ‘living part’ of this very real-dynamic process made visible.

At concerts based on written music (scores…), it all revolves around the sets of symbols that someone has produced, which others convert into sounds, and perhaps about the person who holds the ‘office of the interpreter’ and tells other implementers how they should convert. The ‘typically human’ aspect may then be recognized in the ‘background of the notation’, in the way of ‘converting’ or ‘interpreting’, and then the effect of the sound cloud in the room on the people who sit, listen, and experience various emotions…

How much of the human process is revealed in such a form of event?

There is almost never any talking, and if there is, what is there to talk about? About one’s own feelings? About the technical intricacies of the written? About the skill of the converters? About the beauty of a voice? Yes, it’s not easy to integrate the sound event into the life process… and yet, it affects somehow, one remembers, talks about it later, may rave or complain…

The Hidden Human

Let’s briefly change the context and dive directly into the current global euphoria many people have over the new chatbots, which increasingly fascinate more people in everyday life, products of ‘generative Artificial Intelligence’ (chatGPT & Co).

The algorithms behind the interface are comparatively simple (although the global deployment is due to impressive engineering). What fascinates people in front of the interface is ‘how human the algorithms appear in the interface’. They use everyday language just as ‘we humans’ do, ultimately even better than most of those who sit in front of it. And — almost irresistibly — many see, because of this language and the accessible knowledge ‘behind the interface’, not a simple machine but something ‘profoundly human’. What is ‘human’ about this appearance, however, are the words, sentences, and texts that the simple algorithm has compiled from millions of documents, all of which come from humans. On its own, this algorithm cannot generate a single sentence! It lacks fundamental prerequisites. The ‘actual’ wonder sits in front of the ‘apparent’ wonder: it is we humans, who have, are, and represent something that we are barely aware of ourselves (we are ‘blind through ourselves’), and we marvel when simple algorithms show us what we are… ultimately like a mirror of humanity, but most do not notice; we get excited about simple algorithms and forget that we ourselves are exactly this wonder that has produced all this, continues to produce… we become blind to the real wonder that we ourselves are, each of us, all together.

Collective Intelligence – Collective ‘Spirit’…

In the case of algorithms, the term ‘artificial intelligence (AI)’ has been used for a long time, and more moderately, ‘machine learning (ML)’. However, the concept of intelligence has not yet been truly standardized, even though psychology has developed and experimentally researched interesting concepts of ‘intelligence’ (e.g., the ‘Intelligence Quotient (IQ)’) for humans for about 120 years. The communication between psychology and computer science, however, has never been very systematic; rather, everyone does ‘their own thing’. Thus, precisely determining the relationship between ‘human intelligence (HI)’ and ‘artificial intelligence (AI)’ has so far been quite difficult; the terms are too vague, not standardized. Moreover, it is complicated by the fact that the ‘actually impressive achievements’ of humans are not their ‘individual achievements’ (although these are important), but everything that ‘many people together over a long time’ have accomplished or are accomplishing. The term ‘Collective Human Intelligence (CHI)’ is in this direction but is probably too narrow, as it’s not just about ‘intellect’ but also about ‘communication’, ’emotions’, and ‘goals’. Unfortunately, research on the topic of Collective Human Intelligence is still far behind. The focus on the individual runs deep, and then in times of artificial intelligence, where individual machines achieve remarkable feats (under the premise of the collective achievements of humans!), even the study of individual human intelligence has fallen into the shadow of attention.

How do we get out of this impasse?

Sound Art as a Case Study?

I hadn’t attended a sound art concert in many years. But there were still memories, various aspects swirling through my mind.

The tip from Tobias catapulted me out of my usual daily routines into such a sound art event at the Mousonturm on June 11, 2024, at 8:00 pm.

As I said, there is a lot to talk about here. For a long time, I have been preoccupied with the question of the ‘collective’ dimension in human interaction. The ‘synchronization’ of people by algorithms is nothing unusual. In a way, humans have always been ‘standardized’ and ‘aligned’ by the prevailing ‘narratives,’ and the rapid spread of modern ‘narratives’ and the speed with which millions of people worldwide join a narrative is a fact. Most people (despite education) are apparently defenseless against the emergence of ‘narratives’ at first, and then very soon so strongly ‘locked-in’ that they reproduce the narratives like marionettes.

What role can ‘sound art’ play against such a backdrop? Sound art, where there is nothing ‘written’, no ‘central interpreter’, no ‘converters of the written’, but, yes, what?

That evening, the first group, ‘Art Ensemble Neurotica’, seemed to me to most broadly illustrate the profound characteristics of sound art. In the two following solo acts, where the individual performer interacted with sound they themselves produced, the special dimension of sound art was also present, in my view, but more concealed due to the arrangement.

In the case of Neurotica: Four people generated sound, live, each individually: Dirk Hülstrunk (narrator) – Michael Junck (digital devices) – Johannes Aeppli (percussion) – Guido Braun (strings & conductor). Each person on stage was a ’cause’, equipped with instruments that allowed all sorts of sound effects. There were no written notes; there hadn’t been a real rehearsal beforehand, but some arrangements (according to Guido).

Anyone who knows how diversely each individual can generate sound under these conditions can imagine that this seemingly infinite space can give rise to tension about what will happen next?

Describing the totality of sound that emanated from the four performers upfront for 45 minutes is nearly impossible in detail. At no stage did it seem (I exchanged views immediately afterwards with Roland (incorrectly identified as Robert in the sketch) next to me—we didn’t know each other, it was a coincidence we sat next to each other), that one sound source drowned out or overwhelmed another; everything appeared side by side and intertwined in a somehow ‘fitting form’, appealing and stimulating. Patterns from all four individual sources could be recognized interacting with each other over extended phases, yet they were supple, changing shape. Effects like volume shifts, echo, reverb, distortion, etc., did not feel out of place but seemed ‘harmonic’… giving each source a ‘character’ that combined with the others to form an overall impression…

Can such an arrangement of sounds be taken ‘purely abstractly’, detached from their creators? Could software generate such a complex sound event?

While the listener initially hears only the produced sound and might not immediately decide from this perspective whether it matters who and how this sound is produced, from the perspective of creation it quickly becomes clear that these sounds cannot be isolated from the producer, from the ‘inner states’ of the producer. Ultimately, the sound is created in the moment, in the interaction of many moments inside each individual actor (human), and this individual is not ‘alone’, but through his perception and many jointly experienced sound processes, each possesses a ‘sound knowledge’ that he more or less ‘shares internally’ with others, and thus each can bring his current inner states into a ‘dialogue’ with this ‘shared sound knowledge’. It is precisely this inner dialogue (largely unconscious) that provides opportunities for complex synchronizations, which an individual alone, without a shared history of sound, could not have. The resulting complex sounds are therefore not just ‘sound’ but are more manifestations of the internal structures and processes of the creators, which as internal meaning are linked with the external sound: Sound art sound is therefore not just sound one hears, it is also fully a kind of ‘communication’ of ‘inner human states’, spread over various collaborating individuals, thus a true collective event that presupposes the individual but extends far beyond in the happening. In this form of distributed sound art, the individual can experience themselves as a ‘WE’ that would otherwise be invisible.

Postscript

So, I now have this strange feeling that participating in this sound art event has led me deeper into the great mystery of us humans, who we are, that we have a special dimension of our existence in our ability to ‘collectively feel, think, and act,’ which somewhat liberates us from ‘individuality’ towards a very special ‘We’.

While a soundscape is ‘authentic’ and as such not ‘underminable’, ‘narrative spaces’—the use of language with an assumed, but not easily controllable potential meaning—are extremely ‘dangerous’ spaces: assumed meanings can be false and—as we can see today on a global scale—are predominantly wrong with correspondingly devastating consequences. Moving in distributed sound spaces has its ‘meaning’ ‘within itself’; the ‘Self in sound together’ is not underminable; it is mercilessly direct. Perhaps we need more of this…

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Start: June 14, 2024, Last Modification: June 14, 2024)

Starting Point

In both the section “Talking about the World” and the section “Verifiable Statements,” the theme of ‘change’ continuously emerges: our everyday world is characterized by everything we know being capable of ‘changing,’ including ourselves, constantly, often unconsciously; it just happens. In the context of people trying to collectively create an understanding of the world, perhaps also attempting to ‘plan’ what should be done together to achieve the best possible living situation for as many as possible in the future, the phenomenon of ‘change’ presents an ambivalent challenge: if there were no change, there would be no future, only ‘present’; but with change occurring, it becomes difficult to ‘look into the future’. How can we know into what future state all these changes will lead us? Do we even have a chance?

Changes

Motivation

In the current scenario, we assume a context of people trying to collectively form a picture of the world, who may also be attempting to ‘plan’ joint actions. It’s essential to recognize that the ‘relevant’ topics of interest are influenced by ‘which people’ one is working with, as each group within a society can and often does have its ‘own perspectives’. It is not only in ‘autocratic’ societal systems that citizens’ perspectives can be easily overlooked; there are plenty of examples in officially ‘democratic’ systems where citizens’ concerns are also overlooked, warranting closer analysis.

This discussion initially focuses on the fundamental mechanisms of ‘change’, specifically the ‘collective description’ of changes. The motivation for this emphasis stems from the fact that different people can only ‘coordinate (align) their actions’ if they first manage to ‘communicate and agree’ on the ‘contents of their actions’ through ‘communication processes’.

While simple situations or small groups may manage with verbal communication alone, most scenarios require ‘written texts’ (documents). However, written text has a disadvantage compared to direct speech: a ‘text’ can be ‘read’ in a situation where the ‘reader’ is not currently in the situation being described. In terms of ‘verifiability of statements’, this presents a real challenge: every text, due to ‘learned meaning relationships’, automatically has a ‘meaning’ that is activated ‘in the mind of the reader’, but it is crucial to verify whether there is a ‘real verifiable correspondence’ to the situation ‘described’ in the text.

If we assume that a group of people seriously contemplates a ‘future’ that they believe is ‘more likely to occur than not’—not just ‘theoretically’ but ‘actually’—then there must be a way to design the description of a ‘starting situation’ such that all participants have a chance to verify its accuracy in their shared everyday life.

…

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Start: June 7, 2024, Last change: June 9, 2024)

Starting Point

Speaking in everyday life entails that through our manner of speaking, we organize the perceptions of our environment, solely through our speech. This organization occurs through thinking, which manifests in speaking. As previously described, while the ability to speak is innate to us humans, the way we use our speech is not. In speaking, we automatically create an order, but whether this order actually corresponds to the realities of our everyday world requires additional verification. This verification, however, does not happen automatically; we must explicitly desire it and carry it out concretely.

Verifiable Statements

If one accepts the starting point that linguistic expressions, which enable our thinking, are initially ‘only thought’ and require additional ‘verification in everyday life’ to earn a minimal ‘claim to validity in practice’, then this basic idea can be used as a starting point for the concept of ’empirical verifiability’, which is seen here as one of several ‘building blocks’ for the more comprehensive concept of an ’empirical theory (ET)’.

Language Without Number Words

Here are some everyday examples that can illustrate some aspects of the concept of ’empirical verifiability’:

Case 1: There is an object with certain properties that the involved persons can perceive sensorily. Then one person, A, can say: ‘There is an object X with properties Y.’ And another person, B, can say: ‘Yes, I agree.’

Case 2: A specific object X with properties Y cannot be sensorily perceived by the involved persons. Then person A can say: ‘The object X with properties Y is not here.’ And another person, B, can say: ‘Yes, I agree.’

Case 3: There is an object with certain properties that the involved persons can sensorily perceive, which they have never seen before. Then person A can say: ‘There is an object with properties that I do not recognize. This is new to me.’ And another person, B, can then say: ‘Yes, I agree.’

The common basic structure of all three cases is that there are at least two people who ‘speak the same language’ and are in a ‘shared situation’ in everyday life. One person—let’s call him A—initiates a conversation with a ‘statement about an object with properties,’ where the statement varies depending on the situation. In all cases, the person addressed—let’s call him B—can ‘agree’ to A’s statements.

The three cases differ, for example, in how the object ‘appears’: In case 1, an object is ‘simply there,’ one can ‘perceive’ it, and the object appears as ‘familiar.’ In case 2, the object is known, but not present. In case 3, there is also an object, it can be perceived, but it is ‘not known.’

For the constructive success of determining an agreement that finds approval among several people, the following elements are assumed based on the three cases:

The participants possess:

• ‘Sensory perception’, which makes events in the environment recognizable to the perceiver.
• ‘Memory’, which can store what is perceived.
• ‘Decision-making ability’ to decide whether (i) the perceived has been perceived before, (ii) the perceived is something ‘new,’ or (iii) an object ‘is no longer there,’ which ‘was there before.’
• A sufficiently similar ‘meaning relationship’, which enables people to activate an active relationship between the elements of spoken language and the elements of both perception and memory, whereby language elements can refer to contents and vice versa.

Only if all these four components [2] are present in each person involved in the situation can one convey something linguistically about their perception of the world in a way that the other can agree or disagree. If one of the mentioned components (perception, memory, decision-making ability, meaning relationship) is missing, the procedure of determining an agreement using a linguistic expression is not possible.

[1] There are many different cases!

[2] These four concepts (perception, memory, decision-making ability, meaning relationship) are ‘incomprehensible on their own.’ They must be explained in a suitable context later on. They are used here in the current concept of ‘verifiable statements’ in a functional context, which characterizes the concept of ‘verifiable statement’.

Language with Numerals

Typically, everyday languages today include numerals (e.g., one, two, 33, 4400, …, 1/2, 1/4), although they vary in scope.

Such numerals usually refer to some ‘objects’ (e.g., three eggs, 5 roses, 33 potatoes, 4400 inhabitants, … 1/2 pound of flour, 44 liters of rainfall in an hour, …) located in a specific area.

A comprehensible verification then depends on the following factors:

• Can the specified number or quantity be directly determined in this area (a clear number must come out)?
• If the number or amount is too large to estimate directly in the area, is there a comprehensible procedure by which this is possible?
• What is the time required to make the determination in the area (e.g., minutes, hours, days, weeks, …)? If the necessary time always increases, it becomes increasingly difficult to make the statement for a specific time (e.g., the number of residents in a city).

These examples show that the question of verification quickly encompasses more and more aspects that must be met for the verifiability of a statement to be understood and accepted by all involved.

Language with Abstractions

Another pervasive feature of everyday languages is the phenomenon that, in the context of perception and memory (storing and recalling), abstract structures automatically form, which are also reflected in the language. Here are some simple examples:

IMAGE: Four types of objects, each seen as concrete examples of an abstract type (class).

In everyday life, we have a word for the perceived objects of types 1-4, even though the concrete variety makes each object look different: In the case of objects of group 1, we can speak of a ‘clock,’ for group 2 of a ‘cup,’ for 3 of ‘pens,’ and in the case 4 of ‘computer mice,’ or simply ‘mice,’ where everyone knows from the context that ‘mouse’ here does not mean a biological mouse but a technical device related to computers. Although we ‘sensorily’ see something ‘different’ each time, we use the ‘same word.’ The ‘one word’ then stands for potentially ‘many concrete objects,’ with the peculiarity that we ‘implicitly know’ which concrete object is to be linked with which word. If we were not able to name many different concrete objects with ‘one word,’ we would not only be unable to invent as many different words as we would need, but coordination among ourselves would completely break down: how could two different people agree on what they ‘perceive in the same way’ if every detail of perception counted? The same object can look very different depending on the angle and lighting.

The secret of this assignment of one word to many sensually different objects lies not in the assignment of words to elements of knowledge, but rather the secret lies one level deeper, where the events of perception are transformed into events of memory. Simplifying, one can say that the multitude of sensory events (visual, auditory, gustatory (taste), tactile, …) after their conversion into chemical-physical states of nerve cells become parts of neuronal signal flows, which undergo multiple ‘processings’. As a result, the ‘diversity of signals’ is condensed into ‘abstract structures’ that function as a kind of ‘prototype’ connected to many concrete ‘variants.’ There are thus something like ‘core properties’ that are ‘common’ to different perception events like ‘cup,’ and then many ‘secondary properties’ that can also occur, but not always, the core properties do. In the case of the ‘clock,’ for example, the two hands along with the circular arrangement of marks could be such ‘core properties.’ Everything else can vary greatly. Moreover, the ‘patterns of core and secondary properties’ are not formed once, but as part of processes with diverse aspects e.g., possible changes, possible simultaneous events, etc., which can function as ‘contexts’ (e.g., the difference between ‘technical’ and ‘biological’ in the case of the term ‘mouse’).

Thus, the use of a word like ‘clock’ or ‘cup’ involves— as previously discussed—once the reference to memory contents, to perceptual contents, to learned meaning relationships, as well as the ability to ‘decide’ which of the concrete perception patterns belong to which learned ‘prototype.’ Depending on how this decision turns out, we then say ‘clock’ or ‘cup’ or something else accordingly. This ability of our brain to ‘abstract,’ by automatically generating prototypical ‘patterns’ that can exemplify many sensorially different individual objects, is fundamental for our thinking and speaking in everyday life. Only because of this ability to abstract can our language work.

It is no less impressive that this basic ‘ability to abstract’ of our brain is not limited to the relationship between the two levels ‘sensory perception’ and ‘storage in memory,’ but works everywhere in memory between any levels. Thus, we have no problem grouping various individual clocks based on properties into ‘wristwatches’ and ‘wall clocks.’ We know that cups can be seen as part of ‘drinking vessels’ or as part of ‘kitchenware.’ Pens are classified as ‘writing instruments,’ and ‘computer mice’ are part of ‘computer accessories,’ etc.

Often, such abstraction achievements are also referred to as ‘categorizations’ or ‘class formation,’ and the objects that are assigned to such class designations then form the ‘class content,’ where the ‘scope’ of a class is ‘fluid.’ New objects can constantly appear that the brain assigns to one class or another.

Given this diversity of ‘abstractions,’ it is not surprising that the assignment of individual objects to one of these classes is ‘fluid,’ ‘fuzzy.’ With the hundreds or more different shapes of chairs or tables that now exist, it is sometimes difficult to decide, is this still a ‘chair’ or a ‘table’ in the ‘original sense’ [2] or rather a ‘design product’ in search of a new form.

For the guiding question of the verifiability of linguistic expressions that contain abstractions (and these are almost all), it follows from the preceding considerations that the ‘meaning of a word’ or then also the ‘meaning of a linguistic expression’ can never be determined by the words alone, but almost always only by the ‘context’ in which the linguistic expression takes place. Just as the examples with the ‘numerical words’ suggest, so must one know in a request like “Can you pass me my cup” which of the various cups was the ‘speaker’s cup.’ This presupposes the situation and ‘knowledge of the past of this situation’: which of the possible objects had he used as his cup?[3]

Or, when people try to describe a street, a neighborhood, a single house, and the like with language. Based on the general structures of meaning, each reader can form a ‘reasonably clear picture’ ‘in his head’ while reading, but almost all details that were not explicitly described (which is normally almost impossible) are then also not present in the reconstructed ‘picture in the head’ of the reader. Based on the ‘experience knowledge’ of the language participants, of course, everyone can additionally ‘color in’ his ‘picture in the head.'[4]

If a group of people wants to be sure that a description is ‘sufficiently clear,’ one must provide additional information for all important elements of the report that are ‘ambiguous.’ One can, for example, jointly inspect, investigate the described objects and/ or create additional special descriptions, possibly supplemented by pictures, sound recordings, or other hints.

When it comes to details, everyday language alone is not enough. Additional special measures are required.[5]

[1] A problem that machine image recognition has struggled with from the beginning and continues to struggle with to this day.

[2] The ‘original’ sense, i.e., the principle underlying the abstraction performance, is to be found in those neuronal mechanisms responsible for this prototype formation. The ‘inner logic’ of these neuronal processes has not yet been fully researched, but their ‘effect’ can be observed and analyzed. Psychology has been trying to approximate this behavior with many model formations since the 1960s, with considerable success.

[3] Algorithms of generative artificial intelligence (like chatGPT), which have no real context and which have no ‘body-based knowledge,’ attempt to solve the problem by analyzing extremely large amounts of words by breaking down documents into their word components along with possible contexts of each word so that they can deduce possible ‘formal contexts,’ which then function as ‘quasi-meaning contexts.’ To a certain extent, this works meanwhile quite well, but only in a closed word space (closed world).

[4] A well-known example from everyday life here is the difference that can arise when someone reads a novel, forms ideas in their head, and eventually someone produced a movie about the novel: to what extent do the ideas one has made of individual people correspond with those in the movie?

[5] Some may still know texts from so-called ‘holy scriptures’ of a religion (e.g., the ‘Bible’). The fundamental problem of the ‘ambiguity’ of language is of course intensified in the case of historical texts. With the passage of time, the knowledge of the everyday world in which a text was created is lost. Then, with older texts, there is often a language problem: the original texts, such as those of the Bible, were written in an old Hebrew (‘Old Testament’) or an old Greek (‘New Testament’), whose language use is often no longer known. In addition, these texts were written in different text forms, in the case of the Old Testament also at different times, whereby the text has also been repeatedly revised (which is often also connected with the fact that it is not clear who exactly the authors were). Under these conditions, deducing an ‘exact’ meaning is more or less restricted or impossible. This may explain why interpretations in the approximately 2000 years of ‘Bible interpretation’ have been very different at all times.