Changes: July 20.2019 (Rewriting the introduction)
This Philosophy Lab section of the uffmm science blog is the last extension of the uffmm blog, happening July 2019. It has been provoked by the meta reflections about the AAI engineering approach.
SCOPE OF SECTION
This section deals with the following topics:
How can we talk about science including the scientist (and engineer!) as the main actors? In a certain sense one can say that science is mainly a specific way how to communicate and to verify the communication content. This presupposes that there is something called knowledge located in the heads of the actors.
The presupposed knowledge usually is targeting different scopes encoded in different languages. The language enables or delimits meaning and meaning objects can either enable or delimit a certain language. As part of the society and as exemplars of the homo sapiens species scientists participate in the main behavior tendencies to assimilate majority behavior and majority meanings. This can reduce the realm of knowledge in many ways. Biological life in general is the opposite to physical entropy by generating auotopoietically during the course of time more and more complexity. This is due to a built-in creativity and the freedom to select. Thus life is always oscillating between conformity and experiment.
The survival of modern societies depends highly on the ability to communicate with maximal sharing of experience by exploring fast and extensively possible state spaces with their pros and cons. Knowledge must be round the clock visible to all, computable, modular, constructive, in the format of interactive games with transparent rules. Machines should be re-formatted as primarily helping humans, not otherwise around.
To enable such new open and dynamic knowledge spaces one has to redefine computing machines extending the Turing machine (TM) concept to a world machine (WM) concept which offers several new services for social groups, whole cities or countries. In the future there is no distinction between man and machine because there is a complete symbiotic unification because the machines have become an integral part of a personality, the extension of the body in some new way; probably far beyond the cyborg paradigm.
The basic creativity and freedom of biological life has been further developed in a fundamental all embracing spirituality of life in the universe which is targeting a re-creation of the whole universe by using the universe for the universe.
Who has followed the discussion in this blog remembers several different phases in the conceptual frameworks used here.
The first paradigm called Human-Computer Interface (HCI) has been only mentioned by historical reasons. The next phase Human-Machine Interaction (HMI) was the main paradigm in the beginning of my lecturing in 2005. Later, somewhere 2011/2012, I switched to the paradigm Actor-Actor Interaction (AAI) because I tried to generalize over the different participating machines, robots, smart interfaces, humans as well as animals. This worked quite nice and some time I thought that this is now the final formula. But reality is often different compared to our thinking. Many occasions showed up where the generalization beyond the human actor seemed to hide the real processes which are going on, especially I got the impression that very important factors rooted in the special human actor became invisible although they are playing decisive role in many processes. Another punch against the AAI view came from application scenarios during the last year when I started to deal with whole cities as actors. At the end I got the feeling that the more specialized expressions like Actor-Cognition Interaction (ACI) or Augmented Collective Intelligence (ACI) can indeed help to stress certain special properties better than the more abstract AAI acronym, but using structures like ACI within general theories and within complex computing environments it became clear that the more abstract acronym AAI is in the end more versatile and simplifies the general structures. ACI became a special sub-case
To understand this oscillation between AAI and ACI one has to look back into the history of Human Computer/ Machine Interaction, but not only until the end of the World War II, but into the more extended evolutionary history of mankind on this planet.
It is a widespread opinion under the researchers that the development of tools to help mastering material processes was one of the outstanding events which changed the path of the evolution a lot. A next step was the development of tools to support human cognition like scripture, numbers, mathematics, books, libraries etc. In this last case of cognitive tools the material of the cognitive tools was not the primary subject the processes but the cognitive contents, structures, even processes encoded by the material structures of the tools.
Only slowly mankind understood how the cognitive abilities and capabilities are rooted in the body, in the brain, and that the brain represents a rather complex biological machinery which enables a huge amount of cognitive functions, often interacting with each other; these cognitive functions show in the light of observable behavior clear limits with regard to the amount of features which can be processed in some time interval, with regard to precision, with regard to working interconnections, and more. And therefore it has been understood that the different kinds of cognitive tools are very important to support human thinking and to enforce it in some ways.
Only in the 20th century mankind was able to built a cognitive tool called computer which could show capabilities which resembled some human cognitive capabilities and which even surpassed human capabilities in some limited areas. Since then these machines have developed a lot (not by themselves but by the thinking and the engineering of humans!) and meanwhile the number and variety of capabilities where the computer seems to resemble a human person or surpasses human capabilities have extend in a way that it has become a common slang to talk about intelligent machines or smart devices.
While the original intention for the development of computers was to improve the cognitive tools with the intend to support human beings one can get today the impression as if the computer has turned into a goal on its own: the intelligent and then — as supposed — the super-intelligent computer appears now as the primary goal and mankind appears as some old relic which has to be surpassed soon.
As will be shown later in this text this vision of the computer surpassing mankind has some assumptions which are
What seems possible and what seems to be a promising roadmap into the future is a continuous step-wise enhancement of the biological structure of mankind which absorbs the modern computing technology by new cognitive interfaces which in turn presuppose new types of physical interfaces.
To give a precise definition of these new upcoming structures and functions is not yet possible, but to identify the actual driving factors as well as the exciting combinations of factors seems possible.
COGNITION EMBEDDED IN MATTER
The main idea is the shift of the focus away from the physical grounding of the interaction between actors looking instead more to the cognitive contents and processes, which shall be mediated by the physical conditions. Clearly the analysis of the physical conditions as well as the optimal design of these physical conditions is still a challenge and a task, but without a clear knowledge manifested in a clear model about the intended cognitive contents and processes one has not enough knowledge for the design of the physical layout.
SOLVING A PROBLEM
Thus the starting point of an engineering process is a group of people (the stakeholders (SH)) which identify some problem (P) in their environment and which have some minimal idea of a possible solution (S) for this problem. This can be commented by some non-functional requirements (NFRs) articulating some more general properties which shall hold through the whole solution (e.g. ‘being save’, ‘being barrier-free’, ‘being real-time’ etc.). If the description of the problem with a first intended solution including the NFRs contains at least one task (T) to be solved, minimal intended users (U) (here called executive actors (eA)), minimal intended assistive actors (aA) to assist the user in doing the task, as well as a description of the environment of the task to do, then the minimal ACI-Check can be passed and the ACI analysis process can be started.
COGNITION AND AUGMENTED COLLECTIVE INTELLIGENCE
If we talk about cognition then we think usually about cognitive processes in an individual person. But in the real world there is no cognition without an ongoing exchange between different individuals by communicative acts. Furthermore it has to be taken into account that the cognition of an individual person is in itself partitioned into two unequal parts: the unconscious part which covers about 99% of all the processes in the body and in the brain and about 1% which covers the conscious part. That an individual person can think somehow something this person has to trigger his own unconsciousness by stimuli to respond with some messages from his before unknown knowledge. Thus even an individual person alone has to organize a communication with his own unconsciousness to be able to have some conscious knowledge about its own unconscious knowledge. And because no individual person has at a certain point of time a clear knowledge of his unconscious knowledge the person even does not really know what to look for — if there is no event, not perception, no question and the like which triggers the person to interact with its unconscious knowledge (and experience) to get some messages from this unconscious machinery, which — as it seems — is working all the time.
On account of this logic of the individual internal communication with the individual cognition an external communication with the world and the manifested cognition of other persons appears as a possible enrichment in the interactions with the distributed knowledge in the different persons. While in the following approach it is assumed to represent the different knowledge responses in a common symbolic representation viewable (and hearable) from all participating persons it is growing up a possible picture of something which is generally more rich, having more facets than a picture generated by an individual person alone. Furthermore can such a procedure help all participants to synchronize their different knowledge fragments in a bigger picture and use it further on as their own picture, which in turn can trigger even more aspects out of the distributed unconscious knowledge.
If one organizes this collective triggering of distributed unconscious knowledge within a communication process not only by static symbolic models but beyond this with dynamic rules for changes, which can be interactively simulated or even played with defined states of interest then the effect of expanding the explicit and shared knowledge will be boosted even more.
From this background it makes some sense to turn the wording Actor-Cognition Interaction into the wording Augmented Collective Intelligence where Intelligence is the component of dynamic cognition in a system — here a human person –, Collective means that different individual person are sharing their unconscious knowledge by communicative interactions, and Augmented can be interpreted that one enhances, extends this sharing of knowledge by using new tools of modeling, simulation and gaming, which expands and intensifies the individual learning as well as the commonly shared opinions. For nearly all problems today this appears to be absolutely necessary.
ACI ANALYSIS PROCESS
Here it will be assumed that there exists a group of ACI experts which can supervise other actors (stakeholders, domain experts, …) in a process to analyze the problem P with the explicit goal of finding a satisfying solution (S+).
For the whole ACI analysis process an appropriate ACI software should be available to support the ACI experts as well as all the other domain experts.
In this ACI analysis process one can distinguish two main phases: (1) Construct an actor story (AS) which describes all intended states and intended changes within the actor story. (2) Make several tests of the actor story to exploit their explanatory power.
ACTOR STORY (AS)
The actor story describes all possible states (S) of the tasks (T) to be realized to reach intended goal states (S+). A mapping from one state to a follow-up state will be described by a change rule (X). Thus having start state (S0) and appropriate change rules one can construct the follow-up states from the actual state (S*) with the aid of the change rules. Formally this computation of the follow-up state (S’) will be computed by a simulator function (σ), written as: σ: S* x X —> S.
With the aid of an explicit actor story (AS) one can define the non-functional requirements (NFRs) in a way that it will become decidable whether a NFR is valid with regard to an actor story or not. In this case this test of being valid can be done as an automated verification process (AVP). Part of this test paradigm is the so-called oracle function (OF) where one can pose a question to the system and the system will answer the question with regard to all theoretically possible states without the necessity to run a (passive) simulation.
If the size of the group is large and it is important that all members of the group have a sufficient similar knowledge about the problem(s) in question (as it is the usual case in a city with different kinds of citizens) then is can be very helpful to enable interactive simulations or even games, which allow a more direct experience of the possible states and changes. Furthermore, because the participants can act according to their individual reflections and goals the process becomes highly uncertain and nearly unpredictable. Especially for these highly unpredictable processes can interactive simulations (and games) help to improve a common understanding of the involved factors and their effects. The difference between a normal interactive simulation and a game is given in the fact that a game has explicit win-states whereas the interactive simulations doesn’t. Explicit win-states can improve learning a lot.
The other interesting question is whether an actor story AS with a certain idea for an assistive actor (aA) is usable for the executive actors. This requires explicit measurements of the usability which in turn requires a clear norm of reference with which the behavior of an executive actor (eA) during a process can be compared. Usually is the actor Story as such the norm of reference with which the observable behavior of the executing actors will be compared. Thus for the measurement one needs real executive actors which represent the intended executive actors and one needs a physical realization of the intended assistive actors called mock-up. A mock-up is not yet the final implementation of the intended assistive actor but a physical entity which can show all important physical properties of the intended assistive actor in a way which allows a real test run. While in the past it has been assumed to be sufficient to test a test person only once it is here assumed that a test person has to be tested at least three times. This follows from the assumption that every executive (biological) actor is inherently a learning system. This implies that the test person will behave differently in different tests. The degree of changes can be a hint of the easiness and the learnability of the assistive actor.
If an appropriate ACI software is available then one can consider an actor story as a simple theory (ST) embracing a model (M) and a collection of rules (R) — ST(x) iff x = <M,R> –which can be used as a kind of a building block which in turn can be combined with other such building blocks resulting in a complex network of simple theories. If these simple theories are stored in a public available data base (like a library of theories) then one can built up in time a large knowledge base on their own.
This suggests that a symbiosis between creative humans and computing algorithms is an attractive pairing. For this we have to re-invent our official learning processes in schools and universities to train the next generation of humans in a more inspired and creative usage of algorithms in a game-like learning processes.
The overall context is given by the description of the Actor-Actor Interaction (AAI) paradigm as a whole. In this text the special relationship between engineering and the surrounding society is in the focus. And within this very broad and rich relationship the main interest lies in the ethical dimension here understood as those preferences of a society which are more supported than others. It is assumed that such preferences manifesting themselves in real actions within a space of many other options are pointing to hidden values which guide the decisions of the members of a society. Thus values are hypothetical constructs based on observable actions within a cognitively assumed space of possible alternatives. These cognitively represented possibilities are usually only given in a mixture of explicitly stated symbolic statements and different unconscious factors which are influencing the decisions which are causing the observable actions.
These assumptions represent until today not a common opinion and are not condensed in some theoretical text. Nevertheless I am using these assumptions here because they help to shed some light on the rather complex process of finding a real solution to a stated problem which is rooted in the cognitive space of the participants of the engineering process. To work with these assumptions in concrete development processes can support a further clarification of all these concepts.
ENGINEERING AND SOCIETY
DUAL: REAL AND COGNITIVE
As assumed in the AAI paradigm the engineering process is that process which connects the event of stating aproblem combined with a first vision of a solution with a final concrete working solution.
The main characteristic of such an engineering process is the dual character of a continuous interaction between the cognitive space of all participants of the process with real world objects, actions, and processes. The real world as such is a lose collection of real things, to some extend connected by regularities inherent in natural things, but the visions of possible states, possible different connections, possible new processes is bound to the cognitive space of biological actors, especially to humans as exemplars of the homo sapiens species.
Thus it is a major factor of training, learning, and education in general to see how the real world can be mappedinto some cognitive structures, how the cognitive structures can be transformed by cognitive operations into new structures and how these new cognitive structures can be re-mapped into the real world of bodies.
Within the cognitive dimension exists nearly infinite sets of possible alternatives, which all indicate possible states of a world, whose feasibility is more or less convincing. Limited by time and resources it is usually not possible to explore all these cognitively tapped spaces whether and how they work, what are possible side effects etc.
Somehow by nature, somehow by past experience biological system — like the home sapiens — have developed cultural procedures to induce preferences how one selects possible options, which one should be selected, under which circumstances and with even more constraints. In some situations these preferences can be helpful, in others they can hide possibilities which afterwards can be re-detected as being very valuable.
Thus every engineering process which starts a transformation process from some cognitively given point of view to a new cognitively point of view with a following up translation into some real thing is sharing its cognitive spacewith possible preferences of the cognitive space of the surrounding society.
It is an open case whether the engineers as the experts have an experimental, creative attitude to explore without dogmatic constraints the possible cognitive spaces to find new solutions which can improve life or not. If one assumes that there exist no absolute preferences on account of the substantially limit knowledge of mankind at every point of time and inferring from this fact the necessity to extend an actual knowledge further to enable the mastering of an open unknown future then the engineers will try to explore seriously all possibilities without constraints to extend the power of engineering deeper into the heart of the known as well as unknown universe.
EXPLORING COGNITIVE POSSIBILITIES
At the start one has only a rough description of the problem and a rough vision of a wanted solution which gives some direction for the search of an optimal solution. This direction represents also a kind of a preference what is wanted as the outcome of the process.
On account of the inherent duality of human thinking and communication embracing the cognitive space as well as the realm of real things which both are connected by complex mappings realized by the brain which operates nearly completely unconscious a long process of concrete real and cognitive actions is necessary to materialize cognitive realities within a communication process. Main modes of materialization are the usage of symbolic languages, paintings (diagrams), physical models, algorithms for computation and simulations, and especially gaming (in several different modes).
As everybody can know these communication processes are not simple, can be a source of confusions, and the coordination of different brains with different cognitive spaces as well as different layouts of unconscious factors is a difficult and very demanding endeavor.
The communication mode gaming is of a special interest here because it is one of the oldest and most natural modes to learn but in the official education processes in schools and universities (and in companies) it was until recently not part of the official curricula. But it is the only mode where one can exercise the dimensions of preferences explicitly in combination with an exploring process and — if one wants — with the explicit social dimension of having more than one brain involved.
In the last about 50 – 100 years the term project has gained more and more acceptance and indeed the organization of projects resembles a game but it is usually handled as a hierarchical, constraints-driven process where creativity and concurrent developing (= gaming) is not a main topic. Even if companies allow concurrent development teams these teams are cognitively separated and the implicit cognitive structures are black boxes which can not be evaluated as such.
In the presupposed AAI paradigm here the open creative space has a high priority to increase the chance for innovation. Innovation is the most valuable property in face of an unknown future!
While the open space for a real creativity has to be executed in all the mentioned modes of communication the final gaming mode is of special importance. To enable a gaming process one has explicitly to define explicit win-lose states. This objectifies values/ preferences hidden in the cognitive space before. Such an objectification makes things transparent, enables more rationality and allows the explicit testing of these defined win-lose states as feasible or not. Only tested hypothesis represent tested empirical knowledge. And because in a gaming mode whole groups or even all members of a social network can participate in a learning process of the functioning and possible outcome of a presented solution everybody can be included. This implies a common sharing of experience and knowledge which simplifies the communication and therefore the coordination of the different brains with their unconsciousness a lot.
TESTING AND EVALUATION
Testing a proposed solution is another expression for measuring the solution. Measuring is understood here as a basic comparison between the target to be measured (here the proposed solution) and the before agreednorm which shall be used as point of reference for the comparison.
But what can be a before agreed norm?
Some aspects can be mentioned here:
First of all there is the proposed solution as such, which is here a proposal for a possible assistive actor in an assumed environment for some intended executive actors which has to fulfill some job (task).
Part of this proposed solution are given constraints and non-functional requirements.
Part of this proposed solution are some preferences as win-lose states which have to be reached.
Another difficult to define factor are the executive actors if they are biological systems. Biological systems with their basic built in ability to act free, to be learning systems, and this associated with a not-definable large unconscious realm.
Given the explicit preferences constrained by many assumptions one can test only, whether the invited test persons understood as possible instances of the intended executive actors are able to fulfill the defined task(s) in some predefined amount of time within an allowed threshold of making errors with an expected percentage of solved sub-tasks together with a sufficient subjective satisfaction with the whole process.
But because biological executive actors are learning systems they will behave in different repeated tests differently, they can furthermore change their motivations and their interests, they can change their emotional commitment, and because of their built-in basic freedom to act there can be no 100% probability that they will act at time t as they have acted all the time before.
Thus for all kinds of jobs where the process is more or less fixed, where nothing new will happen, the participation of biological executive actors in such a process is questionable. It seems (hypothesis), that biological executing actors are better placed in jobs where there is some minimal rate of curiosity, of innovation, and of creativity combined with learning.
If this hypothesis is empirically sound (as it seems), then all jobs where human persons are involved should have more the character of games then something else.
It is an interesting side note that the actual research in robotics under the label of developmental robotics is struck by the problem how one can make robots continuously learning following interesting preferences. Given a preference an algorithm can work — under certain circumstances — often better than a human person to find an optimal solution, but lacking such a preference the algorithm is lost. And actually there exists not the faintest idea how algorithms should acquire that kind of preferences which are interesting and important for an unknown future.
On the contrary, humans are known to be creative, innovative, detecting new preferences etc. but they have only limited capacities to explore these creative findings until some telling endpoint.
This suggests that a symbiosis between creative humans and computing algorithms is an attractive pairing. For this we have to re-invent our official learning processes in schools and universities to train the next generation of humans in a more inspired and creative usage of algorithms in a game-like learning processes.
The context for this text is the whole block dedicated to the AAI (Actor-Actor Interaction) paradigm. The aim of this text is to give the big picture of all dimensions and components of this subject as it shows up during April 2019.
The first dimension introduced is the historical dimension, because this allows a first orientation in the course of events which lead to the actual situation. It starts with the early days of real computers in the thirties and forties of the 20 century.
The second dimension is the engineering dimension which describes the special view within which we are looking onto the overall topic of interactions between human persons and computers (or machines or technology or society). We are interested how to transform a given problem into a valuable solution in a methodological sound way called engineering.
The third dimension is the whole of society because engineering happens always as some process within a society. Society provides the resources which can be used and spends the preferences (values) what is understood as ‘valuable’, as ‘good’.
The fourth dimension is Philosophy as that kind of thinking which takes everything into account which can be thought and within thinking Philosophy clarifies conditions of thinking, possible tools of thinking and has to clarify when some symbolic expression becomes true.
In history we are looking back in the course of events. And this looking back is in a first step guided by the concepts of HCI (Human-Computer Interface) and HMI (Human-Machine Interaction).
It is an interesting phenomenon how the original focus of the interface between human persons and the early computers shifted to the more general picture of interaction because the computer as machine developed rapidly on account of the rapid development of the enabling hardware (HW) the enabling software (SW).
Within the general framework of hardware and software the so-called artificial intelligence (AI) developed first as a sub-topic on its own. Since the last 10 – 20 years it became in a way productive that it now seems to become a normal part of every kind of software. Software and smart software seem to be interchangeable. Thus the new wording of augmented or collective intelligence is emerging intending to bridge the possible gap between humans with their human intelligence and machine intelligence. There is some motivation from the side of society not to allow the impression that the smart (intelligent) machines will replace some day the humans. Instead one is propagating the vision of a new collective shape of intelligence where human and machine intelligence allows a symbiosis where each side gives hist best and receives a maximum in a win-win situation.
What is revealing about the actual situation is the fact that the mainstream is always talking about intelligence but not seriously about learning! Intelligence is by its roots a static concept representing some capabilities at a certain point of time, while learning is the more general dynamic concept that a system can change its behavior depending from actual external stimuli as well as internal states. And such a change includes real changes of some of its internal states. Intelligence does not communicate this dynamics! The most demanding aspect of learning is the need for preferences. Without preferences learning is impossible. Today machine learning is a very weak example of learning because the question of preferences is not a real topic there. One assumes that some reward is available, but one does not really investigate this topic. The rare research trying to do this job is stating that there is not the faintest idea around how a general continuous learning could happen. Human society is of no help for this problem while human societies have a clash of many, often opposite, values, and they have no commonly accepted view how to improve this situation.
Engineering is the art and the science to transform a given problem into a valuable and working solution. What is valuable decides the surrounding enabling society and this judgment can change during the course of time. Whether some solution is judged to be working can change during the course of time too but the criteria used for this judgment are more stable because of their adherence to concrete capabilities of technical solutions.
While engineering was and is always a kind of an art and needs such aspects like creativity, innovation, intuition etc. it is also and as far as possible a procedure driven by defined methods how to do things, and these methods are as far as possible backed up by scientific theories. The real engineer therefore synthesizes art, technology and science in a unique way which can not completely be learned in the schools.
In the past as well as in the present engineering has to happen in teams of many, often many thousands or even more, people which coordinate their brains by communication which enables in the individual brains some kind of understanding, of emerging world pictures, which in turn guide the perception, the decisions, and the concrete behavior of everybody. And these cognitive processes are embedded — in every individual team member — in mixtures of desires, emotions, as well as motivations, which can support the cognitive processes or obstruct them. Therefore an optimal result can only be reached if the communication serves all necessary cognitive processes and the interactions between the team members enable the necessary constructive desires, emotions, and motivations.
If an engineering process is done by a small group of dedicated experts — usually triggered by the given problem of an individual stakeholder — this can work well for many situations. It has the flavor of a so-called top-down approach. If the engineering deals with states of affairs where different kinds of people, citizens of some town etc. are affected by the results of such a process, the restriction to a small group of experts can become highly counterproductive. In those cases of a widespread interest it seems promising to include representatives of all the involved persons into the executing team to recognize their experiences and their kinds of preferences. This has to be done in a way which is understandable and appreciative, showing esteem for the others. This manner of extending the team of usual experts by situative experts can be termed bottom-up approach. In this usage of the term bottom-up this is not the opposite to top-down but is reflecting the extend in which members of a society are included insofar they are affected by the results of a process.
Societies in the past and the present occur in a great variety of value systems, organizational structures, systems of power etc. Engineering processes within a society are depending completely on the available resources of a society and of its value systems.
The population dynamics, the needs and wishes of the people, the real territories, the climate, housing, traffic, and many different things are constantly producing demands to be solved if life shall be able and continue during the course of time.
The self-understanding and the self-management of societies is crucial for their ability to used engineering to improve life. This deserves communication and education to a sufficient extend, appropriate public rules of management, otherwise the necessary understanding and the freedom to act is lacking to use engineering in the right way.
Without communication no common constructive process can happen. Communication happens according to many implicit rules compressed in the formula who when can speak how about what with whom etc. Communication enables cognitive processes of for instance understanding, explanations, lines of arguments. Especially important for survival is the ability to make true descriptions and the ability to decide whether a statement is true or not. Without this basic ability communication will break down, coordination will break down, life will break down.
The basic discipline to clarify the rules and conditions of true communication, of cognition in general, is called Philosophy. All the more modern empirical disciplines are specializations of the general scope of Philosophy and it is Philosophy which integrates all the special disciplines in one, coherent framework (this is the ideal; actually we are far from this ideal).
Thus to describe the process of engineering driven by different kinds of actors which are coordinating themselves by communication is primarily the task of philosophy with all their sub-disciplines.
Thus some of the topics of Philosophy are language, text, theory, verification of a theory, functions within theories as algorithms, computation in general, inferences of true statements from given theories, and the like.
In this text I apply Philosophy as far as necessary. Especially I am introducing a new process model extending the classical systems engineering approach by including the driving actors explicitly in the formal representation of the process. Learning machines are included as standard tools to improve human thinking and communication. You can name this Augmented Social Learning Systems (ASLS). Compared to the wording Augmented Intelligence (AI) (as used for instance by the IBM marketing) the ASLS concept stresses that the primary point of reference are the biological systems which created and create machine intelligence as a new tool to enhance biological intelligence as part of biological learning systems. Compared to the wording Collective Intelligence (CI) (as propagated by the MIT, especially by Thomas W.Malone and colleagues) the spirit of the CI concept seems to be similar, but perhaps only a weak similarity.