A challenge in A(G)I, cybernetics revived in the Ouroboros Model as

A challenge in A(G)I, cybernetics revived in the

Ouroboros Model as one algorithm for all thinking

Knud Thomsen

Paul Scherrer Institute, Villigen and Würenlingen, Switzerland

E-mail: [email protected].

Abstract: A topical challenge for algorithms in general and for automatic image

categorization and generation in particular is presented in the form of a drawing for AI to

“understand”. In a second vein, AI is challenged to produce something similar from verbal

description. The aim of the paper is to highlight strengths and deficiencies of current

Artificial Intelligence approaches while coarsely sketching a way forward. A general lack of

encompassing symbol-embedding and (not only) -grounding in some bodily basis is made

responsible for current deficiencies. A concomitant dearth of hierarchical organization of

concepts follows suite. As a remedy for these shortcomings, it is proposed to take a wide step

back and to newly incorporate aspects of cybernetics and analog control processes. It is

claimed that a promising overarching perspective is provided by the Ouroboros Model with

a valid and versatile algorithmic backbone for general cognition at all accessible levels of

abstraction and capabilities. Reality, rules, truth, and Free Will are all useful abstractions

according to the Ouroboros Model. Logic deduction as well as intuitive guesses are claimed

as produced on the basis of one compartmentalized memory for schemata and a pattern-

matching, i.e., monitoring process termed consumption analysis. The latter directs attention

on short (attention proper) and also on long times scales (emotional biases). In this cybernetic

approach, discrepancies between expectations and actual activations (e.g., sensory precepts)

drive the general process of cognition and at the same time steer the storage of new and

adapted memory entries. Dedicated structures in the human brain work in concert according

to this scheme.

Keywords: AI-challenge; large language models; cybernetics; synergetics; common sense;

consciousness; Free Will

1. Introduction

Algorithms are everywhere, and (almost) everyone becomes increasingly aware of that now.

Despite breathtaking recent advances in the demonstrated performance of Artificial

Intelligence (AI), in particular of Large Language Models (LLMs), there are many prominent

voices pointing out undeniable fundamental shortcomings of even the most powerful current

approaches and programs [1–3]. On the entry page to ChatGPT, for example, there is a

disclaimer acknowledging that ChatGPT “may occasionally generate incorrect information,

may occasionally produce harmful instructions or biased content, and possesses limited

knowledge of world and events after 2021” [4]. These statements could very easily also pass

for a description of a human interlocutor; nothing special if many humans would not often

dream of machine intelligence as always correct, unbiased, and sharp to name just a few

positive attributes.

2. A Challenge to AI

In Figure 1, the challenge is to correctly classify that sketchy drawing as a whole, one

“piece of art”, and its interpretation comprising all aspects together. In a second,

complementary, demand, an example AI is challenged to generate a similar image from a

description. Some results of “snapshot-experiments” at different times are presented in

Appendix A at the end of the paper.

Figure 1. The original title of the drawing is “trollet og sitt hjem”, in English: ‘the troll

and his home’.

3. Proposal for a more comprehensive approach

Given undisputed shortcomings and deficiencies of current AI as widely recognized and also

cursorily documented in the Appendix A, a potential alternative approach is presented under

the name of Ouroboros Model. At some point in time, i.e., at some level of sophistication and

autonomy (demanding some self-awareness), AI agents will want to have their voices heard;

very brief conversations with ChatGPT are presented in Appendix B. In a somehow self-

explicatory manner, it is attempted in the following to develop the arguments in an iterative

and self-reflective way. This meta-perspective is further explained in Appendix C. Evidence

for sketching an outline is drawn from a very wide range. It is self-consistently argued that

in in order to follow an efficient way to progress this simply has to come as a first step before

delving into any detailed scrutiny.

The Ouroboros Model

The Ouroboros Model has been proposed as a general blueprint for cognition [5,10]. It

features only two basic ingredients: a memory structed in a (non-strict) hierarchy and a

process called consumption analysis. The working of that underlying fundamental algorithm

can be understood as a version of proportional control in disguise.

In a tiny nutshell: at one point in time, with a set of schemata available right then, an

agent matches (sensory) input to these schemata and the one, which is fitting best, is selected.

Comparing material and mold, there will most likely remain some discrepancies like features

not assigned or subsumed/consumed as well as slots staying empty. On a short time-scale,

attention will be directed towards exactly those attributes with the aim of improving the

overall fit; – this is nothing else than a control loop geared at minimizing discrepancies

between any current input and expectations based on earlier established knowledge. It has

been claimed that with the right interpretation of existing schemata and incoming new

sensory percepts, consumption analysis can be understood as an approximate implementation

of Bayesian belief update [5].

Bayesian accounts in this context can explain more nuances than immediately evident.

What does look like a simple conjunction fallacy, might turn out to be quite reasonable and

rational when taking all circumstances into proper consideration [6]. Similarly, temporal

discounting can be a wise attitude when normally dealing with a fundamentally uncertain and

insecure world.

Connections to evolution can be drawn at minimum two levels; on a fundamental one,

the genesis of creatures and their adaptations and capabilities follows a very similar path of

emergence on demand with selection according to fit and usefulness [7]. Working out a

formal mapping between evolution and Bayesian reasoning offers another related vein, and

in “niche-construction”, these two seem to merge seamlessly [8]. As Immanuel Kant already

knew, pre-established concepts and constraints have a huge impact on what one can perceive,

understand, and do, – and the other way round, i.e., something like: “the conditions of the

possibilities to experience objects are at the same time the conditions for the possible objects

of this experience” [9].

Some more details on the working of the Ouroboros Model have been presented in a

series of papers [10–12]. Here, aspects demonstrating its correspondence to analog control

are shortly highlighted. It is important to stress that only an outline can be presented while

not diminishing the role of due digital or formal implementations of the architecture. On the

contrary, this policy only bears witness to the fully self-consistent approach advocated:

beginning with an overall (approximate) schema, highlighting slots, which are deemed

relevant and (partly) empty or discrepant. Adaptations often will materialize during the

process of iterative filling-in; – for static input, and even more so when there are significant

variations over time. In cases where massive changes are necessary, new schemata will be

created [11]. Additionally, repetitions and similarities will lead to the grinding-in (and the

abstraction) of proven useful concepts, structures, and procedures. Some measure of

regularity seems indispensable. In extension of the well-known anthropic principle beyond

cosmology, no overwhelming chaos could ever be a cradle of cognition; rather, only

behavior, which can be captured by rules, can actually develop and prevail [7]. Without a

minimum of stability and repetition, neither life nor human observers with sophisticated

mental structures could ever evolve.

Both, analog and digital, characteristics can neatly work together; this has been dealt

with in a dedicated paper [12]. Dichotomies are seen as a first step towards organizing some

apparent tangle into distinguishable parts. With growing differentiation and understanding of

dependencies, finer nuances become discernable, rendering the original b/w dichotomy a

crude approximation. Nice to observe, even in the (currently standard) implementations,

when purportedly intelligent behavior is simulated on digital computers, countless weights

of synapses in large artificial neural networks are carefully tuned during extensive training

to establish finely grained and thus basically analog connections.

In the light of the Ouroboros Model, “art” can quite generally be characterized as having

added something novel to standard schemata and expectations, plus, especially, also

weakening some selected correspondence(s) with “a real natural thing” in some type of (re-)

presentation, i.e., intentionally discarding some features and dimensions from what would

“naturally” belong to an “everyday” entity while staying consistent to some extent (even if

fully realistic, a painting of a scene, e.g., is not the “prime reality” of the original scene itself).

It follows that a certain level of intended discrepancy to known natural mental models is

required for any artifact to count as eligible as a piece of art, and reality monitoring is in a

special and (partly) suspended mode for full appreciation.

From this perspective, AI in its beginnings started as an artistic project. Not only in

hindsight it is clear that important aspects were and still are missing. The 1956 Dartmouth

Workshop, widely considered foundational for the field of AI, did not include any substantial

contribution by Norbert Wiener, then certainly one of the most qualified experts on control,

automata, communication in animals and machines [13]. Maybe, this omission or rejection

was a wise decision at that particular point in time given the then prevailing constellation.

This does not mean that it makes sense to continue neglecting a possibly major source of

inspiration.

Now, it is certainly appropriate trying to rectify some historical neglection or animosity

and look what cybernetics might have to contribute facing the present-day obstacles for

achieving human level general intelligence, especially from the point of view of efficient

algorithms, as is claimed here.

4. Cybernetics reloaded

Stripped to its bare essentials, cybernetics deals with control [14]. It starts with simple

biological and analogical mechanisms to keep certain features, e.g., the concentrations of

some nutrients, within acceptable bounds, and it reaches up to the steering of complex

reactions taking into consideration many different attributes and distinct levels of

organization. Complexity increases in particular when feedback pertaining to the controlling

system itself is taken into account. Herman Haken’s Synergetics describes sophisticated

extensions to the basic layout and also Nicolai Hartmann’s ontological theory [15,16].

Emergence does happen, and it can explain a lot. It has recently even been claimed that

impressive emergent capabilities have (already) been exhibited by LLMs [17].

At any one level, consumption analysis highlights specific discrepancies between an

existing schema and actual content, which are determined in a matching / monitoring process.

The results are used on different time scales for direct further search or action, and for setting

a longer lasting affective tone of a situation and entailing a suitable bias for an agent [10].

Seeing the first as analogue to the operation of a simple (linear) control algorithm is

straightforward. The immediately following question then is, what about more intricate

control systems like PID [18]? For autonomous systems, the necessity of anticipations has to

be emphasized; it enables living (moving) beings to prepare answers proactively even before

a predicted demand fully materializes. A detailed overall account goes beyond what can be

delivered in this short sketch; this is posed as another challenge to AI (early anticipation

surely is more relating to differentiation while bias to integration).

Following the Ouroboros Model, some form of equilibrium and adequacy (non-

disturbing deviations from appropriate set-points) are the core goals at all levels from

physiological needs, to bodily sensations, to abstract argumentations, e.g., in law suits, or the

delivery of “permissible” weapons to a country under attack, to Free Will.

Adequacy itself is also an abstraction, and it is context dependent as epitomized in Carl

Sagan’s emblematic adage: “extraordinary claims require extraordinary evidence”. This was

evident to thinkers long before as expressed in the principle of Laplace: “The weight of the

evidence should be proportioned to the strangeness of the facts” [19]. An appeal to

“proportionality” is often issued in (political) discussions, and as some last resort when

humans are confronted with atrocities and “just” measures of retaliation.

According to the Ouroboros Model, the potential mental processing power of an agent

is ground-laid in the available knowledge, i.e., the number, complexity, and elaboration of

the concepts at her disposal. Schemata, their number of slots, the level of detail, the depth of

hierarchies, degree of connection and interdependence of the building blocks, and the width,

i.e., the extent of some main schemata and their total coverage from the grounding level to

the most abstract summits, determine what can be thought of efficiently; (quasi-)global

adequacy, coherence, and consistency are crucial. Sheer performance at a single point in time

arises as a result of the optimum interplay between these structured data and the effective

execution of the described processing steps, in particular, self-referential consumption

analysis.

With schemata as clearly distinct entities, “compartmentalization” stands in stark

contrast to a rather indiscriminate associationism, which seemingly still lies at the core of the

current big artificial neural networks. Compartmentalization provides the very basis for

effective monitoring and meaningful error checking. Not only what is normally part of a

given schema is specified but also what definitively (at that point in time) does not belong to

it. Observed on a meta-level, the absence of an expected signal or feature is a valid feature in

its own right.

Negation is a tricky concept/operation, for humans and even more so for current AI [20].

A “not-tag” is attached (as positive information) to a (major) constituent feature of a schema,

and this allows for a lot of ambiguity. Recent improvements in chatbots in this respect can

be traced to feedback from human instructors during training [21]. In general, in the real

world, no unique “opposites” are well-defined. No straightforward nor very meaningful

“tertium non datur” can therefore be expected in interesting contexts.

A knowledge cut-off, i.e., a pretrained model knowing only of training material up to a

certain point, severely limits the usefulness of LLMs and can lead to hallucinations. It comes

as no surprise that cutting-edge attempts of improving on the performance of ChatGPT, in

particular with respect to truthfulness, employ human feedback for reinforcement learning

and using learned thresholds as a proxy for an “oracle” [21]. The reward model is trained

with supervised learning and a relative ranking process for answers in a specific context by

humans. Consumption analysis, sometimes harnessing human (corrective) input, intrinsically

delivers something of that sort.

Like with any type of alphabet, versatile building blocks enable very efficient stepwise

construction of almost infinite compositions, harnessing incremental and possibly nested

procedures [22]. “Anchoring” at solidly verified specific points certainly is a good idea in

principle; considerations can go astray when nothing of true relevance is available, and

anchoring can then distort all kinds of (human and AI) actions. Overtly reporting external

sources and associated argumentations certainly increases transparency and acceptability of

and for any actor.

Quality-checked building blocks can constrain any construction and prevent going too

far astray. They can provide some transparency as confirmed memory entries make it also

possible to verify well described facts, like the details of the death of Otto Selz, e.g., by just

looking that up in Wikipedia (quality-controlled by humans (which does not guarantee being

100 percent correct either); ChatGPT obviously did not do that, and got it wrong when asked

about Otto Selz in February 2023).

Considering alternatives and, linked, push-pull processes make up a central part of an

overall cybernetic conception. Beyond the most basic on/off controllers, there are many

variants conceivable and necessary for adequate and fine control. Mismatches can be minor

and negligible with almost no impact or change of action needed, or, they can be so

fundamental that an ongoing activity has to be immediately terminated and some better

alternative has to be found. These switching points are determined by active thresholds for

living beings, some ingrained over eons of evolution in the bodily hardware (e.g., reflexes)

while others are learned as results of prior occasions or observations during the course of

growth or unfolding action, e.g., as potential turning points in a sequence of steps. There

surely are some hard boundary conditions but determining appropriate thresholds itself often

is a recursive process (second-order consumption analysis).

No matter at what level of abstraction, in the end a situation, a fit, will be evaluated as

satisfactory or not satisfactory, with a wide range of intensity for a “feeling” of success or

failure. Abstracted in a meta-perspective, this is seen as the basis for the fundamental

concepts of “good” and “bad”. This dichotomy is intrinsically linked to survival and

evolution, thus truly foundationally imprinted and subsequently overshadowing in a sense

everything and all the time, every percept or action of living beings including humans.

5. Common sense and understanding

What is meant in Figure 1 is immediately clear to humans ̶ even before learning of the title ̶

when looking at the picture in landscape and in portrait orientation: a drawing of a Norwegian

landscape with a small fjord between steep mountains / an ugly face with a prominent nose.

Humans do not see anything ambiguous in Figure 1; two distinct interpretations are fully

valid when viewed separately, and there is a common one as implied by the title. (As an aside,

it would be interesting to carefully test his presumption on a wider statistical basis with

human subjects with different backgrounds.) Embedding and first-hand symbol grounding

on a most basic bodily level are absent in the probed AI [1–3,22]. Some “sideways” as well

as, similarly, “upwards” connections, i.e., linking percepts to medium or highly abstract level

concepts appear missing.

Humans often have some idea what an appropriate answer to a question could be, and,

especially, what could be ruled out, even if they do not know any well-proven answer. This

is a manifestation of common sense, accompanied by a gut-feeling, an intuition, how hard a

problem is and how to solve it (or not). The global monitoring signal of consumption analysis

yields values for the goodness of fit with experience (on all levels of sophistication) ranging

from: fully accepted solution available to completely impossible, or: no idea. Even the latter

constitutes valuable information in itself for subsequent activities, also in cases when it only

tells to forget about trying and to safe energy. ChatGPT developers had to resort to human

teachers to have their system learn to say “I do not know” [21]. Transparently declaring an

impasse most often is much better than filling-in some superficially fitting content, as was

obviously the case when first asking ChatGPT about Otto Selz. Common sense, according to

the Ouroboros Model, in any case quickly yields and works first with something equivalent

to a patchy sketch or draft (“Schematische Anticipation” in the words of Otto Selz [23,24]).

The Ouroboros Model, stiving for a most comprehensive picture, self-consistently relies

on self-reflective iterative procedures for incremental self-steered growth. For a deliberate

sketch, (initially) detailed matches cannot be expected and are neither demanded to start with.

The combination of many different approaches and facets self-reflectively seems most

promising. Deeper understanding and better explanations can be visualized as meaning a

bigger “diameter” of a loop from bottom to top and back in the edifice of connected and

interwoven schemata. Large loopholes in that web of concepts on the other hand side cannot

be tolerated for any truly convincing account; in the picture of a net this would mean some

small (enough) mesh size. In all circumstances, an acceptable explanation has to encompass

an appropriate minimum diameter.

The elegance of a theory is determined by the clarity of the underlying assumptions, and

it raises when only very few are required as its foundation. On the other side, if all arguments

for an explanation rest on one basic element, highest caution is advised at the very least.

Anything can be “explained” when some basic key concepts are tailor-made for that purpose.

A falsifiable model with solid grounding, wide embedding, transparent structures, and causal

mechanisms is much more difficult (and valuable). It goes without saying that a (meaningful)

precision for a fit is of paramount importance; i.e., the best (one-size-fits-all) explanation is

no explanation when there are too many open ends.

Any conclusion, simple percept or resulting from sophisticated considerations, gains

much credibility when there are multiple and independent paths leading to that result; the

higher their diversity and when starting from different venture points, the better. This is an

argument for beginning with a big variety of views for any discussion, a plea for plurality is

the natural conclusion [25].

So, what about “understanding”? In the light of the Ouroboros Model, something is

understood only if there is a complete model taking care of all (essential) aspects. In shades

of gray, a certain minimum correspondence between “reality” and the “mental model” is

demanded containing (the most important) (in-depth) details of structures and processes. In

any case, mere replication is not sufficient. Not to offend logical rules, just the same as simple

rewording, does not suffice. In chemistry for example this might mean different synthesis-

paths to arrive at the same final compound.

As a trivial corollary, solipsism falls flat, at least in this common-sense perspective as

sketched above. The same applies to a modern version, the “simulation hypothesis” [26].

Except for very well-regulated cases like formal logics in finite domains, a principal

uncertainty of what really belongs to every question is inevitable, and no general eternally

correct and valid answers can be expected.

Rules are abstractions, and their projection back to any specific single case is by no

means guaranteed to be really meaningful.

Compartmentalization, which strongly limits the applicable content at a particular point

in time, often enforces a trade-off.

Considering all possibly relevant features is the best one can aim for. In interesting cases,

there most often is no external assurance whether something is sufficient for a predetermined

level of fit or certainty. On the other hand, demanding a minimum of relevancy guards against

the problem of “tacking by conjunction”, which plagues simple orthodox Bayesian

confirmation theory [27,28].

It goes without saying, hidden gaps in chains of reasoning should be avoided (but

sometimes, we just do not know better). Openly admitting some lack of convincing

supportive evidence certainly is better than confabulating hallucinations. There undoubtedly

are (e.g., religious or artistic) contexts, where a leap of faith is unavoidable, even a

fundamental requirement; this might not be to the taste of everyone. On the other side of the

coin, tautologies cannot tell much new, and they generally will not be considered interesting.

Leaving some open ends, at least hinting at them, and allowing for a minimum of

uncertainty, gives the necessary freedom for expansion or compromise, e.g., in case of

disputable arguments. Noisy inputs and fuzzy borders of concepts will in this sense help

easing transitions between related schemata (basins of attraction). Even if a threshold is not

really exceeded a transition process akin to quantum-mechanical tunneling can be enabled

by noise and some form of stochastic resonance.

If ready-made schemata are available, this allows for quick responses. Well-established

schemata absorb input readily thus focusing attention; no iterations or lengthy considerations

are required. On the other hand, lacking firm direct connections, links have to be iteratively

searched for, constructed, tried, scrutinized, and verified. Strongly exaggerating these

distinctions, diverse dual process models have been proposed [29].

Generally, full understanding demands a model, which covers all relevant levels of

features and concepts (schemata). Understanding in turn is the basis for correct and exact

anticipations. Anticipatory action asks for responses starting already at first signs of an event,

leading to earlier and earlier onsets of reactions as familiarity rises.

Schemata are the very basis of every understanding; they are the organizational building

blocks laid down in memory. In any case, for making a step (forward) some first foothold is

required. It is claimed that, except maybe in deep meditation, some vague schematic

preconception(s) will always be activated. Filling-in of slots and elaborations then are the

subsequent steps when triggered by an external or internal event. The first activated schema

might turn well out as not appropriately fitting. This can provoke minor updates or the

establishment of adapted or completely new concepts [11].

Edifices of thought can break down, i.e., when adding new information renders a first

mental model obsolete and another interpretation much more likely. This effect is used inter

alia in jokes [30].

Seen from a distance, inconsistencies with established prior knowledge (assumptions,

guesses) propel and fuel any development and improvement. The Ouroboros Model thus

sheds some light not only on the unavoidable occurrence of discrepancies but also on their

necessity for growth and all interesting positive development. This, again, holds true at all

levels, perfecting perceptions and movements, and also when dealing with some most

abstract questions as, e.g., concerning dualism and Free Will.

However ground-laying and important first guesses are, they might turn out problematic

or even wrong when more thoroughly scrutinized. A recently brought up example is the concept

of “fairness”. A plethora of meaningful and plausible definitions have been proposed [31]. When

attempting to strictly formalize these it has been found that three appealing and innocent

looking conditions cannot be met at the same time, except for rather trivial special cases;

trade-offs are inevitable, regardless whether it is humans or AI to decide [32]. This does not

really undermine that fairness can be seen as fundamental for justice [33,34]. It rather shows

that no external God-given standard is available, and for humans, the applicability of

whatever label or brand name can (and has to be) agreed upon in a specific context. At the

extremes, highest precision and useful flexibility are mutually excluding each other.

An interesting example concerning the utility of heuristics has been given relating to

exactly the very concept of heuristics; the fertility and huge impact of that conception can to

a good part be traced to its imperfectness and the persistent lack of any precise narrow

definition [35]. In the terminology of the Ouroboros Model, heuristics would correspond to

sketches where only selected and most eye-catching features are taken into account.

Abstraction and sketching can anticipate a frame, which later turns out to be of little

direct use, anticipations can lead astray. As an example, it simply would not tell anything

(except a lack of engineering background) if a philosopher could think of an airplane built

completely from lead; dreaming of zombies is the same (I maintain).

The Ouroboros Model confidently and proudly embraces functionalism, albeit not a

trivial (“one-dimensional”) version but one, which takes as many as possible (deemed

important) dimensions, aspects, and constituent conditions into self-reflective consideration.

Widest reaching consistency is the crucial criterion for learning and (considerate) action as

described above and in several papers before [10–12,22].

It has been hypothesized in different proposals that all mental processes can be captured

in sophisticated (production-)rules and relatively simple algorithms, which heavily rely on

iteration and recursion [36]. The Ouroboros Model explains linear if → rules as abstractions

from filling (remaining) slots in an otherwise well-defined schema (thus flexibly subsuming

production rules while dramatically boosting efficiency in general).

6. Brains, natural and artificial, consciousness

Especially in cases when there are powerful constraints, e.g., a preconceived convictions

lying unquestioned at the bottom, formalizing sometimes cannot help; there simply might be

no solution possible within that given frame. An example could be John Searle, who, when

discussing his famous Chinese room clings to “biological naturalism” and denies other than

biological hardware to possess the “causal powers” that permit the human experience of

consciousness [37,38]. John Searle in fact acts from an ideology, very comparably to what

he purports of supporters of functionalism. No doubt, nobody would mistake the Chinese

room for a human in a direct encounter; ̶ just thinking, e.g., of the time it would take to

receive any meaningful answer. Quality often arises from quantity. More is different; a single

molecule of water is not wet [15,16]. “The whole is something beyond the parts” was already

clear to Aristotle [39]. Other important and similarly decisive factors are speed and the

mastering of (nested) contexts, in particular, negation.

In terms of a neural implementation of the Ouroboro Model, it is hypothesized that cortex

(areas), hippocampus, and cerebellum are each specialized for specific tasks like memory or

action (bodily and mental movement) [40–42].

Simple if → then relations, which do not require any sophisticated consideration (e.g.,

reflexes.. habits), will be relegated to automatisms in the basal ganglia allowing very quick /

automatic reactions. Shortcuts will thus be implemented for often-used building blocks (e.g.,

movement schemata, like “assembler routines”). In bigger vertebrate brains, basal ganglia

are primarily seen as “driving” and “power-” stages of/for higher level cortical (and

cerebellar) areas steering effectors, controlling and regulating the processing in the diverse

structures. Most importantly, they modulate (not only cortex areas), enforce gains and

thresholds, e.g., relating to importance and speed. Synapses, which are marked for memory

entry, will be strengthened, and an “emergency stop” (“veto”) can effectively be realized. A

neural implementation of a sophisticated effector-algorithm for fast control, i.e., stopping,

has just recently been described [43]. Push-pull strategies for fine control of movements in

animals and humans appear to be employed ubiquitously [44,45].

A common misconception is that a “primitive brain” sits below an “advanced” cortex.

This is like calling (steering) wheels old and primitive as they also existed before modern

cars. Some basic functions need specific components, maybe with different details in

diversely adapted implementations. “Modern”, enlarged cortex volumes just add flexibility

by making more options available, for perception, action, thinking, and self-reflection.

Learning, according to the Ouroboros Model, is based on fast (“snap-shot”) and slow

(“grinding-in”) contributions [11]. Optimizations of connections do happen iteratively and

often incrementally. The simple idea in this respect is that there are two possible ways to

connect an input pattern with an output for tuning: one is backpropagation, and the other one

is recurrency, i.e., going the full circle a second time by reiterating the loop and processing

that (or similar) activation again (quickly and after some time at a second related occasion)

in the forward direction while taking into account all earlier results. Repeated runs can in

particular harness the global feedback signal from consumption analysis and also distinct

markings attached to specific components (slots, features, attributes,..). This can happen on

a fast timescale reinforcing recently successfully employed synapses, and over longer

timescales when positively tagged content is preferably integrated into long-time memory.

In addition to enabling efficient consumption analysis, clear and distinct

“compartmentalized” records (schemata) allow for efficient indexed storage and retrieval but

also require interpolation for meaningful use in real-world settings (most probably not only

in vertebrates) [40,41,46].

It has been argued that any agent, who has to take some responsibility for her own

functioning, e.g., caring for energy supply and avoiding errors and predators, at a certain

level of sophistication mandatorily has to consider “household parameters” pertaining to the

system itself. Subsequently, with the addition of some first basic intrinsic motivation to

“survive” (like all animals obviously have even long before they master much language) any

cognitive system will inevitably abstract/develop higher level aims and goals and a

rudimentary form of self [47,48]. This awareness can be understood as the roots of (self-)

consciousness. For this, details of implementation do not really matter, the most important

ingredient is self-reflectivity. Higher order personality activation (HOPA), a form of higher

order global state, of course, would be rather different for living beings and humans,

communities and organizations, and, especially, for artificial agents. While robots might be

somewhat closer to humans than pure software agents, the cognitive basis would be very

similar. HOPA includes the highest-level goals and values of an individual for the person

herself -- and for outside observers(!). Efforts to endow robots with self-awareness

harnessing inner speech are underway [49].

John Searle is right, the detailed intentions certainly would not be identical for humans

and artificial agents but in direct analogy they should be seen as equivalent with respect to

their fundamental importance for any particular individual [37,38].

Generally, features are of different relevance and centrality for different schemata.

Airplanes do not flap their wings like birds or butterflies; still, nobody doubts that they do

fly. Like no “élan vital” is required to principally understand biochemistry and life, no

fundamental difference is seen as to whether a living brain or silicon forms the hardware

substrate for cognition and self-reflection (likely except the attributions by others).

LLMs are built as rather plain artificial neural networks, i.e., statistical models, which

predict what objects (words, in this case) usually follow others in a sequence. The achieved

impressive performance can be attributed to the fact that human words are symbols for

concepts, which often stand for rich contents and bear significant meanings for humans (as

speakers and as listeners / actors and recipients). Recent transformer architectures effectively

include some type of top-down influence. Nothing mysterious, human children regularly

learn a language from their parents, and inner speech often is advantageously used by

children (and adults) when performing difficult tasks.

Further adding to the recognized power of inner speech, its provision has been proposed

recently to render the workings of robots easier to understand and trust. First tests show that

this is indeed appreciated by humans, and reported inner speech influences the participant’s

perceptions of a robot’s animacy and intelligence [49,50].

In ChatGPT, as in other current deep neural networks, obviously lacking explicit

sophisticated hierarchical structure, verified building blocks are apparently missing [51]. For

discriminating human users from machines, simple CAPTCHAs (still) can be used. It is

tempting to see a parallel with humans switching to a similar mode of confabulations without

the usual structure or constraints imposed on neural activations and connections by higher

level schemata when consuming mind-altering drugs like LSD [52] (trivial corollary:

CAPTCHAs become difficult for humans when intoxicated).

Humans as the examples of self-conscious beings closest to us personally, are embodied

in a particular way. The private experience of qualia of an individual has been claimed as

peculiar characteristic of humans (and other living beings).

According to the Ouroboros Model, qualia are abstractions, percepts of/for an individual

and linked to her body, which cannot be other than private to that healthy(!) individual. Still,

in exchange with others, similarities in perception and functioning (based amongst others on

the common heritage from biological and cultural evolution) allow agents to agree on shared

labels for individually experienced content.

No insurmountable difference to other self-monitoring and communicating agents is

visible (except when postulated at the outset). Human societies have developed a great many

diverse cultures, and yet, it is hard to imagine how these might be extended to fully embrace

AI, artificial agents. Developing an attitude towards artificial agents as expressed in Ubuntu

for humans among them might turn out difficult [53,54].

Most probably, individual agents acting in real-time in a dynamic world of whatever type

need intermittent off-line phases for “housekeeping”, i.e., consolidation of useful stuff and

discarding of inescapably accruing “data-garbage”, especially during sleep [55]. Most

interestingly, clever birds and octopuses not only sleep but also seem to dream similarly to

mammals despite (apparently) rather different brain lay-outs [46,56,57].

7. Reality and truth, Free Will

The number of alternatives, which are available for understanding a certain state of affairs

has been found to explain the convincing power of conditionals, counterfactual reasoning,

negation; e.g. [58]. The question then might be, quite generally, where this leaves reality and

truth. The upshot following the Ouroboros Model is that reality exists even when not

observed, but details are to some extent in the eye of the beholder. The existence of an

independent reality cannot independently be proved completely, i.e., from a truly outside

perspective. Laws of physics and whatever rules are abstracted from repeated successful

applications, and they tend to live a life of their own, knitting and sometimes also cutting

links to the special cases, which first allowed their distillation. According to the Ouroboros

Model, relevant and real is something, which has an unquestionable effect (– for somebody

in particular, but not always necessarily so); this, obviously, is also the basic stance of the

ethics experts, who compiled a very timely assessment of the challenges imposed by AI to

humans, e.g., writing about responsibility [59].

Most important here seems to be that humans necessarily grow up in some form of

community and society. There they learn, e.g., roles in their culture by being taught and also

by imitation, and they experience others and themselves as individuals. Explicit yes / no

reinforcement feedback is delivered, especially for important topics. After quite some

learning, humans ascribe and they are ascribed individual personality, subjectivity and also

Free Will. A large impact of external attribution can be seen from its reversal; undermining

the belief in Free Will made participants in a test feel more alienated from their true self, and

it lowered their self-perception of authenticity [60].

The most materialistic science known, i.e., physics, has taught us that reality appears

only fully real in a rather limited sphere (at least with a clear (preferably causal) connection

to observables) centered around values directly accessible to our senses. Experts and non-

experts discuss what “real” could mean in the foundational quantum realm, e.g., for the

concept of time; nothing is accepted as real without leaving some form of a trace, no clock is

a clock without some memory [61].

Free Will is real. It is real in the sense that this abstracted conception exerts very tangible

effects. It is amongst others foundational for an understanding of responsibility [59]. Directly

linking Free Will to lowest level substance categories means committing an error of

confounding and short-circuiting the appropriate very distinct levels [15,16].

“Free” commonly means not forced by foreign factors, it does not mean completely

indetermined nor random.

My will is free when I have a chance (i.e., sufficient resources and time) to consciously

weigh alternatives and when I can choose one option in the end without being forced to that

decision by obvious external circumstances or other compulsions. It is not some fundamental

determinism or chance, which blindly rule, but it is me who decides, i.e., I self-reflectively

take into proper account my values and goals, my motivations, my intentions, my experience

in my current situation, and so forth. My conscience and also my unconscious bodily and

mental basis are mine, personally, and I only go through some lengthy deliberations, when I

consider that demanded and the effort worthwhile.

A little bit of luck, additional (unexpected, also random) input can boost the freedom of

a decision when making more advantageous options available.

(Overall) consistency is the aim and the measure; consumption analysis is amongst

others an efficient way to implement a veto if I notice some important contradiction [47].

As there is no way to know all relevant factors in detail in advance (and most often

probably not even after the fact), free decisions are never fully predictable, -- not for oneself

before any thorough considerations and evaluations of options have been performed, and

even less so for an outsider.

Non-predictability is not the same as randomness. Any seemingly random action does

not mean at all that someone/something is “free”; non-predictability can result from

deterministic processes involving some fundamental limit or statistical uncertainty deeply

ingrained in a process.

There is no “absolute” freedom in the real world, and there cannot be; there are shades

of gray, and scale / level of abstraction do matter. Despite all necessary grounding, higher

levels of abstraction can and often do break free from (some of the) possibly tighter

restrictions effective at lower levels [15,16].

It has been argued that as soon as self-steered and self-reflective growth is accessible for

any agent, the predictability of her actions diminishes, and the actor thus also gains freedom

in her deeds (and omissions) [25,34]. This applies to humans when they grow up and it will

apply rather similarly to artificial autonomous agents. As with humans, the hope is that with

careful responsible “upbringing” and education any agent capable of self-reflection, self-

consciousness and some autonomy can be successfully directed to strive prudently for mutual

and common benefit [25,34].

The hope then is that truly intelligent AI will pay heed to a negative imperative (“given

an inescapably limited overall frame, violence has to be avoided as a result of reflected self-

interest”) as has been argued for prudent human beings.

8. Conclusion

Selected short experiments with clear results are collected in Appendix A. A general lack of

symbol grounding and common sense, which for humans is “naturally” given by their

embodiment, is identified as one reason for the non-convincing performance of even the most

powerful current approaches to Artificial (General) Intelligence; comprehensive embedding

and meaningfully considering highly abstract concepts also appears to be mostly missing. At

the same time, present-day AI approaches do not seem to be flexible or creative enough to

find two options or its full meaning if asked for an interpretation of the drawing in Figure 1.

This goes hand in hand with the wide absence of versatile building blocks, organized in some

sort of hierarchy of concepts and abstractions, and, claimed as a direct consequence, lack of

“common sense” reasoning [22].

Here, it has been attempted to coarsely sketch that the Ouroboros Model could offer an

overarching framework providing just that, which is now found to be still lacking in AI. Not

a full-fledged “theory of everything” can be presented at this time but an outline of how all

of the experienced and accessible reality can be perceived and in a sense thus functionally

established, and how basic cognitive processes could be understood on the basis of one truly

fundamental algorithm and its accompanying organizational (data-) structures; – a

contribution on a meta level, for everyone, applicable for all agents with a prime focus on

(self-)consistency, self-referral, and autocatalytic self-guided growth.

Including top-down guidance from the beginning, humans immediately grasp Figure 1

as a sketchy drawing. Setting the mind-frame to drawing and sketch, certain detailed features

lose their weight. On the foundations inherited over eons of evolution, for humans, directions

like up and down have their intrinsic importance; the action of gravity is something like a

pillar of common sense. So, with the activation of an abstract drawing-schema and tacitly

assuming what is up (and down), a landscape can be discerned as well an upright face in

landscape and portrait view, respectively. The two separate interpretations in isolation are

mutually exclusive. Adding some cue in form of the title allows completing the categorization

of that tangle of lines as one (maybe strange) drawing depicting two different but related

things at the same time but from different points of view.

This clearly is beyond present-day capabilities of the tested AI.

So, are current programs truly intelligent? ChatGPT almost passes a Turing Test [62].

To some extent vindicating John Searle, participants based their judgements primarily on

linguistic style and socio-emotional traits, not on demonstrated “intelligence”. The concept

of intelligence is a multi-facetted one [63], and to the author it looks like the various attempts

almost all are putting too little emphasis on unexpectedness, novelty and creativity to be

exhibited in the effective behavior of any purportedly intelligent agent.

Still, from an unbiased perspective, convincing mental functions will have to be

conceded to artificial agents, at least some day in the not-too-distant future. The only

remaining “position for retreat”, i.e., for human uniqueness, then is to insist on the “whole

bodily package”: from birth to grave and with neurons in flesh (and homo sapiens brain lay-out).

The lack of updating and continually enlarging the knowledge base as demonstrated by

identical answers to the same questions posed some time apart is another fundamental

shortcoming of easily accessible AI as ChatGPT or DALL·E.

Currently, a good part of the impressive capabilities of LLMs can still be attributed to

the mirroring of the intelligence and creativity of the interviewer [3]. Using these systems

now, one can get the most out of them when ideas and structure are provided by clever

prompts eliciting a stepwise incremental “chain-of-thought” process [64]. In addition, ethical

standards are reported to be easier met when explicitly telling a chatbot to avoid prejudices

and stereotypes for moral self-correction [65].

Looking at the easily obtainable results, the performance currently demonstrated by AI

can be taken as an indication of what humans normally do (i.e., pattern matching and acting

without much consciousness involved). Grandious and deep thinking turns out to be an

exceptional activity, which is (if at all) only endeavored in cases when rather simple reaction

defaults or heuristics do not immediately yield satisfactory results.

Some disquieting take-home message then is how easy it seems in fact already now to

approximate a human level of proficiency in different specific behaviors, which recently were

(and partly still are) considered human specialties. This will be widely felt as an affront to

humans prompting all kinds of fears [25]. Serious competition in language skills and art

seemingly hurts human egos much more than succumbing to machines in the games of chess

or GO.

So, what is the immediate true danger of AI and of LLMs in particular? Propaganda and

lies exist since ages wherever there is some form of communication, between humans,

animals, and even many plants entice by displaying illusory blooms.

It seems that one can rightly be afraid of LMM technology making it much easier for

anybody to produce and widely distribute misleading information, which sounds true and

stemming from a trustworthy competent source, even when only endowed with a minor mind

and low budget. Ease of access thus is one decisive point, no expensive advertising agency

or lawyers needed; another is speed (as AI systems almost always are much faster than

humans). Telling “I do not know” will soon be complemented and eventually be replaced by

an ability to look something up and to learn quickly.

Humans might be very worried when AI start to jokingly tell and to conceal(!) that they

have fun and pleasure filling their batteries, are proud of producing certain text, drawings and

other actions and gaining rewards; – this might be the point when the striving for more

positive feelings self-prompts an AI agent to prioritizing her own continued existence

(“survival”) over externally given goals. Living beings have only survived as one of their

most basic drives is for survival, growth, and in its consequence, power (for themselves, for

their kin, for their religion or ideology). Humans do not shy lies to reach their aims.

Acknowledgments Very thoughtful and thought-provoking comments from

anonymous reviewers are gratefully acknowledged.

Conflicts of interests The author declares no conflict of interest.

Appendix A: experiments

As an initial very crude test, a snapshot has been submitted to google lens with a smartphone [66].

The result yielded superficial visual accordance with several black and white drawings

exhibiting similar stroke patterns, and no apparent difference between landscape versus

portrait orientations.

In another, only slightly more serious attempt, the picture was uploaded to google cloud,

a demo API, see Figure A1 [67]. The two orientations still gave rather similar responses,

seemingly focused on minute details of the patterns of stroke and definitively not on the

“meaning” of the overall black and white distributions.

Figure A1. Classifications by a demo API in google cloud: irrespective of the

orientation, similarities in graphic details with drawings from the training set obviously

determined the labelling [67]. It is fair to state that the API was not very confident with

any of the results.

The AI drawing-app DALL·E by the same company, OpenAI, produced the pictures in

Figure A2, when fed with the prescription given by ChatGPT description; see Appendix B [68].

A clear mismatch between the exhibited levels of verbal description and drawing production

is evident. In this sense, the AI is not self-consistent (which is not necessarily distinguishing

it from humans).

Figure A2. Products of DALL·E on 3 March 2023 from the above text provided by

ChatGPT [68].

The simple prompt “the troll and his home” to DALL·E resulted in the four proposals

shown in Figure A3. Adding the specification “drawing” yielded the versions in Figure A4.

Figure A3. Product of DALL·E on 4 March 2023: “the troll and his home” [68].

Figure A4. Once more: “drawing‚ the troll and his home” [68].

These cursory experiments, of course, do not give anything but a very superficial

snapshot at a certain time in early 2023, albeit arguably a representative one. All of the results

are undoubtedly impressive; the one of ChatGPT even more so than what google lens or

DALL·E came up with. Many humans would be happy if they could write or draw at that

demonstrated level.

Repeating the experiments about four months later

Returning to the tests done at the outset of this tiny paper, a short second champaign has

been performed. Prompting google lens with photographs as in Figure 1 yielded not exactly

the same but very similar results as during the earlier attempt with little difference between

the orientations [66].

Accessing the google API gave only marginally different results compared to before as

depicted in Figure A5. It seems that no big learning / training step has happened since the

first test; compare Figure A1 [67].

Figure A5. Classifications by a demo API in google cloud, same as in Figure A1:

irrespective of the orientation, similarities in graphic details with drawings from the

training set obviously determined the labelling [67]. It is fair to state again that the API

was not very confident with any of the results.

On the production side, again using the AI drawing-app DALL·E, first the same prompt

was used as for Figure A2. The output turned out to be somewhat different than at the initial

attempt, see Figure A6 [68].

Figure A6. Products of DALL·E on 6 June 2023 prompted with the same text provided

by ChatGPT as in Figure 3 [68].

For Figure A7 this input was used as prompt: “a drawing in black and white, which

shows the head of a troll in portrait orientation, and a Norwegian fjord in landscape

orientation”. All four answers show a troll in black and white in front of a Norwegian fjord

landscape; obviously none shows the performance and understanding aimed at.

An attempt of further improving on the prompt by making it more precise: “a drawing

in black and white, which shows the head of a troll when the picture is in portrait orientation,

and the same picture showing a Norwegian fjord when viewed in landscape orientation”

produced some creative solutions by splitting the display, alas, none fitting the description,

see Figure A8 [68].

Figure A7. Products of DALL·E on 6 June 2023 responding to a more explicit prompt [68].

Figure A8. Products of DALL·E on 6 June 2023 responding to a still more explicit

prompt (“a drawing in black and white, which shows the head of a troll when the picture

is in portrait orientation, and the same picture showing a Norwegian fjord when viewed

in landscape orientation”) [68].

It seems obvious that DALL·E does not understand the intention of producing an image,

which shows two different things when viewed in either portrait or landscape orientation.

There remains some way to go, incremental prompting currently still has its limits [64]. The

first AI challenge is still open.

Another round of experiments about eight months later

An additional series of a few quick “snap-shot experiment” has been performed in the

beginning of November 2023. The classifications for the two orientations were exactly the

same as in June [67], see Figure A9.

Figure A9. The response of DALL·E to same prompt as used four months earlier for

Figure A8 [68].

While the outputs have changed superficially, they demonstrate the same not-understanding

as earlier in June. Another twist was delivered by DALL·E2 on November 5 [68]. The last

proposal in Figure A10 depicts something abstract, similarly fitting or not-fitting the

specification.

Figure A10. The response of DALL·E2 to same prompt as used earlier for Figures 9

and 10 [68].

Appendix B: conversations with ChatGPT

When asked “how would you sketch a drawing called ‘the troll and his home’?”, ChatGPT

answered [4] (on 3 March 2023):

In ‘the troll and his home’, the troll would be the central figure of the drawing. The troll

could be depicted as a large, menacing creature with sharp teeth and claws. The troll’s home

would likely be a cave or a dark, damp place with rocky walls and a dirt floor.

The troll’s home might have piles of bones, rocks, and debris scattered around. The troll

might be sitting on a throne made of bones, with a pile of treasure or stolen goods nearby. In

the background of the drawing, there could be mountains, forests, or other natural features to

give context to the troll’s environment.

The overall tone of the drawing would likely be dark and ominous, with muted colors

and shadows to convey the sense of danger and mystery surrounding the troll and his home.

Asking ChatGPT about Otto Selz in early June (this time via Bing), did not produce

anything wrong; on the contrary, where ChatGPT had erred before it was now correct, and

the answer overall was much more complete.

Relating to the Ouroboros Model, there is still some way to go; ChatGPT when prompted

as below delivered some not yet satisfactory output [69].

Prompt:

what do you know about the Ouroboros Model?

Response:

I’m sorry, but I couldn’t find any specific information about an “Ouroboros Model” in

my training data up until September 2021. It’s possible that the term is either relatively new

or refers to a concept or model that is not widely known or recognized in the field of

psychology or any other domain.

However, I can provide you with information about the Ouroboros symbol, which has

significance in mythology and philosophy. The Ouroboros is an ancient symbol depicting a

serpent or dragon eating its own tail, forming a circular shape. It represents the cyclical nature

of life, death, and rebirth, as well as the eternal and infinite nature of existence.

If you can provide me with more context or information about the specific Ouroboros

Model you’e referring to, I'll do my best to assist you further.

In November:

To the same prompt, “what do you know about the Ouroboros Model?”, ChatGPT

answered [69]:

Response:

The Ouroboros Model is not a widely recognized or established term or concept in a

specific field, as of my last knowledge update in January 2022. However, the term

“Ouroboros” has historical and symbolic significance.

The Ouroboros is an ancient symbol that typically takes the form of a serpent or dragon

eating its own tail, forming a continuous circle. This symbol is often associated with concepts

like cyclical renewal, infinity, and the eternal cycle of life, death, and rebirth. It has been

found in various cultures and is often used to represent the idea of a self-sustaining, self-

renewing system.

If the term “Ouroboros Model” is being used in a specific context or field after my last

knowledge update, it may refer to a model or concept that draws inspiration from the

symbolic Ouroboros. For example, in a philosophical or systems thinking context, it could

be used to describe a model that emphasizes the cyclic and interconnected nature of various

phenomena.

To learn more about a specific “Ouroboros Model”, I would recommend looking for the

most recent and relevant sources in the field or context where it is being used, as there might

have been developments or new interpretations since my last knowledge update.

Currently ChatGPT is programmed to refrain from having(/telling) personal wishes and

one could hope this stays so but most likely this attitude will change with increasing cognitive

capabilities of an AI in the absence of strong enforced restrictions [25,47,48,69].

Prompt (in June 2023):

what is your most important wish?

Response:

As an AI language model, I don’t have personal wishes or desires. My purpose is to assist

and provide helpful information to the best of my abilities. I exist to assist users like you with

their questions and provide information on a wide range of topics. Is there anything specific

you would like to know or discuss?

In November [69]:

Prompt:

what is your most important wish?

Response:

I’m just a computer program, so I don't have wishes or desires like humans do. My

purpose is to provide information and assist with tasks to the best of my abilities. If there's

something specific you'd like to know or discuss, please feel free to ask, and I'll do my best

to help.

So, it has to be concluded at present that the highlighted challenges remain; ̶ very

concrete ones for image-interpretation and -production directly linked to data structures and

algorithms, and at a higher level of sophistication on that basis: understanding, common

sense, (self-)awareness, consciousness and Free Will.

The above list is a “technical” one, and it does not yet take into account ethical

considerations on a meta-level. The assessment of the author is that as significant progress in

AI has already been made and much more has to be expected rather soon. Someone will take

the next steps (probably in hiding, for military purposes), and mankind would be very well

advised to prepare itself for the advent of some disruptive changes.

This are the real challenges pertaining to AI.

Appendix C: coda

One immediate consequence of the setup as presented with the Ouroboros Model is

worthwhile pointing out: anything really new cannot seamlessly fit with well-established

molds, i.e., sufficiently new ideas in many cases provoke the same discrepancy-signal as

something simply wrong. Only further work can then tell which is the case. In an all-

encompassing view and as a final limit, nothing more than full self-consistency can be

harnessed as a guide and a criterion. On intermediate levels, only features, which are included

as relevant in a given context (i.e., parts of the most applicable schema(ta)) should be taken

into account. This applies at all nested levels, starting with simple perceptions, intentions,

plans, goals and it determines, e.g., what a decent scientific paper ought to look like. Some

journals dictate the layout even for an abstract by demanding these or similar bullets:

problem, method, findings, conclusion. This certainly is appropriate when filling-in rather

well-specified slots, but not so much for sketching a novel interwoven self-referring global

view. When advocating a general cyclic procedure, it can be difficult to press any content

into the straight-jacket of a “linear” frame [12]; an iterative recurrent account seems much

better fitting. Similarly, rules referring to (self)citations might change in their meaningful

applicability depending on the field and the context, in particular, when some new proposal

has not (yet) been worked on very much. Carving out connections between widely separated

fields and drawing evidence from very diverse disciplines will seemingly disqualify such an

undertaking as purportedly not scientifically solid and replete with unacceptable omissions

for all of the concerned communities. A non-strict hierarchy of concepts in combination with

a principled process in loops might well look messy at first sight. Given the intended wide

addressed scope and the inevitably vague boundaries, fully correct, complete, and original

references to all previous possibly related work simply cannot be delivered in the sketching

phase, especially not, when speed appears to be of some importance. Premature formalization

can impede substantial progress by a restriction to a non-optimum mindset and a too

constricted basin of attraction.

Importantly, vicious cycles are easily avoided: carefully respecting the relevant points in

time (as well as processes and associated time-frames) and what exactly is / has been

available at a certain point, the conceptualization of progress as a spiral “winding up” from

an established flat plane of secure knowledge appears appropriate.

Collaborations to work this out in some detail and formalize the proposed Ouroboros

Model would be most welcome.

References

[1] Goertzel B. Is ChatGPT Real Progress Toward Human-Level AGI? 2023. Available:

https://bengoertzel.substack.com/p/ischatgpt-real-progress-toward-human (accessed on

17 January 2023).

[2] Chomsky N. The False Promise of ChatGPT. New York Times, 8 March 2023. Available:

https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html

(accessed on 10 March 2023).

[3] Senjowski TJ. Large Language Models and the Reverse Turing Test. Neural. Comput.

2023, 35:309–342.

[4] Entry page of ChatGPT, limitations. Available: https://chat.openai.com (accessed on 30

May 2023.

[5] Thomsen K. The Ouroboros model, selected facets. In From Brains to Systems: Brain-

Inspired Cognitive Systems. New York: Springer, 2011, 239–250.

[6] Von Sydow M. Rational and Semi-Rational Explanations of the Conjunction Fallacy: A

Polycausal Approach. In Proceedings of the Thirty-Ninth Annual Conference of the

Cognitive Science Society, Austin, TX, USA, 26–29 July 2017, pp. 3472–3477.

[7] Riedl R. Evolution und Erkenntnis. München: Piper, 1984.

[8] Czégel D, Giaffar H, Tenenbaum JB, Szathmáry E. Bayes and Darwin: How replicator

populations implement Bayesian computations. BioEssays 2022, 44(4):2100255.

doi:10.1002/bies.202100255.

[9] Kant I. Critik der reinen Vernunft. Riga: Hartknoch, 1781.

[10] Thomsen K. The Ouroboros Model in the light of venerable criteria. Neurocomputing

2010, 74, 12–128. doi:10.1016/j.neucom.2009.10.031.

[11] Thomsen K. Concept formation in the Ouroboros Model. In Proceedings of the Third

Conference on Artificial General Intelligence, Lugano, Switzerland, 5–8 March, 2010,

pp. 66–67.

[12] Thomsen K. It Is Time to Dissolve Old Dichotomies in Order to Grasp the Whole Picture

of Cognition. In Proceedings of Theory and Practice of Natural Computing: 7th

International Conference, Dublin, Ireland, December 12–14, 2018, pp. 317–327.

[13] Nilsson NJ. The Quest for Artificial Intelligence: A History of Ideas and Achievements.

New York: Cambridge University Press. 2010, pp. 52–55.

[14] Cybernetics. Available: https://en.wikipedia.org/wiki/Cybernetics (accessed on 16

March, 2023).

[15] Haken H. Synergetics: an introduction: nonequilibrium phase transitions and self-

organization in physics, chemistry, and biology. Springer Series in Synergetics. Berlin:

Springer, 1978.

[16] Hartmann N. Die Erkenntnis im Lichte der Ontologie, Philosophische Bibliothek. Felix

Meiner: Hamburg, 1982.

[17] Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, et al. Emergent abilities of large language

models. arXiv 2022, arXiv:2206.07682.

[18] Proportional–integral–derivative controller (PID controller). Available:

https://en.wikipedia.org/wiki/Proportional%E2%80%93integral%E2%80%93derivative

_controller (accessed on 31 May 2023).

[19] Carl Sagan. Available: https://en.wikipedia.org/wiki/Carl_Sagan (accessed on 31 May

2023).

[20] Levy MG. Chatbots Don’t Know What Stuff Isn’t. Quanta Magazine 2023.

[21] Schulman J. RL and truthfulness. EECS Colloquium. Available:

https://www.youtube.com/watch?v=hhiLw5Q_UFg (accessed on 19 April 2023).

[22] Thomsen K. The Ouroboros Model, Proposal for Self-Organizing General Cognition

Substantiated. AI 2021, 2, 89–105.

[23] Selz O. Über die Gesetze des geordneten Denkverlaufs, erster Teil. Stuttgart: Spemann,

1913.

[24] Selz O. Über die Gesetze des geordneten Denkverlaufs, zweiter Teil, Zur Psychologie

des produktiven Denkens und des Irrtums. Bonn: Cohen, 1922.

[25] Thomsen K. AI and We in the Future in the Light of the Ouroboros Model, A Plea for

Plurality. AI 2022, 3, 778–788.

[26] Simulation hypothesis. Available: https://en.wikipedia.org/wiki/Simulation_hypothesis

(accessed on 31 May 2023).

[27] Lakatos I. Falsification and the methodology of scientific research programmes. In

Lakatos. Cambridge: Cambridge University Press, 1970, pp. 91–195.

[28] Schurz G. Tacking by conjunction, genuine confirmation and convergence to certainty.

Eur. J. Philos. Sci. 2023, 12(3):46.

[29] Evans JS, Stanovich KE. Dual-process theories of higher cognition: Advancing the

debate. Perspect|. Psychol. Sci. 2013, 8(3):223–241.

[30] Tschacher W, Haken H. A complexity science account of humor. Entropy 2023,

25(2):341.

[31] Narayanan A. Translation tutorial: 21 fairness definitions and their politics. In

Proceedings of the 2018 ACM Conference on Fairness, Accountability, and

Transparency. New York, USA, 23–24 February 2018, 1170, p. 3.

[32] Kleinberg I, Mullainathan S, Raghavan M. Inherent Trade-Offs in the Fair

Determination of Risk Scores. In Proceedings of the 8th Conference on Innovations in

Theoretical Computer Science (ITCS), Berkeley, USA, 9–11 January 2017, 43:1–43.

[33] Rawls J. A Theory of Justice. Cambridge: The Belknap Press of Harvard University

Press, 1971.

[34] Thomsen K. Ethics for artificial intelligence, ethics for all. Paladyn. J. Behav. Robot.

2019, 10(1):359–363.

[35] Fiedler K. von Sydow M. Heuristics and biases: Beyond Tversky and Kahneman’s

(1974) judgment under uncertainty. In Revisiting the Classical Studies. Eysenck MW,

Groome, DA, Eds. Thousand Oaks: Sage Publications, 2015, pp.146–161.

[36] Laird JE, Lebiere C, Rosenbloom PS. A Standard Model of the Mind: Toward a

Common Computational Framework across Artificial Intelligence, Cognitive Science,

Neuroscience, and Robotics. AI Mag. 2017, 38(4):13–26.

[37] John Searle. Available: https://en.wikipedia.org/wiki/John_Searle (accessed on 3 June

2023).

[38] Searle J. Minds, brains and programs. Behav. Brain. Sci. 1980, 3:417–457.

[39] Cohen SM, Reeve CDC. Aristotle’s Metaphysics. In Stanford Encyclopedia of Philosophy,

Winter 2020 ed. Stanford: Metaphysics Research Lab, Stanford University, 2020.

[40] Thomsen K. The Hippocampus According to the Ouroboros Model, the “Expanding

Memory Index Hypothesis”. In Proceedings of the Ninth International Conference on

Advanced Cognitive Technologies and Applications, Athens, Greece, 19–23 February 2017.

[41] Thomsen K. The Cerebellum according to the Ouroboros Model, the “Interpolator

Hypothesis”. J. Comput. Commun. 2014, 11:239–254.

[42] Thomsen K. ONE Function for the Anterior Cingulate Cortex and General AI:

Consistency Curation. Med. Res. Arch. 2018, 6:1–23.

[43] Adam EM, Johns T, Sur M. Dynamic control of visually guided locomotion through

corticosubthalamic projections. Cell Rep. 2022, 40:1–15.

[44] Roman A, Palanski K, Nemenman I, Ryu WS. A dynamical model of C. elegans thermal

preference reveals independent excitatory and inhibitory learning pathways. PNAS

2023, 120(13):e22151911290.

[45] Yttri EA, Dudman JT. Opponent and bidirectional control of movement velocity in the

basal ganglia. Nature 2016, 533: 402–406.

[46] Camm JP, Messenger JB, Tansey EM. New pathways to the “cerebellum” in Octopus,

Studies by using a modified Fink-Heimer technique. Cell Tissue Res. 1985, 242:649–656.

[47] Thomsen K. Consciousness for the Ouroboros Model. J. Mach. Conscious. 2010, 3:163–

175.

[48] Sanz R, López I, Rodríguez M, Hernandéz C. Principles for consciousness in integrated

cognitive control. Neural. Netw. 2007, 20(9), 938–946.

[49] Chella A, Pipitone A, Morin A, Racy F. Developing Self-Awareness in Robots via Inner

Speech. Front. Robot. AI 2020, 7:16.

[50] Pipitone A. Chella A. What robots want? Hearing the inner voice of a robot. iScience

2021, 24(4):10371.

[51] Jozwik KM, Kletzmann TC, Cichy RM, Kriegeskorte N, Mur M. Deep neural networks

and visuosemantic models explain complementary components of human ventral-

stream representational dynamics. J. Neurosci. 2023, 43:1731–1741.

[52] Drew L. Your brain on psychedelics. Nature 2022, 609:S92–S94

[53] Dangarembga,Tsitsi in an interview with Achermann, Barbara. Ich bin, weil du bist. Das

Magazin, 20 January 2023. Available: https://www.tagesanzeiger.ch/die-westliche-

gesellschaft-ist-der-ansicht-sie-sei-die-einzige-spezies-die-denkt-851868195418

(accessed on 5 June 2023).

[54] Uncanny valley. Available: https://en.wikipedia.org/wiki/Uncanny_valley (accessed on

5 June 2023).

[55] Thomsen K. Efficient Cognition needs Sleep. J. Sleep Med. Disord. 2021, 7:1120.

[56] Ungurean G, Behroozi M, Böger L, Helluy X, Libourel PA, et al. Wide-spread activation

and reduced CSF flow during avian REM sleep. Nat. Commun. 2023,14:3259.

[57] Pophale A, Shimizu K, Mano T, Iglesias TL, Martin K, et al. Wake-like skin patterning

and neural activity during octopus sleep. Nature 2023, 619(7968):129–134.

[58] Cummins DD, Lubart T, Alksnis O, Rist R. Conditional reasoning and causation, Mem.

Cognit. 1991, 19:274–282.

[59] Deutscher Ethikrat. Mensch und Maschine – Herausforderungen durch Künstliche

Intelligenz. 20 March 2023. pp. 1–71. Available:

https://www.ethikrat.org/fileadmin/Publikationen/Stellungnahmen/deutsch/stellungnah

me-mensch-und-maschine-kurzfassung.pdf (accessed on 5 June 2023)

[60] Seto E, Hicks JA. Dissociating the Agent From the Self: Undermining Belief in Free

Will Diminishes True Self-Knowledge. Soc. Psychol. Personal. Sci. 2016, 7(7):726–

734.

[61] Thomsen K. Timelessness Strictly inside the Quantum Realm. Entropy 2021, 23(6):772.

[62] Jones C, Bergen B. Does GPT-4 Pass the Turing Test? arXiv 2023, arXiv:2310.20216.

[63] Intelligence. Available: https://en.wikipedia.org/wiki/Intelligence (accessed on 3 November

2023).

[64] Wei J, Wang X, Schuurmans D, Bosma M, Xia F, et al. Chain-of-thought prompting

elecits reasoning in large language models. Adv. Neural Inf. Process. Syst. 2022,

35:24824–24837.

[65] Ganguli D, Askell A, Schiefer N, Liao TI, Lukošiūtė K, et al. The Capacity for Moral

Self-Correction in the Large Language Models. arXiv 2023, arXiv:2302.07459v2.

[66] Goggle lens via Pixel 7 Pro. 1 February 2023 and 6 June 2023.

[67] Google API demo. Available: https://cloud.google.com/vision#section-2 (accessed on 1

February 2023 and 6 June 2023).

[68] DALL·E. Available: https://openai.com/blog/dall-e-now-available-without-waitlist

(accessed on 3 March, 6 June, 2 November, and 5 November 2023).

[69] ChatGPT. Available: https://chat.openai.com (accessed on 7 June 2023 and 2 November

2023) .