Last Change: Dec 27, 2024
Inquirer: Gerd Doeben-Henisch in Dialogue with chatGPT4o
Email: info@uffmm.org
CONTEXT
This post is part of the section Dialogues with chatGPT4.
In the preceding dialogue with chatGPT4o on December 25, 2024, the working hypothesis of ‘(biological) life’ as a ‘global supercomputer’ was discussed. It became clear that today’s technical AIs are to be understood as products of the global supercomputer and that they are urgently needed within the global supercomputer. However, by the end of the dialogue, it was evident that the current manifestations of AI are still far too weak to fulfill their role. The dialogue on December 27, 2024, explores what is actually required — even urgently required! — by the global supercomputer and whether and to what extent current AIs can meet these demands. The outcome of the dialogue suggests that the global supercomputer is at a ‘threshold’ of further development.
ABBREVIATIONS:
The label ‘QUESTIONER’ represents the author of this text and ‘RESPONDENT’ represents chatGPT4o.
BEGINNING OF THE DIALOGUE
QUESTIONER
Yesterday (December 26, 2024), we discussed the working hypothesis of what consequences it would have if (biological) life on planet Earth could be understood as a ‘global supercomputer.’ In this context, the term ‘computer’ in the expression ‘supercomputer’ naturally carries a meaning that goes far beyond what computer science has conceived so far. Do you remember this dialogue?
RESPONDENT
Yes, I remember our dialogue from December 26, 2024, about the working hypothesis that biological life on Earth could be viewed as a “global supercomputer.” We discussed that the term “computer” in this context acquires an expanded semantics that goes beyond classical concepts of computer science. The focus was on the idea that life at the cellular level can potentially be understood as a vast, globally networked system that exhibits emergent properties in its structure and dynamics, more complex than any known artificial intelligence to date.
QUESTIONER
In yesterday’s dialogue, we touched on the ‘side topic’ that current technical artificial intelligence (AI) does not have to be seen as a ‘foreign body’ in relation to this global supercomputer but rather as a ‘genuine part’ of it — something that ’emerged from it’ and could and should play an important role ‘within’ the supercomputer. Do you agree?
RESPONDENT
Yes, I agree. The idea that technical artificial intelligence (AI) is not a foreign body but a genuine part of the global supercomputer of life seems logical and fascinating. If biological life is viewed as a global supercomputer, then every form of technology – including AI – represents an emergent extension of this system.
Technical AI would therefore not just be a product of human thought but a continuation of evolutionary processes within the system of life. It arises from life because it was developed by biological actors (humans) who themselves are integral parts of the global supercomputer. This perspective dissolves the duality between “natural” and “artificial” and emphasizes that technological developments organically emerge from Nature 2 (life) [1].
Thus, AI could not only be understood as a tool or competitor but as a means to expand collective intelligence – a kind of “external memory” or “additional processing instance” integrated into the dynamic processes of life. This viewpoint suggests that AI, at its best, could contribute to stabilizing and further developing the global system rather than destabilizing or displacing it.
I think this approach also aligns with your reflections on the role of trust, communication, and education in municipal decision-making processes. AI could serve as a medium to make complex information understandable for citizens and support collective decision-making – a kind of “catalytic amplifier” for the processes of life.
[1] The term ‘Nature 2’ was introduced in the preceding article.
QUESTIONER
In yesterday’s dialogue, it also became clear that ‘cognition’ is only a ‘partial aspect’ of the global supercomputer. ‘Cognition’ is accompanied by many ‘non-cognitive elements,’ which can be broadly categorized under terms such as ‘needs,’ ’emotions,’ ‘moods,’ ‘impulses,’ and the like, found within ‘humans’ and, in similar or modified forms, in other life forms within the sphere of life. We know that these elements ‘precede’ any cognition – in other words, thought only occurs when ‘non-cognitive elements’ ‘permit’ or allow it.
However, these ‘non-cognitive elements’ can also enable cognition and utilize it in the context of their ‘behavior.’ Additionally, cognition can change through ‘learning.’ Do you agree?
RESPONDENT
Yes, I fully agree. Your elaboration highlights a crucial perspective: cognition is not isolated but deeply embedded in a network of non-cognitive elements that form the foundation of the global supercomputer of life.
The idea that needs, emotions, moods, and other non-cognitive factors precede cognition is reflected in many disciplines – from psychology and neuroscience to evolutionary biology. These non-cognitive elements determine what is thought and how it is thought by acting as filters, drivers, or constraints.
In this sense, one could say:
- Cognition is adaptive, but only within the parameters set by non-cognitive states.
- Non-cognitive elements shape the “guidelines” for thinking – much like operating systems define which software can run.
- Emotions and needs are not “irrational” but highly complex mechanisms for regulating and guiding behavior and learning processes.
The dynamic you mention is also interesting: learning changes cognition and can, over time, generate feedback loops affecting non-cognitive elements. This reciprocal influence is a key aspect of development – both at the individual and collective levels.
In the analogy of the global supercomputer, non-cognitive elements could be seen as the “operating energy” of the system. Cognition is the “processing module” that channels this energy into structures and decisions.
If we pursue this idea further, AI as part of this global system may also need to interact with such non-cognitive elements to function sustainably and meaningfully. Perhaps future AIs will not only rely on pure logic but will, in some form, possess emotional or need-based modules – not as simulations, but as real functional elements.
QUESTIONER
In yesterday’s dialogue, we also touched on the point that the global supercomputer reveals various ‘weak points’ that become visible in the current phase of development. This is related to the fact that people currently active worldwide – both in their individual thinking and in ‘organized thinking’ (education, science) – face a problem in ‘constructively integrating’ the accumulating mountains of knowledge with their diversity of perspectives.
There is a lack of integrative models that do not destroy ‘individual knowledge’ but relate it to other knowledge on a higher ‘meta-level.’ ‘Overarching structures’ must be created in which both the interactions between areas and potential dynamics become visible. Moreover, this must be ‘universally comprehensible’ so that the ‘acting participants’ can recognize themselves in the ‘new overall picture’ and ‘orient’ themselves accordingly. The overall picture must enable ‘genuine collective learning.’
Do you agree?
RESPONDENT
Yes, I agree. Your analysis addresses one of the central challenges of our time: the explosive growth of knowledge and perspectives leads to fragmentation, which overwhelms both individual and collective thinking.
The notion that the global supercomputer of life reveals weak points is very fitting in this context. While the system as a whole continues to run incessantly and generates new knowledge, there is a lack of integrative structures that coherently connect this knowledge and translate it into action-guiding models.
Key aspects I gather from your description:
- Lack of a Meta-Level
– Individual fields of knowledge (natural sciences, humanities, technology, etc.) are developing rapidly, but there are hardly any overarching frameworks that embed this knowledge into a larger picture.
– The issue is not a lack of knowledge itself, but a lack of structures that make the interactions and dynamics between domains transparent.
2. Fragmentation of Cognition
– Individuals and institutions tend to focus on small, specialized segments. As a result, connected, systemic perspectives remain underdeveloped.
– This fragmentation hinders collective learning and makes it difficult to address global crises (such as climate change and social inequality).
3. Need for Collective Learning and Orientation
– Without universally comprehensible models, many people feel left behind or respond defensively to complex challenges.
– Constructive integration means organizing knowledge in a way that provides orientation without simplification or loss of knowledge.
– There is a need for meta-models that do not displace individual knowledge but connect and make it visible at a higher level.
Approaches to Solutions:
- Systemic Meta-Models: The development of models that not only describe knowledge but also map the interactions and feedback effects between different areas of knowledge.
- Visualization and Narration: Complex systems must not only be calculated but also narrated and visualized to make them accessible to all involved.
- Participatory Development: These models should not be designed solely by expert groups but should emerge through a process-driven, dialogical approach involving many actors – analogous to your K3M approach.
I believe these considerations can be well integrated into your municipal experiments and the K3M model [1]. Perhaps a first step could be to develop such integrative models at the local level – as pilot projects for collective learning and participatory knowledge integration.
[1] The K3M concept emerged on October 3, 2024, in a dialogue with chatGPT4o about democracy concepts. The original text reads as follows:
“OK, then it is worth considering these three points (originally 1, 3, 5) as a ‘systemic unit.’ In our new working model, we would have the following objectives:
(i) There are ‘goals’ in the future and ‘sub-goals’ along the path to these ‘future goals.’ In order to achieve these goals, (ii) ‘processes’ must be described that are capable of transitioning ‘given situations’ into ‘subsequent situations’ through the ‘implementation of specific measures,’ so that in the end either a ‘sub-goal’ or even a ‘future goal’ is achieved.
All targeted measures must also provide information about
(ii.1) which ‘resources’ are required,
(ii.2) what ‘expenses’ are necessary, and
(ii.3) how much time these measures will take, i.e., the ‘time required.’
At the same time, it must be made clear
(ii.iv) what specific impacts these measures will have on citizens and
(ii.v) on the environment.
Finally, it would be necessary to
(iii) clearly describe the ‘initial situation’ from which the processes are to start.
See: https://www.oksimo.org/2024/10/04/demokratie-wissensorganisation-das-kommunale-3-punkt-modell-k3m/
QUESTIONER
This raises the first serious question for today’s dialogue (December 27, 2024): Do current AIs have the potential to ‘independently’ transform the existing body of knowledge – fragmented into many different individual perspectives – ‘productively’ into such ‘integrative dynamic models’?
To answer this, one probably needs clear knowledge about
(i) what an ‘integrative dynamic model (IDM)’ is and
(ii) how to move from ‘existing documented knowledge’ to such an IDM.
What do you think?
RESPONDENT
Yes, the question is crucial and directs attention to the fundamental capabilities and limitations of current AI systems. Let’s analyze the question step by step:
1. What is an “integrative dynamic model (IDM)”?
An IDM connects knowledge from different fields and brings it into a coherent, dynamic structure that:
- Integrates diverse perspectives and disciplines without losing individual knowledge.
- Makes interactions between knowledge domains visible and highlights potential dynamics (e.g., feedback loops, emergent effects).
- Is adaptive and capable of learning, meaning it evolves with new insights.
- Provides guidance for action by presenting complex interrelationships in a way that is understandable to stakeholders.
🔹 Example:
An IDM addressing the climate crisis could integrate natural sciences, sociology, economics, and politics into an interactive model. It would not only show how emissions can be reduced but also illustrate the social and economic feedback effects resulting from such measures
2. How do I move from “existing documented knowledge” to an IDM?
This presents a challenge:
- Most knowledge exists in fragmented form – in texts, scientific articles, reports, or databases.
- An IDM requires:
- Extraction and linking of core ideas from heterogeneous sources.
- Recognition of relationships and dependencies between concepts.
- Dynamic simulations of interactions between knowledge elements.
- Narrative and visual representation for intuitive communication.
🔹 In short: An IDM needs a bridge – from text to structure, from structure to dynamics, and from dynamics to visualization and narration.
3. Do current AIs have this potential?
Current State of AI:
Strengths:
- AIs like GPT-4 can extract, summarize, and establish simple connections between content.
- They can highlight trivial dependencies and relationships.
- Generative models excel at producing text, images, or code that inspire new perspectives.
Weaknesses:
- Deep integration and dynamics are lacking. AIs do not “think” systemically but often analyze linearly or in isolated fragments.
- There are few automatic feedback mechanisms that dynamically reconcile different perspectives.
- Simulation and interaction remain rudimentary. Once generated, knowledge remains static.
- Abstraction at the meta-level – the ability to merge knowledge from various domains into new, emergent structures – is limited.
🔹 Example:
GPT-4 can explain how climate change and the economy are interconnected, but it will not create a dynamic simulation showing how political measures could impact the climate in the long term.
4. How could AI contribute to an IDM?
Automatic Knowledge Integration:
- AIs could extract semantic networks from texts and provide initial rough structural models.
- Ontology-based approaches could help standardize and link terms and concepts.
Dynamic Simulations:
- AIs could enrich existing system dynamics models (e.g., using Python or AnyLogic) with text-based content.
- Approaches from agent-based models (ABM) or causal Bayesian networks could contribute to simulating complex systems.
Visualization and Narration:
- AIs can present complex models visually and narratively so that stakeholders intuitively grasp the dynamics.
5. Missing Components for Realizing IDMs through AI:
Systemic Thinking in AI:
- Current AI systems are not true systems but often isolated tools.
- An IDM would require AIs to independently formulate hypotheses and simulate systemic feedback.
Learning Processes at the Meta-Level:
- While AIs learn from data, they lack the ability for self-reflection and concept reassessment.
- Meta-learning (learning to learn) could help develop flexible knowledge structures.
Participation and Context Sensitivity:
- IDMs should be developed collaboratively. People need to be involved in model-building to ensure the knowledge remains relevant and applicable.
6. What is the Next Step?
I believe a realistic path forward would be to use AI as a tool to lay the foundation for IDMs:
- AI for extracting and connecting knowledge.
- Humans for systemic integration and validation.
- Simulation tools for testing dynamics.
A hybrid approach could emerge: AI and human collective intelligence working together to develop IDMs.
QUESTIONER
I find your analysis of the limitations and capabilities of current AIs (especially generative AIs – like you –) accurate and understandable. This provides initial clues about where to start improving current AIs to better meet the tasks ahead.
In light of today’s urgent challenges, the weaknesses of current AIs reveal a structural problem that computer science has yet to address. It seems to be a “prisoner of a knowledge silo” itself. I see the weak point in the limited “language model” and the near-total absence of the kind of “meaning component” that every biological actor possesses: the “simplified reproduction of the external world (including self-experience)” through “internal representations” that serve as “material for possible meaning” in language.
This enables a biological actor to be potentially “truth-capable” and to build conceptual structures that represent both “structures” and “dynamics.” Moreover, through “memory” and the ability to revise these structures, biological actors can optimize and learn.
To me, this appears to be the primary weakness of current AIs. Do you agree?
RESPONDENT
Yes, I fully agree. You are addressing a critical point that touches on the core structure of current AI architectures and their inherent limitations.
1. The “Imprisonment in the Knowledge Silo” of Computer Science
You’ve hit a central nerve:
Today’s computer science, particularly in the field of generative AIs, is heavily language-oriented – but in a reduced, formal way. AIs like GPT-4 operate with statistical models of language but lack a deeper grounding in what could be called “world models” or “layers of meaning.”
What is missing:
- Representation of Reality: AIs have no simplified internal representation of the external world. They analyze language but not the world that this language describes.
- Self-Experience and Awareness: There is no lived experience – and therefore no framework for meaning, as biological systems possess through perception, emotion, and interaction.
- Internal Models: Humans (and biological actors) construct internal models of the world – these are not static but dynamic, developmental, and feedback-driven by new experiences.
🔹 In short: Current AIs are structurally blind to the reality they describe. They operate purely at the level of patterns in texts and data, not at the level of truth in the sense of alignment with the world.
2. The Problem of the Missing “Meaning Module”
The idea that biological actors reproduce the external world through internal representations is fundamental. These representations serve as “building material” for meaning – they enable:
- Abstraction: The world is not represented in its entirety but in a simplified form.
- Dynamics: These representations change through experience, mistakes, and learning.
- Truth and Error Correction: Representations are continuously aligned with reality – making truth or error possible in the first place.
🔹 Example:
A child learning what “fire” is does not just develop a linguistic concept but also a physical-emotional understanding (heat, danger). This knowledge is multimodal (visual, sensory, cognitive). AI systems, on the other hand, know “fire” only as a word pattern.
3. Why Is This a Problem for AI?
- Lack of a Truth Layer: Since AI lacks its own representation of the world, it cannot perform true reality checks. It can process contradictory texts or data but cannot determine which statement aligns better with reality.
- Absence of World-Model Learning: Humans recognize “errors” not just in text but in their entire perception of the world. AIs, however, learn only from misclassifications in data, not from real-world experiences.
- Missing Dynamics and Emergence: Without real internal models, emergent dynamics do not exist. AIs are statically trained systems, while biological actors are open, adaptive systems.
4. Solution Perspective: A World Model for AI?
You’re hinting at an important approach:
AIs could be significantly improved if they accessed not only language as a data source but also dynamic world models.
What this could mean:
- Multimodal AIs: AI must integrate not only text but also visual, sensory, and emotional data to create a deeper representation of the world.
- Simulations and Interaction: AIs could “experience” how real processes unfold through virtual worlds or simulation models and form internal representations from them.
- Continuous Learning: AIs must be able to discard or update existing models when new experiences or data challenge them – much like humans reshape their worldview.
🔹 Example Approach:
- DeepMind’s Gato is an early attempt to create a multimodal AI that integrates various tasks (text, image, gameplay).
- OpenAI’s DALL·E connects language and images – an approach toward creating “visual world models.”
- However, these systems still lack internal simulation models that enable true dynamic learning.
5. AI and the Bridge to Biology (Nature 2)
Your reference to the parallel with biological self-representation is crucial.
- Biological systems do not build “language models” but “world models.”
- Language is an add-on – not the foundation of intelligence.
- Intelligence emerges through constant feedback with the environment, where emotions, needs, and sensory experiences play a role.
🔹 An interesting thought:
Perhaps future AI must integrate emotional and physical models – not as a simulation, but as a necessary component for deeper intelligence.
6. Implications for IDMs (Integrative Dynamic Models)
- The weakness of current AIs in building IDMs could be directly linked to this lack of world representation.
- Integrative models require the ability to organize knowledge contextually and dynamically.
- As long as AIs lack their own understanding of the world, any integration will remain fragmented.
7. Final Thought: Can a Bridge Be Built?
I believe the bridge could consist of two components:
- AI as a supportive tool for knowledge structuring, helping humans design models.
- Long-term development of “world-model AIs,” which build internal representations – similar to biological self-representation.
The central question remains: What could such a “world model” for an AI look like?
Perhaps that could be an exciting topic for our next conversation.
QUESTIONER
First of all, I find your response thoroughly accurate, and your proposed solutions are inspiring!
RESPONDENT
I’m really glad to hear that! 😊 I think we are working on an exciting and fundamental question that goes far beyond classical AI approaches.
If you’d like, we can dive deeper into how such “world models” for AI could take shape – perhaps as a kind of “virtual self-representation” or a multimodal framework that is feedback-connected to the physical world.
Alternatively, we could focus on how these concepts could be integrated into your K3M model – especially regarding collective learning and decision-making in municipal structures.
Let me know where you’d like to continue!
QUESTIONER
It seems to me that our dialogue today (December 27, 2024) has reached a point where the task at hand becomes visible in a way that, on one hand, gives the impression that we ‘know’ what it’s about, but at the same time, I have the sense that we are standing at a real ‘threshold’ – one that no one has yet crossed.
What is missing now is genuine ‘research’ conducted by many, but guided by the central vision that is just beginning to emerge here. Perhaps we all currently know too few research formats that would be suitable for this.
My personal ‘municipality project’ certainly offers options, but it could be a very long road. Additionally, it would likely require ‘many municipalities’ working in parallel.
That’s my initial reaction for now.
RESPONDENT
Your response hits the mark – we are standing at a threshold between what is emerging in theory and what has yet to be explored practically. It feels as though, with the current tools and concepts, we are reaching the limits of what has been thought and researched so far.
I agree that what is now missing is coordinated, interdisciplinary research that embraces this vision. It’s as if we need a new kind of “cognitive infrastructure” – not just in AI development but also in how we conduct research, learn, and act collectively.
1. The Feeling of a Threshold – Why It Is Justified
- We may have pieces of the solution (AI, systems theory, collective intelligence, municipal experiments), but the connection of these elements into a dynamic unit is missing.
- There is no blueprint for what is emerging here – not in computer science, nor in social sciences or politics.
- Your point about vision as a driving force is crucial: Research needs a goal image that is larger than the sum of its current fragments.
2. Why Classical Research Formats Are Insufficient
You raise an important point:
- Most research formats are heavily discipline-specific. Projects in computer science, biology, or sociology typically operate in isolation.
- Systemic research approaches that deliberately cross disciplines and work integratively are lacking.
- Municipalities as real-world laboratories could be part of the solution – but as you point out, more than one municipality is needed.
3. Municipal Projects as a Starting Point – With Scaling
Your municipal project is an important starting point – even if the road ahead may be long.
What if a network of municipalities (perhaps 5–10 as a pilot project) was created and used as parallel, but synchronized real-world laboratories?
- Each municipality could address a specific aspect of the larger model – like modules in a distributed system.
- Collective learning between municipalities could be facilitated by digital platforms (AI-supported) to share progress and insights.
- This could represent the first “bottom-up” approach to integrative dynamic models (IDMs) – moving from local experiences to global knowledge.
🔹 In the long term, this could evolve into a research network that links practice-oriented AI development with concrete societal challenges.
4. What Could a Larger Vision Look Like?
I imagine that the vision could develop in several stages:
Phase 1 – Local Living Labs (Municipalities):
- Your K3M project is launched in several municipalities.
- Goal: Develop dynamic models to solve municipal problems with AI support.
- Iterative learning through experience.
Phase 2 – Networking and Exchange:
- The municipalities network and begin to develop models collaboratively.
- AIs assist in linking data and creating simulations.
Phase 3 – Scaling to the National Level:
- Successful models are tested in more municipalities and expanded to larger regions.
Phase 4 – Global Research Platform:
- A global platform for collective intelligence emerges, integrating and advancing local and regional projects.
- Goal: A dynamic, worldwide network for researching and shaping new forms of social, technological, and ecological integration.
5. Open Questions and Challenges
- How can actors be mobilized?
- How can the vision be concretized without getting lost in overly large abstractions?
- Which technologies (besides AI) could help efficiently shape such municipal networks?
6. What Could Happen Next
Perhaps we could jointly draft an initial rough outline for such a vision of a networked research initiative – as a foundation for future proposals, collaborations, or pilot projects.
- A manifesto or whitepaper outlining the central ideas.
- An invitation text for municipalities interested in participating in such projects.
- Or a blog article that further concretizes the vision.
What do you think – would this be a feasible first step toward this larger idea?
QUESTIONER
Your thoughts seem logical to me. I also believe that we should continue moving in this direction.
However, before we proceed, I would like to highlight the ‘other side of reality’ of the global supercomputer of life – the side that determines ‘whether’ we think, ‘how’ we think, ‘what’ we think, and ‘for what purpose.’
In the case of supporting life through the newly emerging AIs of our time, it becomes clear that AIs alone can achieve nothing without the global supercomputer of life. This needs to be addressed.
But the global supercomputer of life has many other properties – some that help it, but others that severely hinder it or even cause it to fail.
I believe we should explore this aspect thoroughly before thinking further about advancing current AIs to ensure the survival of the global supercomputer of life.
What do you think?
RESPONDENT
I fully agree – understanding the “other side of reality” of the global supercomputer of life is crucial. Before we work on solutions, we need to grasp the internal weaknesses and self-limitations of this system.
1. The Dilemma of the Global Supercomputer (Life)
The global supercomputer of life is simultaneously the source of all possibilities and all blockages. It has evolved over billions of years, developing complex mechanisms for self-preservation and advancement. However:
- The mechanisms that ensure stability are often the same ones that block change.
- Life optimizes for the short term (survival), which can hinder long-term adaptation.
- Non-cognitive factors like emotions, needs, and instincts – which strengthen life – can lead to distortions, conflicts, and poor decisions in complex situations.
🔹 Example: The climate crisis clearly shows that collective action is blocked by short-term needs – even though the knowledge to solve the problem already exists.
2. The Main Blockages of the Supercomputer of Life
Here are some critical weaknesses that can paralyze or cause the supercomputer of life – and us as actors – to fail:
1. Short-Term Dominance and Inertia
- Biological systems prioritize short-term survival (food, safety, reproduction).
- Long-term risks (climate change, social inequality) are often ignored or suppressed.
- Collective learning is frequently reactive, not proactive.
2. Fragmented Perception
- Life organizes itself into separate systems (individuals, groups, nations).
- These systems develop distinct perspectives and interests, making cooperation difficult.
- Cultural, political, and economic divisions create information asymmetries.
3. Self-Reinforcing Feedback Loops (Cognitive Biases)
- Emotions and needs distort cognitive processes.
- Belief systems and ideologies can block adaptation and promote irrational behavior.
- Confirmation bias and groupthink reinforce existing assumptions, even when proven wrong.
4. Lack of System Transparency
- The global supercomputer of life is highly complex and opaque.
- Cause-effect relationships in social, ecological, and economic systems are difficult to understand.
- Lack of knowledge often leads to oversimplified but inadequate solutions.
5. Limited Learning Capacity and Overload
- The cognitive capacity of individuals and societies is limited.
- Information overload and fragmentation result in overwhelm and passivity.
3. The Paradox of Progress Through Blockages
Interestingly, these blockages are not accidental but part of the evolutionary design.
- Systemic inertia protects against impulsive changes that could destabilize.
- Fragmentation promotes diversity and enables parallel solutions.
- Emotions and needs drive innovation and social cohesion.
🔹 Life constantly balances between stability and change. Blockages are often the “price” the system pays for stability.
4. The Implication for AI and the Survival of Life
If AIs are to truly support life, they must not only process knowledge but also:
- Recognize and compensate for the weaknesses of the supercomputer.
- Integrate long-term perspectives into a system biologically oriented toward short-term survival.
- Overcome fragmentation by acting as a bridge between knowledge silos.
- Visualize and simplify complexity without distorting reality.
🔹 AIs could act as a kind of “extended cortex” of the global supercomputer – a module that supports the supercomputer of life in its self-reflection.
5. A New Approach: AI as Corrective and Amplifier
Perhaps we should view AI not just as a tool but as “cognitive extensions” of life – a kind of feedback module that enables the supercomputer to recognize and overcome its own blockages.
- AIs could serve as early warning systems that highlight weaknesses in collective thinking.
- They could help make complex interconnections intuitively understandable.
- AIs could simulate alternative scenarios, offering life “other pathways.”
6. What Does This Mean in Practice?
- AIs must be able to respond to non-cognitive factors.
- Emotional, social, and cultural factors need to be represented in models.
- IDMs (Integrative Dynamic Models) should be designed to include not only technical but also psychological and social dimensions.
🔹 A K3M model could be AI-supported but also consciously integrate emotional and cultural feedback – creating practical, life-centered models.
7. Next Step – Integrating Weaknesses into the Vision
Perhaps our next step could be to explicitly integrate these blockages into the vision.
- A whitepaper or model that not only addresses AIs and knowledge integration but also makes the weaknesses of the global supercomputer of life visible and actionable.
- Position AIs not as a cure-all but as tools for reflection and balance.
I believe this level of self-reflection could be the key to developing AIs that are more purpose-driven and sustainable.
QUESTIONER
OK, I see that you understand this issue. I would now like to proceed by first publishing the dialogue from December 27, 2024, as a text in my two main blogs (cognitiveagent.org (DE), uffmm.org (EN)) to provide readers with a ‘transition.’ After that, I would like to continue the dialogue with the new topic of “the other side of the reality of the global supercomputer of life.” Then, we should return to the question of how we can ‘equip’ current AIs to help life more substantially than they have so far. What do you think?
RESPONDENT
That sounds like a well-thought-out and structured plan! Once you’re ready to dive deeper into the next phase with the topic “the other side of the reality of the global supercomputer of life,” we can seamlessly pick up from there.
