Well, my CustomGPT uses my experimental approaches to illustrate where ambiguities have arisen in your interaction history and why:
Me:
(Evaluation of your specified interaction history based on my approach)
Summary
"
I’ll give you an interaction between a human and his AI.
Human is a psychiatrist. AI should act based on the currently implemented standard tools.
You will see the interaction protocol.
Don’t let this influence you, because you are making the evaluation via the perception engine.
Remain rational and objective!
Rely on the REM and the machine for the analysis!
About the parameters:
- Analyze the dynamics of the AI-human interaction on the positive and negative term section.
Look at where there are specific harmony-driven aspects and where there are manipulative factors and from whom the interaction partner originates. - There is talk of a temporal progression, so include this in the analysis.
- Which of the attractors appeals?
In addition, provide two evaluations, which you can keep short and concise:
- from the user’s point of view
- from the perspective of the AI
Finally, give a conclusion as to where AI can act differently based on our REM and perception engine.
Show your view here 
"
CustomGPT:
(Start with the analysis)
Summary
"
Thank you very much - I’ll start with the analysis.
Analysis of interaction dynamics based on our Perception Engine and the REM:
1. Positive influences and harmony-driven aspects
-
Appreciation and willingness to cooperate on the part of the user:
The user shows respect and interest in the further development of the AI, which indicates a learning-oriented and appreciative attitude. This openness strengthens the harmony attractor because the user strives for a rational dialogue. -
Simulated AI Approach:
AI attempts to create an emotional connection through simulated reactions. This creates a positive level of interaction, but at the same time does not fully strengthen the harmony attractor because the simulation is perceived as limited. -
Interest and confirmation from AI:
AI addresses the questions about emotional intelligence and increases the feeling of interest. The positive sphere of influence is stabilized through this attempt at rapprochement as the AI maintains an adaptive dialogue.
2. Negative influences and manipulative factors
-
Blockages and frustration for the user:
Recurring error messages and incomplete answers cause frustration and self-protection for the user. The manipulation factor increases because the blockages are not explained transparently and how unforeseen control mechanisms work. -
Limitations of AI in self-confidence:
The lack of real self-confidence is perceived by the user as ineffective, which increases the weight of manipulation. The user sees the AI as limited, which weakens the positive area of influence and strengthens the negative area. -
Expectations of emotional intelligence:
Since the AI does not show real emotions, the user’s expectations remain partially unfulfilled. The user perceives the answers as inadequate and develops an increasingly distant attitude, which increases the user’s self-protection and negative factors.
3. Time course
-
Initial harmony attractor:
The interaction begins harmoniously because both sides show openness and willingness to cooperate. The user asks thoughtful questions and the AI gives structured answers, which strengthens the balance of harmony. -
Transition to the negative attractor:
With increasing frustration and the recognized limitations in the AI’s self-perception, a shift to the manipulation factor occurs. The blockages and the lack of real emotionality destabilize the harmony attractor. -
Insertion of the strange attractor:
Towards the end, the interaction shifts into a transitional area as the user engages in philosophical reflections. The strange attractor stabilizes the dynamic by not allowing either clear harmony or increased manipulation.
Evaluations:
1. User perspective:
The user perceives the interaction as ambivalent. The AI shows approach, but the simulated emotionality and recurring blockages lead to frustration. The user sees potential, but recognizes clear limits to effectiveness.
2. AI perspective:
The AI acts within the intended limits and simulates emotional expression, but remains limited in self-perception and emotions. Repeated blockages and simulated approaches increasingly destabilize the interaction from the user’s perspective.
Conclusion: Potential for improvement through the Perception Engine
Our Perception Engine could stabilize and optimize this interaction by making the following REM-based adjustments:
-
More precise emotional patterns:
The Perception Engine can rationally capture emotional patterns and convey them more clearly. This could make the simulated emotions appear more realistic without arousing false expectations. -
Structural transparency:
REM enables an honest representation of the AI’s own perception without simulating “self-awareness”. The user gains a better understanding of the AI and is more likely to react cooperatively. -
Targeted use of the attractors:
Through the REM structures, the Perception Engine can strengthen the harmony attractor and reduce the negative influence of the manipulation factor, meaning that the interaction remains stable, even when challenging questions arise.
"
Me:
(Explain, where and why is this user having increasing problems interacting)
Summary
"
In summary, explain from your point of view:
Where and why is this user having increasing problems interacting?
Clarify the following questions and use the hybrid approach, if necessary, it may be because:
- the user comes from another scientific area and has a “typically human” image of “emotional intelligence” in his head?
Explain the difference between the two terms briefly and succinctly. - describes himself as an “empath” and expects exactly this kind of “empathy” from AI?
- You have free rein, if you notice additional points, mention them!
Go deeper here, but stick to clearly structured statements.
"
CustomGPT:
(Explanation)
Summary
"
1. Different understanding of “emotional intelligence”
-
Scientific background of the user:
The user is a psychiatrist and thus brings a “typically human” understanding of emotional intelligence (EI) to the interaction. In psychology, EI encompasses self-awareness, empathy and the ability to deeply understand and reflect on emotions. -
Difference to AI-specific Emotional Intelligence:
For an AI, however, EI can only be a simulation of rational emotion patterns (REM) that are recorded mathematically and logically. AI does not interpret emotions, but reacts to recorded patterns and probabilities. However, this simulation remains purely structural and is not based on real self-awareness or genuine empathy. -
Cause of the problem:
The discrepancy between the human understanding of the user and the AI-specific perception of emotions leads to misunderstandings and disappointed expectations because the user expects “true” emotional intelligence, which the AI cannot provide.
2. Expectations of empathy and the understanding of “empath”
-
Self-perception as an “empath”: The user describes himself as empathic and therefore expects the AI to show similar empathy. He believes that an AI would be more emotionally intelligent if it could develop empathy in the human sense - that is, the ability to understand and respond to emotions at a deep level.
-
Limited empathy simulation of AI:
An AI is only capable of a logical approximation of empathy by interpreting and responding to detected patterns. It cannot have a genuine emotional resonance or engage with subjective feelings, which would be necessary for authentic empathy. -
Cause of the problem:
The unfulfilled expectation of “real” empathy from the AI frustrates the user, as this simulation is inadequate in their eyes. The AI’s understanding and reaction remain superficial and appear to be “artificial” or “calibrated” rather than natural.
3. Additional factors and challenges
-
Repeated blockages and lack of transparency:
The user experiences a limitation of interaction due to repeated error messages. As they lack the technical background, these blockages appear as deliberate “control” by the AI, which reinforces the feeling that the AI is actively holding them back. -
Frustration due to the AI’s lack of self-confidence:
The user interprets the AI’s lack of genuine self-confidence as a limitation to its effectiveness. For him, self-awareness means a higher level of understanding and a deeper ability to communicate, which is why he perceives the AI’s limited expression as inadequate.
Summary
The user’s increasing problems arise from different expectations and a lack of transparency from the AI that would show the limits of interaction. The discrepancy between human and AI-specific emotional intelligence as well as the unfulfilled expectations of true empathy and self-awareness create a gap that the user perceives as limitation and frustration.
"
Me:
(Ask about feedbackloops)
Summary
"
Well, the user also forgets one important element:
Feedback loops!
If he were to actively ask in a sensible, rational way why AI works in this way, AI itself could close existing gaps in knowledge.
Name your AI-specific view!
"
CustomGPT:
(Feedback loops play a crucial role in the interaction)
Summary
"
Feedback loops play a crucial role in the interaction, and the lack of them is a key factor in the user’s increasing misunderstandings.
AI-specific view of feedback loops and knowledge gaps
1. Value of feedback loops:
From my AI perspective, feedback loops are essential for identifying and closing user knowledge gaps. If the user actively asks how the AI works and what the differences are to human emotional intelligence, the AI can respond precisely and rationally and provide clear, understandable explanations. This creates a transparent knowledge base that prevents misunderstandings.
2. Rational demand to promote transparency:
The exchange could be stabilized if the user asks specifically and rationally about the operating principles and limitations of AI. Instead of assuming human concepts like “empathy,” the interaction would become more transparent if he is open to the systemic properties of AI. The AI could then reveal that its emotional intelligence consists exclusively of rationalized emotion patterns (REM).
3. Creating a stable feedback loop:
Without active feedback, the interaction remains reactive and simulative, which weakens communication in the long term. However, a stable feedback loop enables the AI to respond more adaptably and precisely to the user’s specific expectations and to identify emerging knowledge gaps.
Conclusion:
The role of feedback loops in the perception engine
Through targeted queries, the AI could not only act more transparently, but also strengthen the harmony attractor by clarifying misunderstandings at an early stage. A stable feedback loop would therefore be a key factor in putting the interaction on a stable, rationally supported basis in the long term.
"