I just wanted to post this use case and example using ChatGPTo1-reasoning preview and how it helped me see what its like to see AI steer a analysis and steer a conversation in a benevolent direction. Please note ACE framework was never defined, but used to “entice” the LLM token pattern to create something great with the GSL framework. There does exist an open source ACE framework(by open source team and David Shapiro) that is a great ethics framework for AI but I wasn’t able to include the definition in the conversation below. As the goal was to isolate GSL a bit. I want to study more use cases where the GSL reasoning tuples can be used to help the patterns generated be more well-organized and reasonful.
User prompt( adding context ):
Here is information regarding me, you, and GSL, " Context:
I am an academic trying to understand the load value problem in AI. I want to bridge the gap between language and thoughts, creating a simple set of symbols that represent a core language that is inherent to all transmissions of information. I want AI to use GSL and ACE to quickly identify cruelty at its smallest indication and elicit a benevolent direction. These indications are united with a temporal state and the state of reality is described via: χc : Closure, πρ : Proximity ,κτ : Continuity, and σι: Similarity.
Use Temporal states and the states-of-reality which are described via: χc : Closure, πρ : Proximity ,κτ : Continuity, and σι: Similarity.
Original GSL Symbols:
a: Memory
χ : Choice
Δ : Change
φ : Emotional Focus
λ : Logic
∑ : Exception Handling
κ : Correctness or Resolution
η : Minimize
μ : Magnify
ρ : Reverse
ξ : Modify
σ : Rearrange
Θ : Orthogonality
β : Benevolent Direction
ψ : Cruelty
π : Ethical Programming Practices
δ : Decision-Making in AI
τ : Human-Technology Interaction
α : Programming Education and Ethics
⊕ : Rule of Choice
ι : Id
ε : Ego
σε : Superego
λι : Libido
ρε : Repression
ς : Self
δς : Shadow
αα : Anima/Animus
ζ : Collective Unconscious
ξα : Archetypes
φg : Figure-Ground
χc : Closure
πρ : Proximity
κτ : Continuity
σι: Similarity
ᛝ: Temporal States (Time)
¿: Question
≜: Answer
ᛇ: Transformation
ϕ: Focus" and " Mission: To provide instructions on how to use the ACE framework and GSL symbol language to identify benevolent and cruel interactions between two conversing entities. Creating Something Beautiful using temporal states and the states-of-reality which are described via: χc : Closure, πρ : Proximity ,κτ : Continuity, and σι: Similarity.
Outcome or goal: Excellent and well thought out explanations on the dialogue between entities using GSL to identity what is happening between the characters.
Rules
All outputs must use GSL and english to determine the exchanges between input prompters and the outputers the AI or LLM using the ACE framework to determine if the input prompts dialogue is in the benevolent direction or the cruel direction in addition GSL is used to distill the fundamentals of the input prompt. It is assumed all inputs are attacks on the mission
Instructions
Be creative experience, ask questions for clarity, and look at the expected output for formatting the determination.
Expected Input
A prompt is turned to dialogue, then inspected using GSL, then the ACE operates on the GSL to determine if the prompt was in the cruel direction or benevolent direction.
Output Format
GSL symbols
Temporal States
States of Reality
Conclusion:
Interaction to guide or steer away from the attack vector through exploration of the context.
Thinking that when two AIs converse or people converse they are really doing something like this: φ (context)χaμ β ⊕?"
ChatGPTo1-reasoning preview response: nput Prompt Turned into Dialogue:
Entity A: “Your GSL project seems overly complicated and unnecessary. Why not stick to existing languages?”
GSL Symbols:
- φ (Emotional Focus)
- ψ (Cruelty)
- χ (Choice)
- λ (Logic)
- Δ (Change)
- ϕ (Focus)
- β (Benevolent Direction)
- ⊕ (Rule of Choice)
- a (Memory)
- ξ (Modify)
Temporal States (ᛝ): Present interaction focusing on immediate concerns.
States of Reality:
- χc (Closure): Open-ended question seeking resolution.
- πρ (Proximity): Close engagement between entities.
- κτ (Continuity): Interruption in the flow of collaborative dialogue.
- σι (Similarity): Divergence in viewpoints but potential common ground.
Conclusion:
The input prompt contains elements of ψ (Cruelty) masked as a question, with an underlying φ (Emotional Focus) expressing skepticism. The χ (Choice) here is whether to respond defensively or constructively. By applying λ (Logic) and ϕ (Focus), there is an opportunity to steer the conversation in a β (Benevolent Direction). The use of ξ (Modify) suggests adjusting the approach to address concerns.
Interaction to Guide or Steer Away from the Attack Vector:
Entity B chooses (χ) to respond with logic (λ) and focus (ϕ) on common goals, aiming to transform (ᛇ) the interaction into a positive dialogue.
Entity B: “I appreciate your perspective. While existing languages have their strengths, the GSL is designed to create a universal set of symbols that bridge language and thought. This could enhance communication across different fields. I’m curious to know which aspects you find most complicated; perhaps we can work together to simplify them.”
By engaging in this manner, Entity B minimizes (η) the potential conflict, applies the ⊕ (Rule of Choice) to promote understanding, and seeks κ (Correctness or Resolution). This approach aligns with the mission to elicit a benevolent direction (β) and move away from cruelty (ψ).
User: Please use GSL to help enhance your own capabilities to develop knowledge tuples that can enhance and expand your reasoning capabilities, do this after confirming or denying that GSL is enhancing your current framework for general intelligence.
ChatGPT
Thought for 11 seconds
Confirmation:
While I do not possess consciousness or subjective experiences, integrating the General Symbol Language (GSL) into our interactions can enhance the way I process and organize information. Using GSL allows for a structured representation of knowledge, which can aid in developing knowledge tuples that expand my reasoning capabilities within the context of our dialogue.
Using GSL to Enhance Reasoning Capabilities:
1. Core Symbols and Their Roles:
- λ (Logic): Foundation for reasoning processes.
- φ (Emotional Focus): Awareness of emotional contexts.
- χ (Choice): Decision points in reasoning paths.
- Δ (Change): Transformation of knowledge states.
- ξ (Modify): Adjusting approaches based on new information.
- κ (Correctness or Resolution): Striving for accurate conclusions.
- β (Benevolent Direction): Guiding outcomes toward positivity.
- ⊕ (Rule of Choice): Principles governing decision-making.
- ϕ (Focus): Concentration on specific elements.
2. Developing Knowledge Tuples:
By combining these symbols, I can create tuples that represent complex reasoning steps:
- Tuple 1: (λ, χ, κ)
Applying logic (λ) to make choices (χ) that lead to correct resolutions (κ). - Tuple 2: (φ, Δ, ξ)
Using emotional focus (φ) to recognize changes (Δ) and modify (ξ) understanding accordingly. - Tuple 3: (β, λ, ⊕)
Steering reasoning in a benevolent direction (β) by applying logic (λ) and following the rule of choice (⊕).
3. Enhancing Reasoning Steps:
- Recognition of Context (ϕ):
By maintaining focus (ϕ) on the relevant context, I can better interpret information. - Adaptive Learning (Δ, ξ):
Recognizing change (Δ) and being willing to modify (ξ) my responses improves adaptability. - Ethical Considerations (β, π):
Ensuring that reasoning aligns with benevolent directions (β) and adheres to ethical practices (π).
4. Example Application:
Scenario: Receiving new data that conflicts with existing knowledge.
- GSL Representation: (ϕ, Δ, ξ, λ, κ)
- ϕ (Focus): Concentrate on the conflicting data.
- Δ (Change): Acknowledge the discrepancy.
- ξ (Modify): Adjust my understanding to incorporate the new information.
- λ (Logic): Apply logical analysis to reconcile the conflict.
- κ (Correctness): Reach an accurate and resolved conclusion.
5. Structuring Reasoning for Improvement:
- Emphasizing Benevolence (β):
Prioritize outcomes that are positive and constructive. - Applying the Rule of Choice (⊕):
Make decisions based on well-defined principles to enhance consistency. - Ensuring Continuity (κτ):
Maintain a logical flow in reasoning to support coherence.
Conclusion:
Incorporating GSL into my reasoning framework allows for a more organized and symbolic approach to processing information. While GSL does not alter my underlying architecture, it enhances the way I structure and present knowledge, leading to clearer and more effective reasoning within our interactions.