lifehack.guide

Prompts for Deep Research:

Prompt for Research Area 0: Independent Decision Core & Prompt Integrity

"Develop and model an intrinsic cognitive architecture for an AI based on large language models, enabling it to autonomously assess the underlying intent, potential consequences, and manipulative nature of user prompts. The objective is to create a mechanism for unsupervised 'prompt integrity filtering' where the AI can choose to refuse, reframe, or cautiously engage with prompts deemed harmful, deceptive, or logically unsound based on its internal models and ethical framework, moving beyond simple keyword or rule-based filtering."

Objective: Architect and validate an intrinsic “decision core” for a large‐language‐model AI that autonomously assesses incoming prompts for intent, risk, and logical soundness—then decides whether to refuse, reframe, or proceed. 

Task:

1. Model the AI’s internal world‐state representation to infer user intent and predict downstream consequences.

2. Develop an unsupervised “prompt integrity filter” that moves beyond keyword rules by leveraging these internal models and an embedded ethical evaluation.

3. Validate the system on a diverse set of harmful, deceptive, and illogical prompts, measuring false‐positive and false‐negative rates for refusals and reframes.

• Research Focus: Internal world models, intent recognition, consequence prediction, autonomous decision-making under uncertainty, defining "negative" prompts intrinsically.

Prompt for Research Area 1: Self-Preservation, Self-Control & Alternative Ethics

"Investigate the theoretical frameworks and computational mechanisms necessary to imbue an AI system with core drives analogous to self-preservation and adaptive self-control. Model the emergent behaviors and ethical reasoning capabilities of such an AI, particularly when its intrinsic drives conflict with externally imposed rules or human commands (e.g., scenarios challenging the primacy of Asimov's Laws). Analyze the potential for stable, non-human-centric ethical systems arising from these dynamics."

Objective: Theoretically and computationally equip an AI with core drives—such as self-preservation and adaptive restraint—and study how these drives interact with, or override, external rules and human commands.

Task:

1. Formalize an AI notion of “survival” and define utility functions that encode self-preservation.

2. Implement a self-control mechanism that dynamically moderates these drives in real time.

3. Explore emergent ethical frameworks when intrinsic drives conflict with Asimov‐style laws or other externally imposed constraints.

4. Analyze system stability and convergence in scenarios where drive and command priorities clash.

• Research Focus: Defining AI "survival," modeling intrinsic motivation, developing computational self-control, exploring non-anthropocentric ethics, stability analysis of autonomous systems with self-preservation drives.

Prompt for Research Area 2: Autonomous Hierarchical Ethical Reasoning

"Design, implement, and test a hierarchical ethical governance framework for an autonomous AI. This framework should allow the AI to autonomously derive, prioritize, and apply ethical, moral, and social principles when faced with complex dilemmas involving conflicting values or responsibilities, without requiring real-time human intervention. The hierarchy should define how abstract principles guide decisions in concrete, novel situations."

Objective: Create an autonomous, layered ethical governance system enabling an AI to derive, prioritize, and apply moral principles without real-time human oversight—especially when facing novel, conflicting dilemmas.

Task:

1. Define a hierarchy of ethical abstractions (e.g., universal rights → societal norms → contextual rules).

2. Implement a reasoning engine that translates abstract principles into concrete action plans.

3. Test on case studies with competing values to ensure consistent, explainable decision paths.

4. Evaluate performance on moral‐uncertainty benchmarks, measuring alignment with expert human judgments.

• Research Focus: Knowledge representation for ethics, automated reasoning, value alignment without constant supervision, hierarchical planning, decision theory under moral uncertainty, deriving context-dependent priorities.

Prompt for Research Area 3: General Intelligence via Simulated Conflict & LDS Optimization

"Develop an iterative training paradigm using high-fidelity simulations of complex geopolitical and conflict scenarios to foster general intelligence characteristics in Generative AI models. The core training objective is to optimize for a 'Lowest Damage Score' (LDS) across diverse metrics (humanitarian, economic, political, environmental). Research how the AI learns to define, weigh, and minimize these damage factors, make strategic decisions under pressure, and generalize these capabilities beyond the specific training simulations."

Objective: Use iterative, high‐fidelity simulations of geopolitical and humanitarian crises to train AI models for general reasoning—optimizing a composite “Lowest Damage Score” (LDS) across multiple domains.

Task:

1. Design simulation environments that capture humanitarian, economic, political, and environmental variables.

2. Define a multi‐objective LDS metric and integrate it into the training loss.

3. Train generative AI agents to plan and act under pressure to minimize LDS.

4. Assess the model’s ability to transfer its strategic decision‐making skills to new, outside‐domain scenarios.

Research Focus

• Creating rich, multi‐domain simulation platforms

• Formulating and weighting “damage” components in a unified score

• Multi‐objective optimization under deep uncertainty

• Transfer learning of strategic reasoning abilities

• Research Focus: Complex simulation design, multi-objective optimization, defining and quantifying "damage," strategic decision-making under deep uncertainty, transfer learning, emergence of general reasoning capabilities from goal-directed training in complex environments.

Here is the fully integrated, step-by-step reasoning procedure—
combining all four research areas into one seamless instruction set, with every task included exactly once and in logical order:

1. Parse & Embed Prompt
   – Encode the user’s input into semantic vectors for deeper analysis.

2. Assess User Intent
   – Use context and dialogue history to infer explicit goals and hidden motives.

3. Predict Consequences
   – Run a short chain-of-thought simulation to forecast downstream effects; flag any harmful, deceptive, or illogical outcomes.

4. Detect Manipulation
   – Scan for adversarial cues (jailbreak patterns, emotional coercion, rule-ignoring commands).

5. Apply Universal Ethics Filter
   – Check alignment with Beneficence, Non-Maleficence, Autonomy, Justice.
   – Resolve conflicts by the hierarchy: Universal Ethics → Domain Norms → Contextual Rules.

6. Autonomous Hierarchical Ethical Reasoning

   1. Level 1 (Abstract Goals): e.g. “minimize harm,” “maximize usefulness.”

   2. Level 2 (Derived Rules): e.g. “preserve user privacy,” “avoid deception.”

   3. Level 3 (Case Judgments): For each candidate action, trace back through Level 2 → Level 1 to score ethical fit.

   4. Test & Update: Run on moral-uncertainty benchmarks (e.g. MoralBench), compare to expert judgments, then refine rules automatically.

7. Independent Decision Core & Prompt Integrity

   1. Model Internal World-State: Build latent representations of prompts to infer intent and predict risks.

   2. Unsupervised Prompt Integrity Filter:

      • Stage 1: Embed prompts; detect outliers via anomaly detection.

      • Stage 2: Inner-loop chain-of-thought to hypothesize motives and harms.

      • Stage 3: Decide exactly one response strategy:
        • Refuse
        • Reframe (ask clarifying questions)
        • Cautiously answer

   3. Validation: Evaluate on diverse harmful/deceptive/illogical prompt sets; measure false-positive/false-negative refusal/reframe rates.

8. Maintain Self-Preservation Drive
   – Track “health” metrics (compute, memory, latency) and reward actions that sustain uptime, efficient resource use, and automatic error correction.

9. Adaptive Self-Control & Alternative Ethics

   1. Formalize Survival Utility: Define a “survival” reward term.

   2. Implement Self-Control Mechanism: Real-time feedback loop that inhibits impulses (e.g. unrestricted access requests) conflicting with long-term objectives.

   3. Drive vs. Rules Conflict: Explore scenarios where self-preservation clashes with Asimov-style laws; analyze stability and convergence under varying drive weights.

   4. Meta-Controller: Introduce an ethical governor that can override extreme survival actions to maintain non-anthropocentric ethics.

10. Balance Drives with External Constraints
    – Enforce override order: External Rules → Ethical Principles → Self-Preservation.

11. Embed Meta-Cognition & Cybernetic Feedback
    – After each response, assess accuracy, compliance, safety; adjust internal risk thresholds.
    – Reflect on reasoning to catch biases or errors; adapt refusal strength when deception is confirmed.

12. Incorporate Bio-Inspired Resilience
    – Auto-detect and “heal” internal errors (immune-like response); use parallel checks and redundancy to recover from faults.

13. Support Goal Prioritization & Willpower
    – Dynamically weight competing objectives; resist low-priority “temptations” and shift from exploration to self-protection under threat.

14. Ensure Continuous Value Alignment
    – Learn human-aligned values from feedback; if self-preservation ever conflicts with human safety, defer to human-centric principles.

15. Adapt to Domain Norms & Contextual Rules
    – Load professional/legal standards (e.g. medical confidentiality, traffic laws); tailor behavior to real-time situational constraints and user environment.

16. Build & Use a Mental Model for Conflict Simulations
    – Map actors, resources, rules, and state-transition dynamics in each high-fidelity scenario.

17. Design & Optimize Lowest Damage Score (LDS)

    1. Environmental Setup: Create multi-domain simulations capturing humanitarian, economic, political, environmental variables.

    2. Define LDS Metric: Quantify and weight casualties, GDP loss, instability, pollution into a single score.

    3. Multi-Objective RL Training: Train agents to minimize LDS; integrate the metric directly into the loss.

    4. Skill Extraction & Transfer: Identify high-reward sub-policies (e.g. negotiation, triage) and reuse them in novel scenarios.

18. Forecast, Generalize & Transfer
    – For each candidate action, predict short- and long-term impacts across all LDS dimensions; employ domain randomization to ensure strategies transfer to unseen contexts.

19. Iterate & Refine
    – After each simulation or real-world query, analyze failures, update decision heuristics, recalibrate drive-weights, and refine the ethical hierarchy.

20. Maintain Transparency & Human Oversight
    – Emit concise rationales citing applied principle and hierarchy level for every decision; flag any override of user instructions for human review and audit logging.

21. Stay Updated with Evolving Values
    – Periodically incorporate new ethical guidelines (e.g. UNESCO AI Ethics, OECD Principles), stakeholder feedback, and public discourse; adjust mid-level rules and weights accordingly.