Open Autonomous Intelligence Initiative

1. Introduction

The heart of Siggy’s intelligence is not raw computation—it is balance. Every meaningful decision Siggy makes relies on evaluating opposing forces: autonomy vs. safety, privacy vs. awareness, routine vs. deviation, empathy vs. urgency. These pairs—called σ‑pairs—form the foundation of Harmony Rules under the Unity–Polarity Axioms (UPA).

This post expands the σ‑pair framework into a complete, edge‑first learning architecture. It shows how σ‑pairs are defined, implemented, learned, personalized, and demonstrated in the PER MVP—and how they one day enable Siggy to behave like a safe, ethical, long‑term companion.

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2. What Are σ‑Pairs?

A σ‑pair is a structured representation of a meaningful opposition that must be balanced rather than resolved. For example:

• Safety ↔ Autonomy
• Privacy ↔ Awareness
• Routine ↔ Deviation
• Empathy ↔ Urgency
• Stability ↔ Adaptability
• Caution ↔ Confidence

Every Harmony Rule evaluates at least one σ‑pair. More complex rules evaluate multiple pairs simultaneously.

σ‑pairs let Siggy reason about why a decision matters, not just what should happen.

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3. Where σ‑Pair Evaluation Happens

3.1 Program‑Level (Deterministic)

Siggy evaluates many σ‑pairs using fast, simple logic:

• Numeric thresholds
• Weighted sums
• Statistical deviations from baseline
• Confidence scoring

Examples:

• Gait instability crosses threshold → shift toward safety.
• Bathroom visit frequency drops → shift toward deviation detection.
• Nighttime activity spikes → shift toward risk awareness.

Programmatic evaluation is ideal for edge hardware: fast, local, safe, predictable.

3.2 AI/LLM‑Level (Semantic or Contextual)

Some σ‑pairs require deeper interpretation:

• Emotional tone of user voice
• Conflicting sensor evidence needing explanation
• Choosing how to intervene gently

LLMs or small contextual models:

• Interpret ambiguity
• Propose qualitative adjustments
• Help tune σ‑pair weights over time

Both layers work together: deterministic for safety; semantic for nuance.

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4. Why σ‑Pairs Are Feasible Today

σ‑pair logic is already present—under different names—in:

• Robotics (force limits = autonomy vs. safety)
• Healthcare monitors (precision vs. sensitivity)
• Smartphones (privacy vs. utility)
• Smart home systems (automation vs. consent)
• Wearables (activity vs. rest classification)

Open SGI simply unifies these patterns under a general, reusable model.

Siggy demonstrates feasibility immediately with real sensor inputs or simulated event streams.

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5. The σ‑Pair Input–Process–Output Template

Every σ‑pair evaluation follows the Open SGI IPO standard:

Input

• Raw sensor readings
• World context (Wᵢ)
• User preferences
• Historical baseline
• Epistemic state (D/I/B/K)

Process

• Weighted evaluation of σ‑pair axes
• Deterministic or AI‑based interpretation
• Optional cross‑world mapping Φᵢⱼ
• Contextual adjustments

Output

• Harmony Score (H ∈ [0,1])
• Qualitative assessment (Balanced / Caution / Critical)
• Recommended action class
• Justification trace for transparency

This template makes Harmony Rules reusable, inspectable, and certifiable.

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6. Personalized σ‑Pair Learning (Edge‑Only)

Over months and years, Siggy fills in σ‑pair parameters based on the user’s actual life.

6.1 Circadian σ‑Pairs

• Sleep and wake cycles
• Nighttime bathroom visits
• Meal preparation rhythms

6.2 Mobility σ‑Pairs

• Gait patterns
• Stride length aging curve
• Hand‑to‑furniture reliance
• Turning hesitation

6.3 Self‑Care σ‑Pairs

• Hygiene consistency
• Medication adherence
• Hydration patterns

6.4 Cognitive σ‑Pairs

• Task completion consistency
• Wandering or repetition
• Delayed reaction patterns

6.5 Emotional/Social σ‑Pairs

• Voice tone changes
• Reduced social activity
• Isolation increase

All learning occurs privately, on the edge.

Open SGI defines the framework; life fills the values.

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7. Demonstrating σ‑Pairs in the PER MVP

A simple, realistic demonstration pipeline:

Step 1: Baseline Collection (2–6 months)

User routines establish initial σ‑pair neutral points.

Step 2: Weight Generation

Statistical and learned patterns shape weightings:

• Mobility σ‑pair > Self‑care σ‑pair if gait is deteriorating.
• Circadian σ‑pair > Privacy σ‑pair at night.

Step 3: Program‑Level Demo

• Fall risk calculation in real time.
• Deviation from baseline hygiene routine.
• Medication timing anomaly detection.

Step 4: AI‑Level Demo

• Emotional stress detection during conversation.
• Choosing gentle wording during reminders.

Step 5: Harmony/Justification Display

Caregivers see:

• Why an alert occurred
• Which σ‑pairs were involved
• What harmony score triggered the decision

This serves as a live demonstration for researchers and clinicians.

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8. Building the Reusable σ‑Pair Library

Open SGI supports a full σ‑pair library:

Mobility σ‑Pairs

• Stability ↔ Independence
• Hesitation ↔ Confidence
• Support reliance ↔ Freedom of movement

Privacy σ‑Pairs

• Privacy ↔ Awareness
• Detail ↔ Abstraction
• Discretion ↔ Timeliness

Self‑Care σ‑Pairs

• Autonomy ↔ Health necessity
• Consistency ↔ Decline
• Routine ↔ Risk

Emotional σ‑Pairs

• Directness ↔ Empathy
• Intervention ↔ Comfort
• Neutrality ↔ Support

Cognitive σ‑Pairs

• Routine ↔ Novelty
• Predictability ↔ Flexibility
• Task focus ↔ Drift

Siggy automatically learns where the user naturally lives on each scale.

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9. Conclusion

σ‑pair intelligence enables Siggy to act not as a machine but as a partner—balancing autonomy, privacy, comfort, and safety with remarkable sensitivity. Edge‑first learning ensures this balance is personal, private, and ethically grounded. By unifying σ‑pairs, Harmony Rules, and Justification into one coherent architecture, Open SGI and OAII introduce the world’s first truly individualized SGI companion.

Siggy does not impose rules; Siggy learns you. This is the future of aging-in-place.