An OAII perspective on feasibility, cost, and architectural realism
One of the most common questions I receive when describing the OAII Base Model — particularly the World and Event objects — is straightforward and entirely reasonable:
Is this actually feasible today on edge hardware, and can it be done without becoming prohibitively expensive or complex?
The short answer is yes — provided the system is designed correctly.
The longer answer is that feasibility hinges less on raw model capability and more on architecture, gating, and a clear distinction between signals, events, and knowledge.
The Core Architectural Insight
The OAII Base Model does not assume continuous, high-cost inference across all sensors. Instead, it assumes an event-centric pipeline:
- Always-on, low-cost sensing establishes situational awareness
- Candidate Events are formed when deviations from baseline occur
- Higher-cost sensing and inference is invoked only when warranted
- Events are declared, revised, or closed based on accumulating evidence
This is why the Event object is defined as:
An interpreted occurrence derived by comparing current observations to Sensor Knowledge, within a World.
Not as a continuous state, and not as a medical inference.
What Is Feasible on the Edge Today
1. Low-Cost Sentinel Signals (Always-On)
These are inexpensive in compute, power, and hardware cost:
- Motion (PIR / IMU)
- Door / contact sensors
- Time-of-day and duration tracking
- Basic device state signals
These signals are sufficient to establish routine, absence, transition, and anomaly patterns — which already cover a large fraction of aging-in-place use cases.
In OAII terms, these signals continuously inform Event CANDIDATE evaluation.
2. Audio Processing (Triggered, Not Continuous)
Edge-resident audio processing is feasible today if it is:
- gated by voice-activity detection or event triggers
- limited in duration
- privacy-aware and locally processed
Speech transcription or acoustic classification does not need to run continuously to support meaningful Events. In most cases, a short snippet is sufficient to confirm or revise an Event candidate.
3. Vision as an Episodic Sensor
Vision is the most expensive modality — and also the most misunderstood.
The OAII Base Model does not require:
- continuous video streaming
- full scene understanding
- identity recognition
Instead, vision is treated as an episodic signal source, activated only when:
- a candidate Event crosses a confidence threshold, or
- contextual axes (time, location, activity) indicate ambiguity
This makes local vision-based confirmation feasible even on modest edge hardware.
4. Sensor Knowledge Is the Real Enabler
The critical technical insight is the role of Sensor Knowledge:
- baselines (typical motion patterns)
- learned routines
- calibration data
- embeddings or feature summaries
- geofences and thresholds
Events are not recognized by raw sensor values alone, but by comparison to locally stored knowledge.
This comparison is computationally cheap and maps cleanly to the Event methods:
evaluate_event_candidatedeclare_eventrevise_eventclose_event
Why This Is Not Prohibitive
Hardware Cost Is Controllable
An OAII-conformant system does not mandate any specific hardware tier.
- Basic deployments may rely on inexpensive sensors and a small edge computer
- Richer deployments may add episodic audio or vision capability
- Worlds without certain sensors remain fully valid
The standard explicitly allows World-specific event types and sensor sets, avoiding an implicit “one size fits all” requirement.
Compute Cost Is Bounded by Design
The Event lifecycle explicitly supports:
- CANDIDATE states with low-cost evaluation
- incremental confidence updates
- graceful degradation under missing or noisy data
At no point is the system required to force certainty or escalate unnecessarily.
This is what keeps the architecture edge-friendly.
What the OAII Model Explicitly Avoids
To remain feasible and ethically sound, the Base Model deliberately avoids:
- medical diagnosis
- intent attribution
- centralized surveillance assumptions
- global ontology enforcement
- continuous multimodal inference
The focus is on observability, not speculation.
The Bottom Line
An edge-primary, event-based assistant for aging-in-place is technically feasible today, and it can be built without prohibitive cost if:
- Events are treated as contextual interpretations, not raw signals
- Sensor Knowledge is local and incremental
- Expensive modalities are gated and episodic
- Worlds and Event types are allowed to be specific and local
The OAII Base Model is intentionally aligned with these realities. It does not assume future hardware, speculative AI capabilities, or fragile cloud dependencies.
It assumes disciplined architecture.
This post reflects the intent of the OAII Base Model v0.1.x and the ongoing Open SGI MVP exploration.
Open Autonomous Intelligence Initiative 
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