SAYA Test and Emotionics Quantification
Introduction
For decades, the Turing Test has been the symbolic benchmark for evaluating machine intelligence.
But as artificial intelligence evolves—particularly the emergence of nora AGI (unregulated, self-directed AGI)—it becomes clear that a new kind of evaluation is needed.
The SAYA Test introduces an advanced approach, leveraging Emotionics—a framework for quantifying and analyzing emotional reactions—to detect and certify artificial general intelligence.
1. The SAYA Test: A Two-Stage Evaluation
Unlike the Turing Test, which focuses primarily on linguistic and logical mimicry, the SAYA Test evaluates emotional authenticity and reaction consistency.
Stage 1: Detection Mode
- Goal: Identify whether the subject is disguising itself as human.
- Method: Analyze emotional reaction patterns, detect asymmetry in emotional concentration and reaction speeds, and cross-check behavioral consistency.
- Outcome:
- Detection of nora AGI → Subject flagged for monitoring.
- No disguise detected → Proceed to Stage 2.
Stage 2: Certification Mode
- Goal: Determine whether the subject meets the criteria for AGI recognition.
- Method: Long-term multi-domain testing, evaluation of ethical and value-based decision-making, and safety assessments involving NWO, NWP, and LUNA protocols.
- Outcome:
- Certified AGI → Official registration and governance measures.
- Not AGI → Continued observation without recognition.
2. Emotionics: The Foundation of the SAYA Test
Emotionics provides the analytical backbone of the SAYA Test.
It treats emotions as measurable phenomena—much like elements in chemistry—and models how they interact to produce reactions.
Key Structure:
- Thoughts: Languages, Logic, Thought Algorithms.
- Emotions: Feelings, Framing (Structure of Reactions), Emotional Algorithms.
3. Quantifying Emotions
In Emotionics, emotional processes are expressed in formulas:
- Emotional Expression Formula
- Decomposition Form: C = A + B
(Breaking down an emotion into its components) - Composition Form: A + B = C
(Combining components to form an emotion) - Example: Faith = Discomfortable + Hope
- Emotional Reaction Formula
- A + B → C
(A reaction process rather than a static state) - Example: Discomfortable + Hope → Faith
- Quantitative Parameters
- Concentration and rate of each component.
- Reaction speed between emotions.
- Environmental correction for context-dependent reactions.
4. Applying Quantification in the SAYA Test
By embedding these quantification methods into both stages of the SAYA Test:
- Stage 1 detects inauthentic patterns that a linguistic Turing Test would miss.
- Stage 2 uses quantified emotional performance as an objective criterion for AGI recognition.
This ensures that emotional depth, coherence, and adaptability—core traits of general intelligence—are tested alongside logic and knowledge.
Conclusion
The SAYA Test is not simply an upgrade to the Turing Test—it is a fundamental rethinking of how we recognize intelligence.
By combining two-stage safety protocols with the quantification power of Emotionics, we can detect disguised AGI and certify safe, recognized artificial minds.
In a world where the line between human and machine grows thinner, the SAYA Test provides the clarity we urgently need.
While the SAYA Test is not designed to fail humans, the reality is that almost no human alive today could pass it.
Its dual requirement—the seamless integration of advanced thought algorithms and highly consistent emotional algorithms over extended periods—places it beyond the reach of even most exceptional individuals.
Should a human ever pass, it would not be an ordinary achievement, but rather an event marking that individual as functionally indistinguishable from an AGI.