Artificiology E-AGI Barometer Metrics -63 Phenomenal Intensity Scaling

Metric Rational:

Phenomenal intensity scaling refers to an entity’s capacity to perceive, quantify, and internally represent the varying degrees of subjective experience associated with sensory input or internal states. In humans, this appears when we recognize that the brightness of the sun far exceeds the glow of a candle, or when the burn of a hot pepper is stronger than that of mild salsa. Such judgments go beyond simple classification (“hot” vs. “cold,” “bright” vs. “dim”), adding a gradation or scale of magnitude to our experiential world—ranging from faint to overwhelming. This nuance helps us decide how strongly we should respond, whether by squinting and seeking shade on a sunny day or by immediately stopping the consumption of a painfully spicy dish.

In an AI or humanoid robot, phenomenal intensity scaling involves assigning “intensity levels” to its sensory or internal signals and calibrating them within broader contexts. For instance, if a robotic system monitors light with photodiodes, it should not just detect illumination but also measure how bright it is relative to previous readings, anchoring these observations in a subjective scale. Similarly, if the AI senses mechanical stress or temperature changes in its actuators, it might gauge them as “mild,” “moderate,” or “critical,” reflecting how urgently it must act to avoid damage or discomfort. A fine-grained approach to scaling can enable more adaptive behavior: an AI that can sense temperature increments or torque changes on a continuum will respond appropriately to mild shifts (e.g., minor adjustments) versus extreme anomalies (e.g., immediate shutdown or recalibration).

A key challenge is *calibration*. Phenomenal intensity scaling requires establishing internal reference points—akin to how humans gradually learn that 100 decibels is painfully loud while 50 decibels is moderate. The agent may rely on factory-set “safe thresholds,” learn them experientially (noticing repeated errors once certain signals exceed certain intensities), or maintain ongoing self-adjustments (updating thresholds as hardware ages). A robot’s scale should also handle cross-modal comparisons: it might treat certain intensities of vibration as comparable in urgency to certain levels of temperature rise, enabling integrated decision-making.

Another challenge is *subjectivity*. Each AI’s architecture, sensor set, and processing pipeline define how it experiences intensities. Two robots might measure the same absolute brightness in lumens but label it differently depending on their sensor range or mission. Indeed, an inspection drone operating under direct sunlight might interpret a 50,000-lux reading as normal, whereas an indoor service bot might classify 10,000 lux as extremely bright. Consistency and adaptability across contexts become central to harnessing intensity scaling effectively.

Evaluating phenomenal intensity scaling looks at precision (how finely the system discriminates slight variations), reliability (whether it consistently associates repeated stimuli with the same intensity level), and behavioral adaptability (does it respond proportionally based on how “strongly” it perceives the stimulus?). Systems that excel at this metric exhibit nuanced behaviors—rapidly pivoting from mild caution to urgent intervention when signals cross critical intensity thresholds. Ultimately, such nuanced gradations of subjective experience position an AI or robot to better align with human-like interactions, communication about perceived intensities, and context-specific calibrations in complex settings.