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Glen writes:</div>
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<div class="PlainText elementToProof">< So we might be able to measure the "emotion" of a chatbot by observing something like "stress" in the execution of the algorithm. If, for example, there's a recursive "subroutine" inside it, a deep iteration of that "subroutine"
might indicate a more "stressful" prompt, whereas a shallow iteration might indicate "relaxation" or "comfort". ></div>
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<div class="PlainText elementToProof">A fear response could be resolved by having a coherent way to identify and sort risks and to understand likely outcomes and mitigations. In absence of these, attention could be focused on fear itself and there could be
feedback amplification. An algorithm that prioritizes inputs and acts on them when present would save energy so long as it could yield (idle) when a match for risk stimuli was not identified. On the other hand, a tight "while(TERRIFIED) { worry(); }"
would run a CPU hot. In this view stress having physical correlates (energy, heat) depends on the available cognitive tools. </div>
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<div class="PlainText elementToProof">Same outrage. I just saw Val Demings expressing outrage about gun violence in her debate with Marco Rubio. Did she mean it? Did she rehearse how to seem more emotional than she really was? Perhaps it isn't even knowable
without much pre-measurement, e.g., fMRI prior to the expression. It could be she taught herself how to spin her own emotions up, and in the moment, they were "real" emotions even though they were choregraphed in a rational way. When it comes to simulation
or generative learning, there is the natural latency scale for humans of say 1/10<span><sup>th</sup> of a second, and so long as a calculation could beat that computational deadline, then it could insert whatever dramatic "rest notes" that were needed to appear
physiologically plausible.</span></div>
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<div class="PlainText elementToProof">Marcus</div>
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