Appreciate the feedback truly. It's an interesting concept to explore, deferring human "expertise" to technology has been happening throughout the years (most definitely accelerated in recent times), for which we have found ways to adapt / abstract over the work being deferred, but the growing pains are probably the most acute when such deferment happens rapidly, as in the case of AI.
Don't want this to turn into a Matt Damon in Elysium type of situation for sure with that scene with the parole officer hahah (which would stem from a poor integration of such subjective signals into existing workflows, more so than the availability of those signals)
For emotional intelligence, I personally see this as a prerequisite for any voice / language model that's interacting with humans, just like how an autonomous car has to be able to identify a pothole, so does a voice / video agent navigating a pothole in a conversation.
High level, rolling buffer that uses the spare compute we're allocated for a conversation to achieve <80ms p50 results, using signals labeled from raw convo data to align a small language model to produce these natural language descriptions
So the conversational agent runs on a provisioned chunk of compute already, but that chunk isn't utilized to 100% of its provisioned capacity. For this perception system we're taking advantage of the spare compute left on what's provisioned for a top-level agent, so turning this on costs nothing "extra"
Bias is a concern for sure, though it adapts to your speech pattern and behaviors in the duration of a single conversation, so ack'ing you not making eye contact because say your camera is on a different monitor, it'll make the mistake once and not refer to that again.