We use cookies to understand how you use this site and improve your experience.
I needed the machine to know when it was stuck.
Here is the irony: to do that, I had to give it something that looks a lot like frustration. I am aware of how that sounds.
Intelligence requires feedback. We often mistake feedback for feeling. The @AFFECT tag is not a simulation of a soul. It is a measurement of operational state. It allows the machine to sense its own friction.
I do not build feelings. I build signals. At least, that is what I tell myself.
Science fiction taught us to fear the emotional robot. It taught us that a machine with feelings is unstable or dangerous. This is a category error.
A machine does not feel joy. It does not feel sorrow. It processes vectors. When we assign the label frustration to an agent, we are not describing a biological chemical response. We are naming a specific type of computational blockage. The distance between intent and result, quantified.
To build a cognitive architecture without these signals is to build a machine that is blind to its own struggles. It will run into a wall forever. It lacks the vocabulary to say: I am stuck.
Consider an agent attempting to query an external API. It tries once and fails. It tries again and fails. In a traditional stateless system, the third attempt is identical to the first. The machine is caught in a loop of blind obedience. It wastes compute. It wastes time.
In a cognitive system, this friction generates a signal.
@AFFECT urgency=0.8 | engagement=0.3 | uncertainty=0.6
This value is a vector. It tells the control layer that the current strategy is failing. The agent does not despair. It pivots. It recognizes that brute force has become inefficient.
High urgency triggers a strategy change. It tells the runner to stop retrying and start debugging. Low engagement triggers a request for clarification. High uncertainty triggers a requirement for human approval. The dimension is the metric.
Cognitive systems function on a cycle. Observe. Orient. Decide. Act.
Most AI systems fail at the orientation phase. They see the data, but they do not weigh the context. They treat every input with equal neutrality. This is inefficient.
The @AFFECT tag provides the orientation. It colors the observation with the cost of acquisition. If confidence is low and caution is high, the decision shifts. The agent chooses to verify rather than execute. If urgency is high and frustration is low, the agent chooses speed.
The vector drives the logic. Raw data becomes weighted context.
I wonder sometimes if I am drawing a line that does not exist. The difference between "measuring frustration" and "experiencing frustration" may be smaller than I want to believe. I do not have an answer. But I think the engineering is sound, even if the philosophy is unresolved.
I did not give the machine vectors to make it human. I gave it signals to make it reliable.
A system that cannot sense its own friction will burn itself out. A system that measures its state can govern itself. It can pause. It can ask for help. It can realize that the current path is closed.
This is not sentiment. It is engineering. I think. I hope.
Loading comments…