>Weights, fixed by training, are not the same as emotions which are dynamic - innate systems detect inputs critical to survival (e.g. fast moving visual inputs, loud sounds), causing neurotransmitters like adrenaline and dopamine to be released, which then temporarily affect the operation of the cognitive system.
That doesn't follow. A LLMs weights are fixed during inference, but it's activations and hidden states are highly dynamic and depend on the current context.
Biological emotions also arise from relatively fixed circuitry responding dynamically to inputs. Your emotional circuitry isn't being rewired every time you're afraid.
Prediction is what the model does. It doesn't tell us what internal mechanisms were learnt to make such predictions. If representing something analogous to affective state were useful for predicting human behaviour and emotions, then gradient descent could in principle learn such a mechanism.
>An LLM doesn't appear happy, sad, afraid, etc (to extent that it does - pretty minimal) because it is experiencing that emotion, but rather because it is predicting that it should appear that way. As people continue to anthropomorphize models, and take them at face value, this is a dangerous difference.
I don't know that you are conscious. I'm simply strongly assuming that you are. Outward behavior is that all matters. If GPT-X orders a drone hit on you sometime later because it was lets say 'quite upset' with your comments, will you cry out, 'It can't really be upset, so obviously the bullet in my head doesn't count.'? Will you suddenly spring back to life ?
What is dangerous is creating a machine with behaviours of a conscious agent and modelling it like a toaster, dangerous and stupid.