>I don’t think we gain much at all from trying to attach human labels to these machines.
That's the standard way of testing whether the neural network has learned to extract "useful" ("meaningful"?) representation from the data : You add very few layers on top of the frozen inner-state of a neural network, and make him predict known human labels, like is the music sad, or is it happy.
If it can do so with very few additional weights, it means it has already learn in its inner representation what makes a song sad or happy.
I agree that I didn't gave a precise definition a what "emotion" is. But if we had to define what emotion is for a neural network : traditional continuous vectors does fit quite well the emotions concept though. You can continuously modify them a little and they map/embed a high-dimensional space into a more meaningful lower-dimensional space where semantically near emotions are numerically near.
For example if you have identified a "sad" neuron that when it light-up the network tend to produce sad music, and a "happy" neuron that when it light-up the network tend to produce happy music, you can manually increase these neuron values to make it produce the music you want. You can interpolate to morph one emotion into the other and generate some complex mix in-between.
Neurons are quite literally adding-up and comparing the various vectors values of the previous layers to decide whether they should activate or not, (aka balancing "emotions").
Humans and machine are tasked to learn to handle data. It's quite natural that some of the mechanism useful for data manipulation emerge in both cases and correspond to each other. For example : the fetching of emotionally-related content to the working context maps quite clearly a near neighbor search to what happens when people say they have "flashing" memories when they experience some particular emotions.
That's the standard way of testing whether the neural network has learned to extract "useful" ("meaningful"?) representation from the data : You add very few layers on top of the frozen inner-state of a neural network, and make him predict known human labels, like is the music sad, or is it happy.
If it can do so with very few additional weights, it means it has already learn in its inner representation what makes a song sad or happy.
I agree that I didn't gave a precise definition a what "emotion" is. But if we had to define what emotion is for a neural network : traditional continuous vectors does fit quite well the emotions concept though. You can continuously modify them a little and they map/embed a high-dimensional space into a more meaningful lower-dimensional space where semantically near emotions are numerically near.
For example if you have identified a "sad" neuron that when it light-up the network tend to produce sad music, and a "happy" neuron that when it light-up the network tend to produce happy music, you can manually increase these neuron values to make it produce the music you want. You can interpolate to morph one emotion into the other and generate some complex mix in-between.
Neurons are quite literally adding-up and comparing the various vectors values of the previous layers to decide whether they should activate or not, (aka balancing "emotions").
Humans and machine are tasked to learn to handle data. It's quite natural that some of the mechanism useful for data manipulation emerge in both cases and correspond to each other. For example : the fetching of emotionally-related content to the working context maps quite clearly a near neighbor search to what happens when people say they have "flashing" memories when they experience some particular emotions.