Of course not is the intuitive and immediate answer that this question would most likely get. But let’s not be so fast. There is a field in computing called Affective Computing whose goal is to create machines that can detect and interpret human emotions. It uses the appraisal theory of emotions as its guide. This theory is one of the most influential theories of emotions and it is able to bridge the gap between emotions and the symbolic reasoning process favored by the builders of artificial intelligence (AI).
According to the Appraisal Theory, humans determine how they feel about a certain event and entity using a series of template evaluation process. First, we evaluate how novel or familiar something is. Then, we weigh how likely it is to cause a positive or a negative experience in congruence with our goals. Then we evaluate our coping potential: can we influence and control it, can we change it in ways that makes it beneficial to us. Our brains ultimately translate this series of evaluations into a certain feeling towards the event or entity. If it is something familiar, pleasant, reasonably controllable, and congruent with our goals, we are likely to experience positive emotions towards it. If it is something new, unpleasant, out of control, and incongruent with our goals, we are likely to hate it.
So What Does This Mean For Machines?
If the emotional process can be broken down into a series of evaluations with more or less binary outcomes (novel/familiar, controllable/uncontrollable, etc.), then these operations can be performed not just by a human but by a machine too. If we could develop algorithms parallel to the process human brains use to make decisions about familiarity, valence, controllability, and goal congruence, the process through which an emotion is created can be performed by a machine.
Admittedly, we are a long way from the moment when a machine will feel happy or scared, but the Appraisal Theory of emotions provides a roadmap for how we can teach machines to process human emotions.
Stay tuned to learn how machines can learn to detect and interpret human emotions.
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