Recent scientific discoveries have paved the way for AI that can respond appropriately to our emotional behaviors.
Hume argues that emotions drive choice and well-being.
“Reason [by humans and AI] is and ought only to be the slave of the passions [that is, human emotion]” - David Hume. At Hume AI, we take this as a guiding principle behind ethical AI: in order to serve our preferences, algorithms should be guided by our emotions.
Recognizing the need to map out the emotions that animate thought and action, Hume also proposed a taxonomy of over 16 emotional states, but lacked scientific evidence.
Darwin surveys human emotion.
In Expression of Emotion in Man and Animals, Charles Darwin described similarities and differences in over 20 facial, bodily, and vocal expressions across mammalian species, diverse cultures, and stages of life.
Of course, Darwin himself lacked the quantitative tools to formally test his hypotheses about human emotion.
150 years later, studies are still confirming many of Darwin’s observations.
Ekman documents six facial expressions.
Paul Ekman traveled the world to find that six expressions are universally recognized. By focusing on a narrow set of behaviors, Ekman was able to use the statistical methods available to him to confirm some of Darwin’s ideas.
However, the focus on just six emotions also introduced what we call the 30% problem: the focus of scientists for 50 years on only 30% of the full range of emotions people experience.
Scientists try to reduce human emotion.
While many scientists continue to focus on six emotions, others pursue a more ambitious goal: the effort to derive an emotion taxonomy from data. However, due to statistical limitations, these results lead to even more reductive theories of emotion.
Some scientists endorse “core affect”: the notion that emotions are largely captured by how pleasant or unpleasant and calm or aroused an experience or expression seems.