“Our limited conception of what understanding actually involves makes it hard to answer basic questions: How do we know if a system is ‘actually understanding’? What metrics can we use? Could machines be said to ‘understand’ differently from humans?” Mitchell writes in her paper.
What made this specific study interesting was the broad range of perspectives brought together to tackle this complicated topic. Participants in the workshop came from various disciplines, including AI, robotics, cognitive and developmental psychology, animal behavior, information theory, and philosophy, among others.
“When I first got into AI, there was a real interdisciplinary feel to it. AI people attended cognitive science conferences, and vice versa. Then statistics took over AI, and the field got less diverse,” Mitchell said. “But I see a trend now in the field returning to its interdisciplinary roots, which I think is a very positive development.”
The paper includes many examples from studies in fields other than computer science and robotics, which help appreciate the depth of meaning in living beings.