Alan Turing and the "Hard" and "Easy" Problem of Cognition: Doing and Feeling
This is an incremental philosophical analysis of cognitive science and AI, relevant to researchers in philosophy of mind and AI ethics.
The paper tackles the distinction between the 'easy' problem of explaining cognitive abilities and the 'hard' problem of explaining subjective experience, arguing that Turing's methodology based on behavior is insufficient and that even grounded robotic models may not address consciousness.
The "easy" problem of cognitive science is explaining how and why we can do what we can do. The "hard" problem is explaining how and why we feel. Turing's methodology for cognitive science (the Turing Test) is based on doing: Design a model that can do anything a human can do, indistinguishably from a human, to a human, and you have explained cognition. Searle has shown that the successful model cannot be solely computational. Sensory-motor robotic capacities are necessary to ground some, at least, of the model's words, in what the robot can do with the things in the world that the words are about. But even grounding is not enough to guarantee that -- nor to explain how and why -- the model feels (if it does). That problem is much harder to solve (and perhaps insoluble).