Consciousness is a prerequisite for thought, so asking whether a machine can think leads to the more fundamental question of whether a machine can be conscious. In Turing's imitation game, a conscious human is replaced by a non-conscious machine that may deceive an interlocutor, since consciousness cannot be observed from speech or action. This paper examines the developing paradigm of machine consciousness alongside an existing analysis of living consciousness to argue that a conscious machine is feasible and capable of thinking. The proposed route uses learning in a "neural state machine," drawing on Turing's concept of neural "unorganized" machines. The conclusion is that such a machine could possess an artificial form of consciousness resembling the natural form, illuminating the nature of consciousness.
A review of work from several laboratories on modeling consciousness, ranging from functional models that prioritize behavior to material models grounded in brain anatomy. Functional approaches include applications for job-finding where a machine must be indistinguishable from a conscious human, using global workspace theories. Material approaches model attentional brain mechanisms and biochemical processes in children. The chapter distinguishes between attempts to model phenomenology (synthetic phenomenology) and those that do not. Studying consciousness through machine design is expected to produce a computational language for expressing consciousness and computational methods for building flexible, competent machinery.