A New Theoretical Framework for Testing Consciousness in a Machine

Abstract

The major aim of artificial general intelligence’s (AGI) is to allow a machine to perform general intelligence tasks similar to human counterparts. Hypothetically, this general intelligence in a machine can be achieved by establishing cross-domain optimization and learning machine approaches. However, contemporary artificial intelligence (AI) capabilities are only limited to narrow and specific domains utilizing machine learning. Consciousness concept is particularly interesting topic to attain the approaches because it simultaneously encodes and processes all types of information and seamlessly integrates them. Over the last several years, there has been a resurgence of interest in testing theories of consciousness using computer models. The studies of these models are classified into four categories: external behavior associated with consciousness, cognitive characteristics associated with consciousness, a computational architecture correlate of human consciousness and phenomenally of conscious machine. The critical challenge is to determine whether these artificial systems are capable of conscious states by providing a measurement the extent to which the systems are succeeded in realizing consciousness in a machine. Several tests for machine consciousness have been proposed yet their formulation is based on extrinsic measurement of consciousness. Yet extrinsic measurement is not inclusive because many conscious artificial systems behave implicitly. This research proposes a new framework to test machine consciousness based on intrinsic measurement so-called Pak Pandir test. The framework leverages three quantum double-slit settings and information integration theory as consciousness definition of choice.