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The question about the potential for consciousness of artificial systems has often been addressed using thought experiments, which are often problematic in the philosophy of mind. A more promising approach is to use real experiments to gather data about the correlates of consciousness in humans, and develop this data into theories that make predictions about human and artificial consciousness. A key issue with an experimental approach is that consciousness can only be measured using behavior, which places fundamental limits on our ability to identify the correlates of consciousness. This paper formalizes these limits as a distinction between type I and type II potential correlates of consciousness (PCCs). Since it is not possible to decide empirically whether type I PCCs are necessary for consciousness, it is indeterminable whether a machine that lacks neurons or hemoglobin, for example, is potentially conscious. A number of responses have been put forward to this problem, including suspension of judgment, liberal and conservative attribution of the potential for consciousness and a psychometric scale that models our judgment about the relationship between type I PCCs and consciousness.

Research is starting to identify correlations between consciousness and some of the spatiotemporal patterns in the physical brain. For theoretical and practical reasons, the results of experiments on the correlates of consciousness have ambiguous interpretations. At any point in time a number of hypotheses co-exist about and the correlates of consciousness in the brain, which are all compatible with the current experimental results. This paper argues that consciousness should be attributed to any system that exhibits spatiotemporal physical patterns that match the hypotheses about the correlates of consciousness that are compatible with the current experimental results. Some computers running some programs should be attributed consciousness because they produce spatiotemporal patterns in the physical world that match those that are potentially linked with consciousness in the human brain.

Human and Machine Consciousness presents a new foundation for the scientific study of consciousness. It sets out a bold interpretation of consciousness that neutralizes the philosophical problems and explains how we can make scientific predictions about the consciousness of animals, brain-damaged patients and machines.

This paper explores some of the potential connections between natural and artificial intelligence and natural and artificial consciousness. In humans we use batteries of tests to indirectly measure intelligence. This approach breaks down when we try to apply it to radically different animals and to the many varieties of artificial intelligence. To address this issue people are starting to develop algorithms that can measure intelligence in any type of system. Progress is also being made in the scientific study of consciousness: we can neutralize the philosophical problems, we have data about the neural correlates and we have some idea about how we can develop mathematical theories that can map between physical and conscious states. While intelligence is a purely functional property of a system, there are good reasons for thinking that consciousness is linked to particular spatiotemporal patterns in specific physical materials. This paper outlines some of the weak inferences that can be made about the relationships between intelligence and consciousness in natural and artificial systems. To make real scientific progress we need to develop practical universal measures of intelligence and mathematical theories of consciousness that can reliably map between physical and conscious states.

A systematic understanding of the relationship between intelligence and consciousness can only be achieved when we can accurately measure intelligence and consciousness. In other work, I have suggested how the measurement of consciousness can be improved by reframing the science of consciousness as a search for mathematical theories that map between physical and conscious states. This paper discusses the measurement of intelligence in natural and artificial systems. While reasonable methods exist for measuring intelligence in humans, these can only be partly generalized to non-human animals and they cannot be applied to artificial systems. Some universal measures of intelligence have been developed, but their dependence on goals and rewards creates serious problems. This paper sets out a new universal algorithm for measuring intelligence that is based on a system’s ability to make accurate predictions. This algorithm can measure intelligence in humans, non-human animals and artificial systems. Preliminary experiments have demonstrated that it can measure the changing intelligence of an agent in a maze environment. This new measure of intelligence could lead to a much better understanding of the relationship between intelligence and consciousness in natural and artificial systems, and it has many practical applications, particularly in AI safety.

Humans are highly intelligent, and their brains are associated with rich states of consciousness. We typically assume that animals have different levels of consciousness, and this might be correlated with their intelligence. Very little is known about the relationships between intelligence and consciousness in artificial systems. Most of our current definitions of intelligence describe human intelligence. They have severe limitations when they are applied to non-human animals and artificial systems. To address this issue, this chapter sets out a new interpretation of intelligence that is based on a system’s ability to make accurate predictions. Human intelligence is measured using tests whose results are converted into values of IQ and g-score. This approach does not work well with non-human animals and AIs, so people have been developing universal algorithms that can measure intelligence in any type of system. In this chapter a new universal algorithm for measuring intelligence is described, which is based on a system’s ability to make accurate predictions. Many people agree that consciousness is the stream of colorful moving noisy sensations that starts when we wake up and ceases when we fall into deep sleep. Several mathematical algorithms have been developed to describe the relationship between consciousness and the physical world. If these algorithms can be shown to work on human subjects, then they could be used to measure consciousness in non-human animals and artificial systems.

A.I.: Artificial Intelligence tells the story of a robot boy who has been engineered to love his human owner. He is abandoned by his owner and pursues a tragic quest to become a real boy so that he can be loved by her again. This chapter explores the philosophical, psychological, and scientific questions that are asked by A.I. It starts with A.I.’s representation of artificial intelligence and then covers the consciousness of robots, which is closely linked to ethical concerns about the treatment of AIs in the film. There is a discussion about how A.I.’s interpretation of artificial love relates to scientific work on emotion, and the chapter also examines connections between the technology portrayed in A.I. and current research on robotics.