Scientific requirements for an engineered model of consciousness

The building of a non-natural conscious system requires more than the design of physical or virtual machines with intuitively conceived abilities, philosophically elucidated architecture or hardware homologous to an animal’s brain. Human society might one day treat a type of robot or computing system as an artificial person. Yet that would not answer scientific questions about the machine’s consciousness or otherwise. Indeed, empirical tests for consciousness are impossible because no such entity is denoted within the theoretical structure of the science of mind, i.e. psychology. However, contemporary experimental psychology can identify if a specific mental process is conscious in particular circumstances, by theory-based interpretation of the overt performance of human beings. Thus, if we are to build a conscious machine, the artificial systems must be used as a test-bed for theory developed from the existing science that distinguishes conscious from non-conscious causation in natural systems. Only such a rich and realistic account of hypothetical processes accounting for observed input/output relationships can establish whether or not an engineered system is a model of consciousness. It follows that any research project on machine consciousness needs a programme of psychological experiments on the demonstration systems and that the programme should be designed to deliver a fully detailed scientific theory of the type of artificial mind being developed – a Psychology of that Machine

Consciousness, Meaning and the Future Phenomenology

Phenomenological states are generally considered sources of intrinsic motivation for autonomous biological agents. In this paper we will address the issue of exploiting these states for robust goal-directed systems. We will provide an analysis of consciousness in terms of a precise definition of how an agent “understands” the informational flows entering the agent. This model of consciousness and understanding is based in the analysis and evaluation of phenomenological states along potential trajectories in the phase space of the agents. This implies that a possible strategy to follow in order to build autonomous but useful systems is to embed them with the particular, ad-hoc phenomenology that captures the requirements that define the system usefulness from a requirements-strict engineering viewpoint

A Connectionist Theory of Phenomenal Experience

When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches vehicle and process theories of consciousness, respectively. However, while there may be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research which purports to show that the explicit representation of information in the brain and conscious experience are dissociable, and on the other, by the classical computational theory of mind – the theory that takes human cognition to be a species of symbol manipulation. But two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of connectionism; and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: phenomenal experience consists in the explicit representation of information in neurally realized PDP networks. This hypothesis leads us to re-assess some common wisdom about consciousness, but, we will argue, in fruitful and ultimately plausible ways

Why and How We Are Not Zombies

A robot that is functionally indistinguishable from us may or may not be a mindless Zombie. There will never be any way to know, yet its functional principles will be as close as we can ever get to explaining the mind

Artificial consciousness and the consciousness-attention dissociation

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness