While the concept of a conscious machine is intriguing, producing such a machine remains controversial and challenging. Here, we describe how our work on creating a humanoid cognitive robot that learns to perform tasks via imitation learning relates to this issue. Our discussion is divided into three parts. First, we summarize our previous framework for advancing the understanding of the nature of phenomenal consciousness. This framework is based on identifying computational correlates of consciousness. Second, we describe a cognitive robotic system that we recently developed that learns to perform tasks by imitating human-provided demonstrations. This humanoid robot uses cause-effect reasoning to infer a demonstrator's intentions in performing a task, rather than just imitating the observed actions verbatim. In particular, its cognitive components center on top-down control of a working memory that retains the explanatory interpretations that the robot constructs during learning. Finally, we describe our ongoing work that is focused on converting our robot's imitation learning cognitive system into purely neurocomputational form, including both its low-level cognitive neuromotor components, its use of working memory, and its causal reasoning mechanisms. Based on our initial results, we argue that the top-down cognitive control of working memory, and in particular its gating mechanisms, is an important potential computational correlate of consciousness in humanoid robots. We conclude that developing high-level neurocognitive control systems for cognitive robots and using them to search for computational correlates of consciousness provides an important approach to advancing our understanding of consciousness, and that it provides a credible and achievable route to ultimately developing a phenomenally conscious machine.

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Recent advances in philosophical thinking about consciousness, such as cognitive phenomenology and mereological analysis, provide a framework that facilitates using computational models to explore issues surrounding the nature of consciousness. Here we suggest that, in particular, studying the computational mechanisms of working memory and its cognitive control is highly likely to identify computational correlates of consciousness and thereby lead to a deeper understanding of the nature of consciousness. We describe our recent computational models of human working memory and propose that three computational correlates of consciousness follow from the results of this work: itinerant attractor sequences, top-down gating, and very fast weight changes. Our current investigation is focused on evaluating whether these three correlates are sufficient to create more complex working memory models that encompass compositionality and basic causal inference. We conclude that computational models of working memory are likely to be a fruitful approach to advancing our understanding of consciousness in general and in determining the long-term potential for development of an artificial consciousness specifically.

The field of artificial consciousness (AC) has largely developed outside of mainstream artificial intelligence (AI), with separate goals and criteria for success and with only a minimal exchange of ideas. This is unfortunate as the two fields appear to be synergistic. For example, here we consider the question of how concepts developed in AC research might contribute to more effective future AI systems. We first briefly discuss several past hypotheses about the function(s) of human consciousness, and present our own hypothesis that short-term working memory and very rapid learning should be a central concern in such matters. In this context, we then present ideas about how integrating concepts from AC into AI systems to develop an artificial conscious intelligence (ACI) could both produce more effective AI technology and contribute to a deeper scientific understanding of the fundamental nature of consciousness and intelligence.

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