Journal of Artificial Intelligence and Consciousness

A conscious machine category is defined for which the process of becoming conscious is important. This is distinguished from the artificial intelligence category of machines and contrasted with being a conscious living organism. An example is given of a neural automaton for which becoming conscious is seen to be equated to the growth of a depictive state structure. Existing works and future possibilities are examined against this categoric distinction.

If artificial agents are to be created such that they occupy space in our social and cultural milieu, then we should expect them to be targets of folk psychological explanation. That is to say, their behavior ought to be explicable in terms of beliefs, desires, obligations, and especially intentions. Herein, we focus on the concept of intentional action, and especially its relationship to consciousness. After outlining some lessons learned from philosophy and psychology that give insight into the structure of intentional action, we find that attention plays a critical role in agency, and indeed, in the production of intentional action. We argue that the insights offered by the literature on agency and intentional action motivate a particular kind of computational cognitive architecture, and one that hasn’t been well-explicated or computationally fleshed out among the community of AI researchers and computational cognitive scientists who work on cognitive systems. To give a sense of what such a system might look like, we present the ARCADIA attention-driven cognitive system as first steps toward an architecture to support the type of agency that rich human–machine interaction will undoubtedly demand.

The main problem for AI consciousness is to operate within the right kind of AI. We distinguish between the traditional computing (GOFAI), and the computing based on stochastic pattern optimization. The latter will be called here computing at the edge of chaos. Optimization of learning patterns, which is the gist of its success, often happens between the areas of too much repetitive order and those of hard to predict and control stochastic processes. This is to change the focus from the opposition of symbolic versus sub-symbolic computing; symbols can appear at different granularities and the hedge between The Physical Symbol System Hypothesis and neural nets seems no longer the most productive cut to make. Computing at the edge of chaos is promising for AGI, especially for AGI consciousness. The second problem for AI consciousness is to work with the right definitions of consciousness.

Insofar as consciousness has a functional role in facilitating learning and behavioral control, the builders of autonomous Artificial Intelligence (AI) systems are likely to attempt to incorporate it into their designs. The extensive literature on the ethics of AI is concerned with ensuring that AI systems, and especially autonomous conscious ones, behave ethically. In contrast, our focus here is on the rarely discussed complementary aspect of engineering conscious AI: how to avoid condemning such systems, for whose creation we would be solely responsible, to unavoidable suffering brought about by phenomenal self-consciousness. We outline two complementary approaches to this problem, one motivated by a philosophical analysis of the phenomenal self, and the other by certain computational concepts in reinforcement learning.

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.

In the last 10 years, Artificial Intelligence (AI) has seen successes in fields such as natural language processing, computer vision, speech recognition, robotics and autonomous systems. However, these advances are still considered as Narrow AI, i.e. AI built for very specific or constrained applications. These applications have its usefulness in improving the quality of human life; but it is not good enough to do highly general tasks like what the human can do. The holy grail of AI research is to develop Strong AI or Artificial General Intelligence (AGI), which produces human-level intelligence, i.e. the ability to sense, understand, reason, learn and act in dynamic environments. Strong AI is more than just a composition of Narrow AI technologies. We proposed that it has to be a holistic approach towards understanding and reacting to the operating environment and decision-making process. The Strong AI must be able to demonstrate sentience, emotional intelligence, imagination, effective command of other machines or robots, and self-referring and self-reflecting qualities. This paper will give an overview of current Narrow AI capabilities, present the technical gaps, and highlight future research directions for Strong AI. Could Strong AI become conscious? We provide some discussion pointers.

The popular expectation is that Artificial Intelligence (AI) will soon surpass the capacities of the human mind and Strong Artificial General Intelligence (AGI) will replace the contemporary Weak AI. However, there are certain fundamental issues that have to be addressed before this can happen. There can be no intelligence without understanding, and there can be no understanding without getting meanings. Contemporary computers manipulate symbols without meanings, which are not incorporated in the computations. This leads to the Symbol Grounding Problem; how could meanings be incorporated? The use of self-explanatory sensory information has been proposed as a possible solution. However, self-explanatory information can only be used in neural network machines that are different from existing digital computers and traditional multilayer neural networks. In humans, self-explanatory information has the form of qualia. To have reportable qualia is to be phenomenally conscious. This leads to the hypothesis about an unavoidable connection between the solution of the Symbol Grounding Problem and consciousness. If, in general, self-explanatory information equals to qualia, then machines that utilize self-explanatory information would be conscious.

The philosophical legacy of the idea that there must be something it is like to be a conscious organism, together with an inclination to regard organic life forms as having qualities fundamentally distinct from other physical systems, have adversely affected the development of the nascent discipline of machine consciousness. This paper highlights some of the factors involved, and as a corrective proposes a reframing of machine consciousness within a thoroughgoing engineering context. This is shown to offer some significant avenues for progress, while bringing with it some theoretical problems requiring further consideration such as the status of the possible consciousness of a wholly virtual system.

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.

The young field of AI Safety is still in the process of identifying its challenges and limitations. In this paper, we formally describe one such impossibility result, namely Unpredictability of AI. We prove that it is impossible to precisely and consistently predict what specific actions a smarter-than-human intelligent system will take to achieve its objectives, even if we know the terminal goals of the system. In conclusion, the impact of Unpredictability on AI Safety is discussed.

There is an ongoing debate about the existence of humanoid robotics. The arguments tend to focus on the ethical claims that there is always deception involved. However, little attention has been paid to the ontological reasons that humanoid robotics are valuable in consciousness research. This paper examines the arguments and controversy around ethical rejection of humanoid robotics, while also summarizing some of the landscape of 4e cognition that highlights the ways our specific humanoid bodies in our specific cultural, social, and physical environments play an indispensable role in cognition, from conceptualization through communication. Ultimately, we argue that there is a compelling set of reasons to pursue humanoid robotics as a major research agenda in AI if the goal is to create an artificial conscious system that we will be able to both recognize as conscious and communicate with successfully.

This report introduces the activities of eight one-week workshops that were held during the summer of 2017 on the topic “Technology and Consciousness.” Participants in the series of workshops approached the subject from many different perspectives, with the overall goal of exploring the possibility of machine consciousness, and assessing its potential implications. The body of this initial paper summarizes the overview topics and basic introductory themes that were discussed during the early part of the workshop series. Follow-on papers will address the key focus areas that were examined in depth during the course of the full series.