How has multiplicity superseded impossibility in philosophical challenges to artificial consciousness? I assess a trajectory in recent debates on artificial consciousness, in which metaphysical and explanatory challenges to the possibility of building conscious machines lead to epistemological concerns about the multiplicity underlying ‘what it is like’ to be a conscious creature or be in a conscious state. First, I analyse earlier challenges which claim that phenomenal consciousness cannot arise, or cannot be built, in machines. These are based on Block’s Chinese (...) Nation and Chalmers’ Hard Problem. To defuse such challenges, theorists of artificial consciousness can appeal to empirical methods and models of explanation. Second, I explain why this naturalistic approach produces an epistemological puzzle on the role of biological properties in phenomenal consciousness. Neither behavioural tests nor theoretical inferences seem to settle whether our machines are conscious. Third, I evaluate whether the new challenge can be managed through a more fine-grained taxonomy of conscious states. This strategy is supported by the development of similar taxonomies for biological species and animal consciousness. Although it makes sense of some current models of artificial consciousness, it raises questions about their subjective and moral significance. (shrink)

This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI safety; (...) and cutting-edge developments in techniques to achieve AI, including machine learning, neural networks, dynamical systems. The book also discusses important applications of AI, including big data analytics, expert systems, cognitive architectures, and robotics. It offers a timely, yet very comprehensive snapshot of what is going on in the field of AI, especially at the interfaces between philosophy, cognitive science, ethics and computing. (shrink)

Human excellences such as intelligence, morality, and consciousness are investigated by philosophers as well as artificial intelligence researchers. One excellence that has not been widely discussed by AI researchers is practical wisdom, the highest human excellence, or the highest, seventh, stage in Dreyfus’s model of skill acquisition. In this paper, I explain why artificial wisdom matters and how artificial wisdom is possible (in principle and in practice) by responding to two philosophical challenges to building artificial wisdom systems. The result is (...) a conceptual framework that guides future research on creating artificial wisdom. (shrink)

Research into the ethics of artificial intelligence is often categorized into two subareas—robot ethics and machine ethics. Many of the definitions and classifications of the subject matter of these subfields, as found in the literature, are conflated, which I seek to rectify. In this essay, I infer that using the term ‘machine ethics’ is too broad and glosses over issues that the term computational ethics best describes. I show that the subject of inquiry of computational ethics is of great value (...) and indeed is an important frontier in developing ethical artificial intelligence systems. I also show that computational is a distinct, often neglected field in the ethics of AI. In contrast to much of the literature, I argue that the appellation ‘machine ethics’ does not sufficiently capture the entire project of embedding ethics into AI/S, and hence the need for computational ethics. This essay is unique for two reasons; first, it offers a philosophical analysis of the subject of computational ethics that is not found in the literature. Second, it offers a finely grained analysis that shows the thematic distinction among robot ethics, machine ethics and computational ethics. (shrink)

The term \synthetic phenomenology" refers to: 1) any attempt to characterize the phenomenal states possessed, or modeled by, an artefact ; or 2) any attempt to use an artefact to help specify phenomenal states. The notion of synthetic phenomenology is clari¯ed, and distinguished from some related notions. It is argued that much work in machine consciousness would bene¯t from being more cognizant of the need for synthetic phenomenology of the ¯rst type, and of the possible forms it may take. It (...) is then argued that synthetic phenomenology of the second type looks set to resolve some problems confronted by standard, nonsynthetic attempts at characterizing phenomenal states. An example of the second form of synthetic phenomenology is given. (shrink)

The study outlines the existing and potential philosophical issues of the idea of conscious machines originated from the development of artificial consciousness within the framework of contemporary research of artificial intelligence and cognitive robotics. The outline shows that the idea of conscious machines is concerned with two big philosophical issues. The first philosophical issue is a definition of consciousness, taking into account the selection of a set of objects that can have consciousness, the typology of consciousness, the clarifying of the (...) nature of consciousness' carriers and the relationship between consciousness and its environment and the relationship between consciousness and language, in order to create an artificial consciousness within a machine, making that machine conscious. The second philosophical issue is a clarification whether only artificially created machines can be conscious machines, or cyborgizated human beings also can be considered of conscious machines. These philosophical issues show that there can be two ways to create conscious machines: 1) the creating of artificial consciousness within artificially created machine; and 2) the cyborgization of human being, transforming it into artificially created machine possessed natural consciousness. (shrink)

Machine awareness is a disputed research topic, in some circles considered a crucial step in realising Artificial General Intelligence. Understanding what that is, under which conditions such feature could arise and how it can be controlled is still a matter of speculation. A more concrete object of theoretical analysis is algorithmic iteration for computational intelligence, intended as the theoretical and practical ability of algorithms to design other algorithms for actions aimed at solving well-specified tasks. We know this ability is already (...) shown by current AIs, and understanding its limits is an essential step in qualifying claims about machine awareness and Super-AI. We propose a formal translation of algorithmic iteration in a fragment of modal logic, formulate principles of transparency and faithfulness across human and machine intelligence, and consider the relevance to theoretical research on -AI as well as the practical import of our results. (shrink)

Ex-Machina and Morgan, two recent science-fiction films that deal with the creation of humanoids, also explored the relationship between artificial intelligence, spatiality and the lingering question mark regarding artificial consciousness. In both narratives, the creators of the humanoids have tried to mimic human consciousness as closely as possible, which has resulted in the imprisonment of the humanoids due to proprietary concerns in Ex-Machina and due to the violent behavior of the humanoid in Morgan. This article addresses the dilemma of whether (...) or not the humanoids in both films possess high levels of artificial consciousness and its possible consequences regarding focalization, a narrative term that presupposes subjectivity, as well as offer two new categories of posthuman focalization—X-focalization and A-focalization. The issue of captivity also has far-reaching ethical implications when considering the underlying assumption of artificial consciousness—if humanoids are indeed endowed with a subjective inner life, then they are entitled to be treated as moral agents, equivalent to humans rather than animals. (shrink)

We introduce an innovative technique that quantifies human expertise development in such a way that humans and artificial systems can be directly compared. Using this technique we are able to highlight certain fundamental difficulties associated with the learning of a complex task that humans are still exceptionally better at than their computer counterparts. We demonstrate that expertise goes through significant developmental transitions that have previously been predicted but never explicated. The first signals the onset of a steady increase in global (...) awareness that begins surprisingly late in expertise acquisition. The second transition, reached by only a very few experts in the world, shows a major reorganisation of global contextual knowledge resulting in a relatively minor gain in skill. We are able to show that these empirical findings have consequences for our understanding of the way in which expertise acquisition may be modelled by learning in artificial intelligence systems. This point is emphasised with a novel theoretical result showing explicitly how our findings imply a non-trivial hurdle for learning for suitably complex tasks. (shrink)

This paper describes how Tononi’s information integration theory of consciousness was used to make detailed predictions about the distribution of phenomenal states in a spiking neural network. This network had approximately 18,000 neurons and 700,000 connections and it used models of emotion and imagination to control the eye movements of a virtual robot and avoid ‘negative’ stimuli. The first stage in the analysis was the development of a formal definition of Tononi’s theory of consciousness. The network was then analysed for (...) information integration and detailed predictions were made about the distribution of consciousness for each time step of recorded activity. This work demonstrates how an artificial system can be analysed for consciousness using a particular theory and in the future this approach could be used to make predictions about the phenomenal states associated with biological systems. (shrink)

This paper seeks to identify, clarify, and perhaps rehabilitate the virtual reality metaphor as applied to the goal of understanding consciousness. Some proponents of the metaphor apply it in a way that implies a representational view of experience of a particular, extreme form that is indirect, internal and inactive (what we call “presentational virtualism”). In opposition to this is an application of the metaphor that eschews representation, instead preferring to view experience as direct, external and enactive (“enactive virtualism”). This paper (...) seeks to examine some of the strengths and weaknesses of these virtuality-based positions in order to assist the development of a related, but independent view of experience: virtualist representationalism. Like presentational virtualism, this third view is representational, but like enactive virtualism, it places action centre-stage, and does not require, in accounting for the richness of visual experience, global representational “snapshots” corresponding to the entire visual field to be tokened at any one time. (shrink)

In this paper the relationship between phenomenology of perception and synthetic phenomenology is discussed. Synthetic phenomenology is presented on the basis of the issues in A.I. and Robotics that required to address the question of what enables artificial agents to have phenomenal access to the environment. Phenomenology of perception is construed as a theory with autonomous structure and domain, which can be embedded in a philosophical as well as a scientific theory. Two attempts at specifying the phenomenal content of artificial (...) agents are discussed. Concepts and experimental evidence on the independence of perception and the coordination of motion and appearances are set out to submit that phenomenology of perception makes a contribution to synthetic phenomenology. (shrink)

The rise of Artificial Intelligence (AI) has produced prophets and prophecies announcing that the age of artificial consciousness is near. Not only does the mere idea that any machine could ever possess the full potential of human consciousness suggest that AI could replace the role of God in the future, it also puts into question the fundamental human right to freedom and dignity. This position paper takes the stand that, in the light of all we currently know about brain evolution (...) and the never-stopping formation of adaptive neural circuitry for learning, memory, decision making and, ultimately, fully conscious reasoning and creativity in the human species, the idea of an artificial consciousness appears misconceived. The paper highlights some of the major reasons why. While awareness to external stimuli for processes such as perception, recognition, and operational problem solving is under the direct control of functionally specific brain networks associated with sensory and cognitive functions across animal species, consciousness is a unique property of the human mind. (shrink)

While practical efforts in the field of artificial intelligence grow exponentially, the truly scientific and mathematically exact understanding of the underlying phenomena of intelligence and consciousness is still missing in the conventional science framework. The inevitably dominating empirical, trial-and-error approach has vanishing efficiency for those extremely complicated phenomena, ending up in fundamentally limited imitations of intelligent behaviour. We provide the first-principle analysis of unreduced many-body interaction process in the brain revealing its qualitatively new features, which give rise to rigorously defined (...) chaotic, noncomputable, intelligent and conscious behaviour. Based on the obtained universal concepts of unreduced dynamic complexity, intelligence and consciousness, we derive the universal laws of intelligence applicable to any kind of intelligent system interacting with the environment. We finally show why and how these fundamentally substantiated and therefore practically efficient laws of intelligent system dynamics are indispensable for correct AI design and training, which is urgently needed in this time of critical global change towards the truly sustainable development. (shrink)

We decided to use our editors’ introduction to briefly address a difficult, somewhat deeper, and in some ways more classical problem: that of what genuine open mindedness really is and how it can contribute to the Mind Sciences. The material in the collection speaks for itself. Here, and in contrast to the vast collection that is Open MIND, we want to be concise. We want to point to the broader context of a particular way of thinking about the mind. And (...) we want to propose an account of what open mindedness could mean in the context of the contemporary, interdisciplinary Mind Sciences. This variant of open mindedness is characterized by epistemic humility, intellectual honesty, and a new culture of charity. It also has a pragmatic dimension: open mindedness of this kind is research generating and fosters an environment of sincere and constructive interdisciplinary collaboration. And it is profoundly inspired by the classical ideals of philosophy as a pursuit of genuine insight and rational inquiry, the importance of a critical and in a certain sense non-judgmental attitude, and the deep relationship between wisdom and skepticism as an epistemic practice. Finally, and again very classically, open mindedness has an ethical dimension as well: it implies sensitivity to normative issues, including issues of an anthropological, sociocultural, and political kind. By bringing these different strands of ideas together and creating a bigger (and admittedly still sketchy) picture of what “open mindedness” might mean in the interdisciplinary Mind Sciences, we hope to start a conversation about how an open-minded attitude and a charitable culture of collaboration can be cultivated in the future. This is very much intended as an invitation to further think about and develop this topic. We hope our readers will join us in this endeavor. DOI: 10.15502/9783958571044 URL: http://dx.doi.org/10.15502/9783958571044 ISBN: 9783958571044. (shrink)

A main goal of the neuroscience of consciousness is: find the neural correlate to conscious experiences (NCC). When have we achieved this goal? The answer depends on our operationalization of “NCC.” Chalmers (2000) shaped the widely accepted operationalization according to which an NCC is a neural system with a state which is minimally sufficient (but not necessary) for an experience. A deeper look at this operationalization reveals why it might be unsatisfactory: (i) it is not an operationalization of a correlate (...) for occurring experiences, but of the capacity to experience; (ii) it is unhelpful for certain cases which are used to motivate a search for neural correlates of consciousness; (iii) it does not mirror the usage of “NCC” by scientists who seek for unique correlates; (iv) it hardly allows for a form of comparative testing of hypotheses, namely experimenta crucis. Because of these problems (i–iv), we ought to amend or improve on Chalmers's operationalization. Here, I present an alternative which avoids these problems. This “NCC2.0” also retains some benefits of Chalmers's operationalization, namely being compatible with contributions from extended, embedded, enacted, or embodied accounts (4E-accounts) and allowing for the possibility of non-biological or artificial experiencers. (shrink)