The object of the study is a special branch of scientific knowledge, formed at the intersection of interests of various humanitarian disciplines – the philosophy of artificial intelligence; the subject of the study is the problems it considers. The author identifies the main traditional questions that the philosophy of artificial intelligence tried to answer earlier. Such questions in the earlier stages of the development of science were concentrated around the possibility of artificial intelligence to become aware of itself, learn to (...) think, feel and create like a person. Scientists since the 1960s interested in the problems of equality of natural and artificial intelligence, the ability of a computer to show benevolence or hostility towards its creators. The research was carried out using a historical and philosophical analysis of issues in the philosophy of artificial intelligence, the method of interpretive synthesis, etc. Systemic-structural, dialectical, cultural-historical, value-based, interdisciplinary approaches to the problem were used. The scientific novelty of the research lies in identifying the problems of the philosophy of artificial intelligence that are relevant at this stage of development of this scientific field. It is noted that the dynamics of the philosophy of artificial intelligence are significantly influenced by the fact that “strong” artificial intelligence has not been created for several decades. This has caused a transformation in the research field of the philosophy of artificial intelligence, and at the moment this branch of scientific knowledge is considering new questions, including why it is not possible to create “strong” AI that completely replicates human thinking; can a computer think, albeit differently, than a human; what is the difference between human and machine thinking; who should be responsible for the decisions and actions of artificial intelligence, and etc. The issues facing the philosophy of artificial intelligence are constantly updated. (shrink)

We can readily identify goal-directed systems and distinguish them from non-goal-directed systems. A woodpecker hunting for grubs is the first, a pendulum returning to rest is the second. But what is it to be a goal-directed system? Perhaps the dominant answer to this question, inspired by systems theories such as cybernetics, is that goal-directed systems are distinguished by their tendency to seek, aim at, or maintain some more-or-less easily identifiable goal. Cybernetics and the like would hold that physical systems subject (...) only to physical laws can exhibit such behavior. If sound, this systems approach to teleology would unify a diverse range of goal-directed phenomena and neatly side-step many traditional bogey-men of teleology, such as anthropomorphism and future causation. Goal-directed phenomena would be a normal feature of the natural causal world that could be described in purely descriptive and quantitative terms, and receive ordinary causal explanations. Thus, the systems approach promises to provide a naturalistic-cum-descriptive account of teleology suitable for use in naturalistic accounts of other phenomena, including the intentionality of mental states and even self-consciousness. (shrink)

In this paper the author tries to disentangle some of the problems tied up in John Searle's famous Chinese-room-argument. In a first step to answer the question what it would be for a system to have not only syntax, but also semantics the author gives a brief account of the functioning of the language understanding systems (LUS) so far developed in the framework of AI research thereby making clear that systems like Winograd's SHRDLU are indeed doing little more than mere (...) number crunching. But things would be entirely different, the author argues, if the database of a LUS were built up by the system itself via some perceptual component-at least, if this perceptual component had the capacity to distinguish objects having a certain property F from objects which do not. For in this case the system could store an internal representation of the fact that the object has the property F in its database if and only if the object in fact has that property. And this would be a good basis for calling such a system a genuine LUS. But Searle has objected to a very similar account of J. Fodor that nothing could be further from true language understanding. The reason for this complaint seems to be that Searle holds the view that a true LUS must e.g., know that the word hamburgers refers to hamburgers and that he moreover claims that this knowledge must be explicit or that the system must be aware of the reference of hamburgers to hamburgers. The author argues that this is asking too much. For it seems plausible to say that a system is able to understand e.g., the word hamburger even if it has only implicit knowledge of the fact that hamburger refers to hamburgers in the sense that it has the capacity to tell hamburgers from non hamburgers and the capacity to bring the word hamburger together just with objects of the former kind. (shrink)

Cognitive science is an interdisciplinary research endeavor focusing on human cognitive phenomena such as memory, language use, and reasoning. It emerged in the second half of the 20th century and is charting new directions at the beginning of the 21st century. This chapter begins by identifying the disciplines that contribute to cognitive science and reviewing the history of the interdisciplinary engagements that characterize it. The second section examines the role that mechanistic explanation plays in cognitive science, while the third focuses (...) on the importance of mental representations in specifically cognitive explanations. The fourth section considers the interdisciplinary nature of cognitive science and explores how multiple disciplines can contribute to explanations that exceed what any single discipline might accomplish. The conclusion sketches some recent developments in cognitive science and their implications for philosophers. (shrink)

Many of the mathematicians and scientists who guided the development of digital computers in the late 1940s, such as Alan Turing and John von Neumann, saw these new devices not just as tools for calculation but as devices that might employ the same principles as are exhibited in rational human thought. Thus, a subfield of what came to be called computer science assumed the label artificial intelligence. The idea of building artificial systems which could exhibit intelligent behavior comparable to that (...) of humans was a heady prospect, and the claims made on behalf of AI during the 1950s and 1960s were impossibly ambitious. Despite some theoretical and applied successes within the field, serious problems soon became evident. Instead of fulfilling the goal of quickly producing artificial intelligent agents which could compete with or outperform human beings, by the 1970s and 1980s AI had settled into a pattern of slower but real progress in modeling or simulating aspects of human intelligence. (shrink)

In philosophy the term intentionality refers to the feature possessed by mental states of beingabout things others than themselves. A serious question has been how to explain the intentionality of mental states. This paper starts with linguistic representations, and explores how an organism might use linguistic symbols to represent other things. Two research projects of Sue Savage-Rumbaugh, one explicity teaching twopan troglodytes to use lexigrams intentionally, and the other exploring the ability of several members ofpan paniscus to learn lexigram use (...) and comprehension of English speech spontaneously when raised in an appropriate environment, are examined to explore the acquisition process. Although it is controversial whether intentionality of mental states or linguistic symbols is primary, it is argued that the intentionality of linguistic symbols is primary and that studying how organisms learn to use linguistic symbols provides an avenue to understanding how intentionality is acquired by cognitive systems. (shrink)

Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information-processing mechanisms, cognitive science needs an account of (...) how the representations invoked in cognitive mechanisms carry information about contents, and I suggest that control theory offers the needed perspective on the relation of representations to contents. Second, I argue that cognitive science requires, but is still in search of, a catalog of cognitive operations that researchers can draw upon in explaining cognitive mechanisms. Last, I provide a new perspective on the relation of cognitive science to brain sciences, one which embraces both reductive research on neural components that figure in cognitive mechanisms and a concern with recomposing higher-level mechanisms from their components and situating them in their environments. (shrink)

Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information‐processing mechanisms, cognitive science needs an account of (...) how the representations invoked in cognitive mechanisms carry information about contents, and I suggest that control theory offers the needed perspective on the relation of representations to contents. Second, I argue that cognitive science requires, but is still in search of, a catalog of cognitive operations that researchers can draw upon in explaining cognitive mechanisms. Last, I provide a new perspective on the relation of cognitive science to brain sciences, one which embraces both reductive research on neural components that figure in cognitive mechanisms and a concern with recomposing higher‐level mechanisms from their components and situating them in their environments. (shrink)

This paper reviews four significant advances on the feedforward architecture that has dominated discussions of connectionism. The first involves introducing modularity into networks by employing procedures whereby different networks learn to perform different components of a task, and a Gating Network determines which network is best equiped to respond to a given input. The second consists in the use of recurrent inputs whereby information from a previous cycle of processing is made available on later cycles. The third development involves developing (...) compressed representations of strings in which there is no longer an explicit encoding of the components but where information about the structure of the original string can be recovered and so is present functionally. The final advance entails using connectionist learning procedures not just to change weights in networks but to change the patterns used as inputs to the network. These advances significantly increase the usefulness of connectionist networks for modeling human cognitive performance by, among other things, providing tools for explaining the productivity and systematicity of some mental activities, and developing representations that are sensitive to the content they are to represent. (shrink)

In his paper "Machines and the Mental" Fred Dretske claims that there is a difference on principle between men on the one and animals and machines on the other side which arises from the fact that men are able to respond to the meaning of symbols whereas animals and machines can only respond to the symbols that have the meaning. In this paper it is argued that this claim does not bear closer scrutiny. Mainly for two reasons: 1. The mere (...) having of a certain meaning does not rank among the possible causal properties of a symbol, not even in the case of men; for it is not the having of the meaning that may have certain effects on the behavior of a person, but the fact that the person associates this meaning with the symbol. 2. Even animals are capable to associate a meaning with a symbol, i. e. even animals can learn to come to believe that p if they become aware of a symbol which has the meaning that p. (shrink)

I start with some of the early challenges to the widely-employed philosophical method of cases—the very challenges that originally prompted the new movement of experimental philosophy—and with some fundamental questions about the method that are yet to have been given satisfying answers. I then propose that what has allowed both ‘armchair’ and ‘experimental’ participants in the ongoing debates concerning the method to ignore or repress those early challenges—and in particular Robert Cummins’s ‘calibration objection’—and to discount fundamental disagreements about those questions, (...) is in large part ‘the claim of continuity’, which is the claim, or assumption, that there is no philosophically significant difference between whatever it is that we do when we give our answer to the theorist’s question of whether some philosophically interesting word ‘applies’ (positively or negatively) to some theoretically significant case, and our use of that same word in the course of ordinary, non-philosophical discourse. I then summarize the ordinary language philosophy (OLP) argument against the claim of continuity, and explain why answers to the theorist’s questions are vulnerable to the calibration objection in a way that our non-philosophical, everyday employment of the same words is not. This then leads me to question another widespread assumption, which is the assumption that the theorist’s questions—as raised in the theorist’s context—have clear enough sense and correct answers. I end by responding to a series of objections to my argument and to OLP more generally. (shrink)

This paper explores the history of ELIZA, a computer programme approximating a Rogerian therapist, developed by Jospeh Weizenbaum at MIT in the 1970s, as an early AI experiment. ELIZA’s reception provoked Weizenbaum to re-appraise the relationship between ‘computer power and human reason’ and to attack the ‘powerful delusional thinking’ about computers and their intelligence that he understood to be widespread in the general public and also amongst experts. The root issue for Weizenbaum was whether human thought could be ‘entirely computable’. (...) This also provoked him to re-consider the nature of machine intelligence and to question the instantiation of its logics in the social world, which would come to operate, he said, as a ‘slow acting poison’. Exploring Weizenbaum’s 20th Century apostasy, in the light of ELIZA, illustrates ways in which contemporary anxieties and debates over machine smartness connect to earlier formations. In particular, this article argues that it is in its designation as a computational therapist that ELIZA is most significant today. ELIZA points towards a form of human–machine relationship now pervasive, a precursor of the ‘machinic therapeutic’ condition we find ourselves in, and thus speaks very directly to questions concerning modulation, autonomy, and the new behaviorism that are currently arising. (shrink)

Those who believe that explicitly Christian thinking is possible in the scientific disciplines tend to assume that it must be antithetical to the world’s thinking. Based on some of the author’s experience, this article examines a different approach, in which Christian thinking is used to account for and enrich the world’s thinking by transplanting it from its current ground-motive into the arguably more fertile soil of the creation-fall-redemption ground-motive. The article shows how Dooyeweerd’s version of Christian thinking has been employed (...) in two areas of thinking in information systems : thinking about the nature of computers and information, with the artificial intelligence question of whether computer is like human being soft systems methodology, by which perspectives on ‘human activity systems’ are orchestrated into new learning and plans. In both areas, the original ideas are accounted for, given philosophical underpinning, reinterpreted and enriched. These two show that Dooyeweerd’s philosophy can be equally useful in thinking grounded in both positivist and interpretivist cultures. (shrink)

Huxley’s Explanatory Gap: There can be no explanation of how states of consciousness arise from interaction among a collection of physical things.

Scientific understanding is a fundamental goal of science. However, there is currently no good way to measure the scientific understanding of agents, whether these be humans or Artificial Intelligence systems. Without a clear benchmark, it is challenging to evaluate and compare different levels of scientific understanding. In this paper, we propose a framework to create a benchmark for scientific understanding, utilizing tools from philosophy of science. We adopt a behavioral conception of understanding, according to which genuine understanding should be recognized (...) as an ability to perform certain tasks. We extend this notion of scientific understanding by considering a set of questions that gauge different levels of scientific understanding, covering information retrieval, the capability to arrange information to produce an explanation, and the ability to infer how things would be different under different circumstances. We suggest building a Scientific Understanding Benchmark (SUB), formed by a set of these tests, allowing for the evaluation and comparison of scientific understanding. Benchmarking plays a crucial role in establishing trust, ensuring quality control, and providing a basis for performance evaluation. By aligning machine and human scientific understanding we can improve their utility, ultimately advancing scientific understanding and helping to discover new insights within machines. (shrink)

Modern biology has not yet come to terms with the presence of many organic codes in Nature, despite the fact that we can prove their existence. As a result, it has not yet accepted the idea that the great events of macroevolution were associated with the origin of new organic codes, despite the fact that this is the most parsimonious and logical explanation of those events. This is probably due to the fact that the existence of organic codes in all (...) fundamental processes of life, and in all major transitions in the history of life, has enormous theoretical implications. It requires nothing less than a new theoretical framework, and that kind of change is inevitably slow. There are too many facts to reconsider, too many bits of history to weave together in a new mosaic. But this is what science is about, and the purpose of the present paper is to show that it can be done. More precisely, it is shown that the whole natural history of the brain can be revisited in the light of the organic codes. What is described here is only a bird’s-eye view of brain macroevolution, but it is hoped that the extraordinary potential of the organic codes can nevertheless come through. The paper contains also another message. The organic codes prove that life is based on semiosis, and are in fact the components of organic semiosis, the first and the most diffused form of semiosis on Earth, but not the only one. It will be shown that the evolution of the brain was accompanied by the development of two new types of sign processes. More precisely, it gave origin first to interpretive semiosis, mostly in vertebrates, and then to cultural semiosis, in our species. (shrink)

Thirty years ago, grounded cognition had roots in philosophy, perception, cognitive linguistics, psycholinguistics, cognitive psychology, and cognitive neuropsychology. During the next 20 years, grounded cognition continued developing in these areas, and it also took new forms in robotics, cognitive ecology, cognitive neuroscience, and developmental psychology. In the past 10 years, research on grounded cognition has grown rapidly, especially in cognitive neuroscience, social neuroscience, cognitive psychology, social psychology, and developmental psychology. Currently, grounded cognition appears to be achieving increased acceptance throughout cognitive (...) science, shifting from relatively minor status to increasing importance. Nevertheless, researchers wonder whether grounded mechanisms lie at the heart of the cognitive system or are peripheral to classic symbolic mechanisms. Although grounded cognition is currently dominated by demonstration experiments in the absence of well-developed theories, the area is likely to become increasingly theory driven over the next 30 years. Another likely development is the increased incorporation of grounding mechanisms into cognitive architectures and into accounts of classic cognitive phenomena. As this incorporation occurs, much functionality of these architectures and phenomena is likely to remain, along with many original mechanisms. Future theories of grounded cognition are likely to be heavily influenced by both cognitive neuroscience and social neuroscience, and also by developmental science and robotics. Aspects from the three major perspectives in cognitive science—classic symbolic architectures, statistical/dynamical systems, and grounded cognition—will probably be integrated increasingly in future theories, each capturing indispensable aspects of intelligence. (shrink)

Very plausibly, nothing can be a genuine computing system unless it meets an input-sensitivity requirement. Otherwise all sorts of objects, such as rocks or pails of water, can count as performing computations, even such as might suffice for mentality—thus threatening computationalism about the mind with panpsychism. Maudlin in J Philos 86:407–432, ( 1989 ) and Bishop ( 2002a , b ) have argued, however, that such a requirement creates difficulties for computationalism about conscious experience, putting it in conflict with the (...) very intuitive thesis that conscious experience supervenes on physical activity. Klein in Synthese 165:141–153, ( 2008 ) proposes a way for computationalists about experience to avoid panpsychism while still respecting the supervenience of experience on activity. I argue that his attempt to save computational theories of experience from Maudlin’s and Bishop’s critique fails. (shrink)

The paper is a commentary on the target article by Christine A. Skarda & Walter J. Freeman, “How brains make chaos in order to make sense of the world”, in the same issue of the journal, pp.161–195. -/- I confine my comments largely to some philosophical claims that Skarda & Freeman make and to the relationship of their model to connectionism. Some of the comments hinge on what symbols are and how they might sit in neural systems.

Recent research in computational neuroscience has demonstrated that we now possess the ability to simulate neural systems in significant detail and on a large scale. Simulations on the scale of a human brain have recently been reported. The ability to simulate entire brains (or significant portions thereof) would be a revolutionary scientific advance, with substantial benefits for brain science. However, the prospect of whole-brain simulation comes with a set of new and unique ethical questions. In the present paper, we briefly (...) outline certain of those problems and emphasize the need to begin considering the ethical aspects of computational neuroscience. (shrink)

The rapid development of natural language processing in the last three decades has drastically changed the way professional translators do their work. Nowadays most of them use computer-assisted translation or translation memory tools whose evolution has been overshadowed by the much more sensational development of machine translation systems, with which TM tools are sometimes confused. These two language technologies now interact in mutually enhancing ways, and their increasing role in human translation has become a subject of behavioral studies. Philosophers and (...) linguists, however, have been slow in coming to grips with these important developments. The present paper seeks to fill in this lacuna. I focus on the semantic aspects of the highly distributed human–computer interaction in the CAT process which presents an interesting case of an extended cognitive system involving a human translator, a TM tool, an MT engine, and sometimes other human translators or editors. Considered as a whole, such a system is engaged in representing the linguistic meaning of the source document in the target language. But the roles played by its various components, natural as well as artificial, are far from trivial, and the division of linguistic labor between them throws new light on the familiar notions that were initially inspired by rather different phenomena in the philosophy of language, mind, and cognitive science. (shrink)

There is a deluge of AI-assisted decision-making systems, where our data serve as proxy to our actions, suggested by AI. The closer we investigate our data (raw input, or their learned representations, or the suggested actions), we begin to discover “bugs”. Outside of their test, controlled environments, AI systems may encounter situations investigated primarily by those in other disciplines, but experts in those fields are typically excluded from the design process and are only invited to attest to the ethical features (...) of the resulting system or to comment on demonstrations of intelligence and aspects of craftmanship after the fact. This communicative impasse must be overcome. Our idea is that philosophical and engineering considerations interact and can be fruitfully combined in the AI design process from the very beginning. We embody this idea in the role of a philosopher engineer. We discuss the role of philosopher engineers in the three main design stages of an AI system: deployment management (what is the system’s intended use, in what environment?); objective setting (what should the system be trained to do, and how?); and training (what model should be used, and why?). We then exemplify the need for philosopher engineers with an illustrative example, investigating how the future decisions of an AI-based hiring system can be fairer than those contained in the biased input data on which it is trained; and we briefly sketch the kind of interdisciplinary education that we envision will help to bring about better AI. (shrink)

Empirical research on human–robot interaction has demonstrated how humans tend to react to social robots with empathic responses and moral behavior. How should we ethically evaluate such responses to robots? Are people wrong to treat non-sentient artefacts as moral patients since this rests on anthropomorphism and ‘over-identification’ —or correct since spontaneous moral intuition and behavior toward nonhumans is indicative for moral patienthood, such that social robots become our ‘Others’?. In this research paper, I weave extant HRI studies that demonstrate empathic (...) responses toward robots with the recent debate on moral status for robots, on which the ethical evaluation of moral behavior toward them is dependent. Patienthood for robots has standardly been thought to obtain on some intrinsic ground, such as being sentient, conscious, or having interest. But since these attempts neglect moral experience and are curbed by epistemic difficulties, I take inspiration from Coeckelbergh and Gunkel’s ‘relational approach’ to explore an alternative way of accounting for robot patienthood based on extrinsic premises. Based on the ethics of Danish theologian K. E. Løgstrup I argue that empathic responses can be interpreted as sovereign expressions of life and that these expressions benefit human subjects—even if they emerge from social interaction afforded by robots we have anthropomorphized. I ultimately develop an argument in defense of treating robots as moral patients. (shrink)

Some may find an oxymoron in my title. But, my use of “instrumental” is to focus attention on the real instruments of science-pumps, dynamos and cyclotrons-and not the view that scientific theories are best understood as instruments. In what follows I characterize and argue for a kind of realism strongly wedded to what we do with scientific instruments, and divorced from what our theories may say about the entities manipulated by these instruments. My discussion owes much to Ian Hacking’s “Experimental (...) Argument for Realism” (Hacking 1983, ch. 16).The following fantasy might help give some idea of the view I am interested in. Suppose that the diskettes, which many people use in conjunction with a small computer, do not store information magnetically; in fact they store it “radioactively,” exactly how, is not relevant. IBM decided that many people in their target personal computer market would be put off by “nuclear diskettes.”. (shrink)

Some strange cases have gripped philosophers of mind. They have been deployed against materialism about human persons, functionalism about mentality, the possibility of artificial intelligence, and more. In this paper, I cry “foul”. It’s not hard to think that there’s something wrong with the cases. But what? My proposal: their proponents ignore questions about composition. And ignoring composition is a mistake. Indeed, materialists about human persons, functionalists about mentality, and believers in the possibility of artificial intelligence can plausibly deploy moderate (...) theories of composition in defense of their views. And as it turns out, these strange cases are an interesting source of evidence for moderate theories of composition. (shrink)

The representational nature of human cognition and thought in general has been a source of controversies. This is particularly so in the context of studies of unconscious cognition, in which representations tend to be ontologically and structurally segregated with regard to their conscious status. However, it appears evolutionarily and developmentally unwarranted to posit such segregations, as,otherwise, artifact structures and ontologies must be concocted to explain them from the viewpoint of the human cognitive architecture. Here, from a by-and-large Classical cognitivist viewpoint, (...) I show why this segregation is wrong, and elaborate on the need to postulate an ontological and structural continuity between unconscious and conscious representations. Specifically, I hypothesize that this continuity is to be found in the symbolic-based interplay between the syntax and the semantics of thought, and I propose a model of human information processing characterized by the integration of syntactic and semantic representations. (shrink)

Two critical questions spun the web of the Turing test debate. First, can an appropriately programmed machine pass the Turing test? Second, is passing the test by such a machine, ipso facto, considered proof that it is intelligent and hence “minded”? While the first question is technological, the second is purely philosophical. Focusing on the second question, this article interrogates the implication of John Searle’s Chinese room denial of machine intelligence. The thrust of Searle’s argument is that a machine lacks (...) intentionality, so it can only simulate intelligence, not duplicate it. In his thinking, whatever a machine inputs to generate an intelligent output has no bearing on humanlike intelligence. Incidentally, Searle did not classify such a machine’s output as simulated and non-intelligent, nor did he explain how this output is actualised with mere simulation. The connection between “unintelligent machine’s input” and “intelligent machine’s output” is at this point shrouded in mystery. Consequently, the more Searle attempts a denial of machine intelligence in the Chinese room, the more he mystifies it. Using the method of critical analysis, this article advances three fundamental arguments to prove a machines’ obscurity in the Chinese room thought experiment. On the ground of Searle’s conviction, the first argument queries the absurdity in bypassing intentionality to produce intelligence; the second points out the obfuscation in generating intelligence with mere computation, and the third draws attention to the dilemma of classifying a machine’s output either as real-life intelligent behaviour or simulated intelligent behaviour. (shrink)

This article argues that if panpsychism is true, then there are grounds for thinking that digitally-based artificial intelligence may be incapable of having coherent macrophenomenal conscious experiences. Section 1 briefly surveys research indicating that neural function and phenomenal consciousness may be both analog in nature. We show that physical and phenomenal magnitudes—such as rates of neural firing and the phenomenally experienced loudness of sounds—appear to covary monotonically with the physical stimuli they represent, forming the basis for an analog relationship between (...) the three. Section 2 then argues that if this is true and micropsychism—the panpsychist view that phenomenal consciousness or its precursors exist at a microphysical level of reality—is also true, then human brains must somehow manipulate fundamental microphysical-phenomenal magnitudes in an analog manner that renders them phenomenally coherent at a macro level. However, Sect. 3 argues that because digital computation abstracts away from microphysical-phenomenal magnitudes—representing cognitive functions non-monotonically in terms of digits—digital computation may be inherently incapable of realizing coherent macroconscious experience. Thus, if panpsychism is true, digital AI may be incapable of achieving phenomenal coherence. Finally, Sect. 4 briefly examines our argument’s implications for Tononi’s Integrated Information Theory theory of consciousness, which we contend may need to be supplanted by a theory of macroconsciousness as analog microphysical-phenomenal information integration. (shrink)

This article argues that existing approaches to programming ethical AI fail to resolve a serious moral-semantic trilemma, generating interpretations of ethical requirements that are either too semantically strict, too semantically flexible, or overly unpredictable. This paper then illustrates the trilemma utilizing a recently proposed ‘general ethical dilemma analyzer,’ GenEth. Finally, it uses empirical evidence to argue that human beings resolve the semantic trilemma using general cognitive and motivational processes involving ‘mental time-travel,’ whereby we simulate different possible pasts and futures. I (...) demonstrate how mental time-travel psychology leads us to resolve the semantic trilemma through a six-step process of interpersonal negotiation and renegotiation, and then conclude by showing how comparative advantages in processing power would plausibly cause AI to use similar processes to solve the semantic trilemma more reliably than we do, leading AI to make better moral-semantic choices than humans do by our very own lights. (shrink)

Logical theories for representing knowledge are often plagued by the so-called Logical Omniscience Problem. The problem stems from the clash between the desire to model rational agents, which should be capable of simple logical inferences, and the fact that any logical inference, however complex, almost inevitably consists of inference steps that are simple enough. This contradiction points to the fruitlessness of trying to solve the Logical Omniscience Problem qualitatively if the rationality of agents is to be maintained. We provide a (...) quantitative solution to the problem compatible with the two important facets of the reasoning agent: rationality and resource boundedness. More precisely, we provide a test for the logical omniscience problem in a given formal theory of knowledge. The quantitative measures we use are inspired by the complexity theory. We illustrate our framework with a number of examples ranging from the traditional implicit representation of knowledge in modal logic to the language of justification logic, which is capable of spelling out the internal inference process. We use these examples to divide representations of knowledge into logically omniscient and not logically omniscient, thus trying to determine how much information about the reasoning process needs to be present in a theory to avoid logical omniscience. (shrink)