Most philosophers appear to have ignored the distinction between the broad concept of Virtual Machine Functionalism (VMF) described in Sloman&Chrisley (2003) and the better known version of functionalism referred to there as Atomic State Functionalism (ASF), which is often given as an explanation of what Functionalism is, e.g. in Block (1995). -/- One of the main differences is that ASF encourages talk of supervenience of states and properties, whereas VMF requires supervenience of machines that are (...) arbitrarily complex networks of causally interacting (virtual, but real) processes, possibly operating on different time-scales, examples of which include many different procesess usually running concurrently on a modern computer performing various tasks concerned with handling interfaces to physical devices, managing the file system, dealing with security, providing tools, entertainments, and games, and possibly processing research data. Another example of VMF would be the kind of functionalism involved in a large collection of possibly changing socio-economic structures and processes interacting in a complex community, and yet another is illustrated by the kind of virtual machinery involved in the many levels of visual processing of information about spatial structures, processes, and relationships (including percepts of moving shadows, reflections, highlights, optical-flow patterns and changing affordances) as you walk through a crowded car-park on a sunny day: generating a whole zoo of interacting qualia. (Forget solitary red patches, or experiences thereof.) -/- Perhaps VMF should be re-labelled "Virtual MachinERY Functionalism" because the word 'machinery' more readily suggests something complex with interacting parts. VMF is concerned with virtual machines that are made up of interacting concurrently active (but not necessarily synchronised) chunks of virtual machinery which not only interact with one another and with their physical substrates (which may be partly shared, and also frequently modified by garbage collection, metabolism, or whatever) but can also concurrently interact with and refer to various things in the immediate and remote environment (via sensory/motor channels, and possible future technologies also). I.e. virtual machinery can include mechanisms that create and manipulate semantic content, not only syntactic structures or bit patterns as digital virtual machines do. -/- Please note: Click on the title above or the link below to read the paper. I prefer to keep all my papers freely accessible on my web site so that I can correct mistakes and add improvements. -/- http://www.cs.bham.ac.uk/research/projects/cogaff/misc/vm-functionalism.html -/- This is now part of the Meta-Morphogenesis project: http://www.cs.bham.ac.uk/research/projects/cogaff/misc/meta-morphogenesis.html. (shrink)

The location of the living human being in the animal world is a long-standing discussion, with recurrences from time to time. Perhaps one of the most significant for contemporary Western thought was the course that Kojeve gave between 1935 and 1939 on the phenomenology of Hegel’s spirit, not so much not only because of the teaching itself but because of the resonances on the rest of French thought and in a certain sense European in general. Bataille, Lacan, Levy-Srauss, Foucault, and (...) Agamben, among others, refer to the turn this course represented for human interpretation.Based on this interpretation, several unavoidable concepts can be identified, without which this discussion would be meaningless: the concept of homo has been indistinctly associated with sapiens, faber, zoon politikón, ludens, conscious, or drive. The first has had a clear expression in the anthropological tradition or non-pragmatic sociology, leaving the last two reserved for psychology and psychoanalysis.In an approximation to the Freudian text of 1915 Drive and destinations of drive, which J. Lacan made in his Seminar 11, “The Four Fundamental Concepts of Psychoanalysis”; specifically, in the class of May 6, 1964 “Disassembly of the Drive,” he reminds us how essential one of the concepts of psychoanalysis is for the analytic experience: the drive (Trieb). This term, with a long history of clarifications and ambiguities in Freud’s work, is found throughout the entire corpus of his metapsychology, basting together the rest of the concepts and giving the body to a theory of the human psyche. Thus, the drive, rooted in psychoanalytic interpretations, which “every analyst (is) knows by existence” (Lacan, Les quatre concepts fondamentaux de la psychanalyse. Le séminaire, livre XI. Éditions du Seuil, 1964, p. 60); however, it has been “used” at will, according to what each theoretician needed for what he had to address.At the very core of this discussion is the relationship with Pathos. The recognition or not of the living human and its type variants, pathology as a result of the non-pathological or pathos as indissoluble from the cultural essence have been some of the central questions. However, autism has appeared as an uncomfortable stone, whether one enters via “normality” toward the “pathological” or vice versa. It has even had a problematic history in the psychoanalytic tradition as well.In this way, since the notion of autism began to be legitimised in the psychoanalytic tradition to name certain subjects—particularly infants—a diverse and challenging use was made of the Freudian notion of drive in its interpretation.Then, to ask: how could “drive theory” explain what psychoanalysis considers “autism”? What are the difficulties in the dialogue between these two signifiers? Are these difficulties due to the misunderstanding of the word autism, as some hypothesise? Or to the different meanings of the drive in Freud, as others think? Or perhaps, are there other epistemic reasons that block these two concepts in the psychoanalytic theoretical gear?We hypothesise that the appearance of autism came to shake up all this tradition for which the human subject was a fracture in the animal evolutionary continuity, both that of consciousness/unconscious—which exceeds the possibility of discussion in this space—and the “drive thesis,” which is the one that concerns us. In this sense, the opposition continuism-discontinuism would now be, for the phylogenetic, put into question, to be eventually replaced by an asymptotic continuism, more assimilable to the Spinozian bet.This chapter explores the references to Sigmund Freud’s theory of drives in the interpretative approaches to child autism. Notably, it analyses the psychoanalytic thesis that proposes a constitutive failure in primordial times to understand the genesis of autism. The work explicitly presents a revelation of drive theory’s importance for the approach to autism. It also drives the exploration of the modes of appropriation and the use of said theorisation to explain the autistic phenomenon. Finally, the chapter discusses the analysis of the difficulties presented to the psychoanalytic authors when trying to accept autistic functioning and their theoretical choices to interpret it according to drive’s theory. (shrink)

ABSTRACTIn Critical Discourse Studies and in other linguistics oriented scholarly journals we now see more research which draws upon multimodality as part of carrying out analyses of how text...

Taking the example of two 1960s popular spy films this paper explores how social semiotics can make a contribution to the analysis of film music. Followingother scholars who have sought to create inventories of sound meanings to help us break down the way that music communicates, this paper explores how wecan draw on the principles of Hallidayan functional grammar to present an inventory of meaning potentials in sound. This provides one useful way to describe thesemiotic resources available to composers to (...) allow them to communicate quite specific ideas, attitudes and identities through combinations of different sounds and sound qualities, by presenting them as systems of meaning rather than as lists of connotations. Here we apply this to the different uses of music and sound in Dr No and The Ipcress Files which allows us to show how we can reveal different ideologies of spying. (shrink)

Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to systems which exploit the effects of (...) outputs on subsequent inputs to achieve good performance over episodes of interaction with their environments. Supervised learning systems, in contrast, merely learn to produce better outputs in response to individual inputs. (shrink)

Philosophers of mind typically group experiential states together and distinguish these from intentional states on the basis of their purportedly obvious phenomenal character. Sytsma and Machery (Phil Stud 151(2): 299–327, 2010) challenge this dichotomy by presenting evidence that non-philosophers do not classify subjective experiences relative to a state’s phenomenological character, but rather by its valence. However we argue that S&M’s results do not speak to folk beliefs about the nature of experiential states, but rather to folk beliefs about the entity (...) to which those experiential states are attributed. In two experiments, we demonstrate that ordinary attributions of subjective experiences (of smell and felt emotions) to a simple robot are not sensitive to valence, but instead respond to functional assumptions about the entity to which the states are (or are not) attributed. (shrink)

The functional capabilities that consciousness seems to provide to biological systems can supply valuable principles in the design of more autonomous and robust technical systems. These functional concepts keep a notable similarity to those underlying the notion of operating system in software engineering, which allows us to specialize the computer metaphor for the mind into that of the operating system metaphor for consciousness. In this article, departing from these ideas and a model-based theoretical framework for cognition, we present an architectural (...) proposal for machine consciousness, called the Operative Mind. According to it, machine consciousness could be implemented as a set of services, in an operative system fashion, based on modeling of the own control architecture, that supervise the adequacy of the system architectural structure to the current objectives, triggering and managing adaptativity mechanisms. (shrink)

ObjectiveType 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities.MethodsA total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects’ clinical data, neuropsychological tests and resting-state functional magnetic resonance (...) imaging data were acquired. Whole-brain network FC were mapped, the topological characteristics were analyzed using a graph-theoretic approach, the FC and topological characteristics of the network were compared between T2DM and HC using a general linear model, and correlations between networks and clinical and cognitive characteristics were identified. The support vector machine (SVM) model was used to identify differences between T2DM and HC.ResultsIn patients with T2DM, FC was higher in two core regions [precuneus/posterior cingulated cortex (PCC)_1 and later prefrontal cortex_1] in the default mode network and lower in bilateral superior parietal lobes (within dorsal attention network), and decreased between the right medial frontal cortex and left auditory cortex. The FC of the right frontal medial-left auditory cortex was positively correlated with the Montreal Cognitive Assessment scales and negatively correlated with the blood glucose levels. Long-range connectivity between bilateral auditory cortex was missing in the T2DM. The nodal degree centrality and efficiency of PCC were higher in T2DM than in HC (P < 0.005). The nodal degree centrality in the PCC in the SVM model was 97.56% accurate in distinguishing T2DM patients from HC, demonstrating the reliability of the prediction model.ConclusionFunctional abnormalities in the auditory cortex in T2DM may be related to cognitive impairment, such as memory and attention, and nodal degree centrality in the PCC might serve as a potential neuroimaging biomarker to predict and identify T2DM. (shrink)

Cardiovascular diseases are currently the leading cause of death worldwide. There are challenges, such as untimely healthcare, lack of access to technologies and timely diagnoses. Therefore, this project focuses on the use of innovative tools, giving way to the need to use artificial intelligence in the field of Machine Learning to improve the prediction of cardiovascular diseases. The research focused on determining the most effective kernel function in Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms, making (...) a fair comparison and evaluating the accuracy and prediction time of each proposed kernel function. Based on the results, these new optimal kernel functions are integrated into the scikit-learn library, achieving validation in the appropriate configuration for predicting the risk of CVD. This innovative approach reduces detection time, minimising the chances of future complications from preventable diseases, and provides timely diagnosis and risk factors with early warnings that can be extremely useful for healthcare personnel. (shrink)

Accelerated Turing machines are Turing machines that perform tasks commonly regarded as impossible, such as computing the halting function. The existence of these notional machines has obvious implications concerning the theoretical limits of computability.

Heidegger has provided a profound account of human existence in terms of the to-be-da. Even though Heidegger disregarded its brain machine basis (and even though brain scientists disregard Heidegger), the issue of the Dasein's machine basis is raised by the empirically extremely well confirmed “supervenience” of the Dasein on the brain. Since the Turing machine will not do as basis for the Dasein, as Dreyfus has shown, contemporary functionalism cannot resolve the issue. Instead an “existential (...) class='Hi'>functionalism,” which looks to some other kind of machine than the computer, is called for. A machine that continuously tunes filters on input and that detects any match between abstract properties of the input flux and filter specifications was considered. A match points to those tuning rules whose abstract conditions have been satisfied by input, and the rules thus selected are enabled to generate their own fulfillment through mechanisms that can presently be only speculated about. Such a machine cannot sustain direct perception but is “methodologically solipsistic.” At the most general level of description, the wet machine that provides a supervenience base for the Dasein is to be considered a “windowless monad.”. (shrink)

Seth mounts a rich and persuasive case that human consciousness can’t be understood without life’s distinctive machinery—autopoiesis, allostatic control, predictive processing under the free-energy principle, and substrate-entangled dynamics. I agree this undercuts the expectation that humanlike consciousness will “come for free” with more intelligent machines. However, we should also be cautious about drawing extrapolations from features of biological consciousness – even essential features – to the possibility of exotic forms of consciousness in artificial systems.

Thread 5 addresses domestic design and technology, from the dwelling machines of modernist functionalism to the smart homes of the present, as exemplified by Le Corbusier’s Villa Savoye (1931) and Apple’s Home application (2016). In this technological and architectonic domain, the performative staples of efficiency, efficacy, and effectiveness are traced in projects of functional transparency, from the Corbusian ‘machine for living in’ to the ‘ubiquitous’ but more invisible technologies of late. Conversely, the theatrical resides in the dramatic, the (...) aesthetic, the sensuous: in synoptic networks that help control and understand the technological meshwork, but also enable a distinctly antitechnological variant of the good old antitheatrical prejudice—beginning from the modernist distrust of ‘ornament’ in Le Corbusier’s time. The chapter concludes on ‘ecological’ notions of extended and enactive cognition that not only bear some affinity with theatricality and performativity, but also neatly define ‘textures of thought’ in which dwellings and inhabitants are equally interwoven. (shrink)

Objective: Patients with hypochondriasis hold unexplainable beliefs and a fear of having a lethal disease, with poor compliances and treatment response to psychotropic drugs. Although several studies have demonstrated that patients with hypochondriasis demonstrate abnormalities in brain structure and function, gray matter volume and functional connectivity in hypochondriasis still remain unclear.Methods: The present study collected T1-weighted and resting-state functional magnetic resonance images from 21 hypochondriasis patients and 22 well-matched healthy controls. We first analyzed the difference in the GMV between the (...) two groups. We then used the regions showing a difference in GMV between two groups as seeds to perform functional connectivity analysis. Finally, a support vector machine was applied to the imaging data to distinguish hypochondriasis patients from HCs.Results: Compared with the HCs, the hypochondriasis group showed decreased GMV in the left precuneus, and increased GMV in the left medial frontal gyrus. FC analyses revealed decreased FC between the left medial frontal gyrus and cuneus, and between the left precuneus and cuneus. A combination of both GMV and FC in the left precuneus, medial frontal gyrus, and cuneus was able to discriminate the hypochondriasis patients from HCs with a sensitivity of 0.98, specificity of 0.93, and accuracy of 0.95.Conclusion: Our study suggests that smaller left precuneus volumes and decreased FC between the left precuneus and cuneus seem to play an important role of hypochondriasis. Future studies are needed to confirm whether this finding is generalizable to patients with hypochondriasis. (shrink)

The viewpoint that consciousness, including feeling, could be fully expressed by a computational device is known as strong artificial intelligence or strong AI. Here I offer a defense of strong AI based on machine-state functionalism at the quantum level, or quantum-state functionalism. I consider arguments against strong AI, then summarize some counterarguments I find compelling, including Torkel Franzén’s work which challenges Roger Penrose’s claim, based on Gödel incompleteness, that mathematicians have nonalgorithmic levels of “certainty.” Some consequences of (...) strong AI are then considered. A resolution is offered of some problems including John Searle’s Chinese Room problem and the problem of consciousness propagation under isomorphism. (shrink)

Although machine learning has been successful in recent years and is increasingly being deployed in the sciences, enterprises or administrations, it has rarely been discussed in philosophy beyond the philosophy of mathematics and machine learning. The present contribution addresses the resulting lack of conceptual tools for an epistemological discussion of machine learning by conceiving of deep learning networks as ‘judging machines’ and using the Kantian analysis of judgments for specifying the type of judgment they are capable of. (...) At the center of the argument is the fact that the functionality of deep learning networks is established by training and cannot be explained and justified by reference to a predefined rule-based procedure. Instead, the computational process of a deep learning network is barely explainable and needs further justification, as is shown in reference to the current research literature. Thus, it requires a new form of justification, that is to be specified with the help of Kant’s epistemology. (shrink)

Machine learning (ML) shows strong performance in making accurate inferences from massive, high-dimensional data. Many scientists turn to this new tool when traditional inferential procedures cannot deal with overly messy data and complex target phenomena. One example is galactic archaeology, a branch of astronomy that aims to unravel the epic history of the Milky Way using the present snapshot of stars with only a handful of physical parameters. Historical inference in galactic archaeology is difficult due to the uncertainty of (...) what counts as evidence of past events. While ML is expected to help address such challenging situations, it comes with its own epistemological limitations. Two issues, theory-ladenness and non-uniqueness, can undermine the evidential power of ML outcomes in scientific inferences. ML’s success in aiding Galactic archaeology teaches lessons about how it can serve key epistemic functions for the historical sciences, unaffected by those two issues. I show that ML can empower the evidence-gathering procedure in science, not by outputting data to be directly taken as evidence, but as evidential constraints: ML partitions and structures data for it to be subsequently interpreted as evidence with the help of domain knowledge and inferential procedures. Used in this way, ML not only provides a solution to the ambiguity of historical evidence but also avoids its own epistemic limitations. This use of ML can be generalized to other historical sciences that engage with big data and automated inference. (shrink)

Computers are already approving financial transactions, controlling electrical supplies, and driving trains. Soon, service robots will be taking care of the elderly in their homes, and military robots will have their own targeting and firing protocols. Colin Allen and Wendell Wallach argue that as robots take on more and more responsibility, they must be programmed with moral decision-making abilities, for our own safety. Taking a fast paced tour through the latest thinking about philosophical ethics and artificial intelligence, the authors argue (...) that even if full moral agency for machines is a long way off, it is already necessary to start building a kind of functional morality, in which artificial moral agents have some basic ethical sensitivity. But the standard ethical theories don't seem adequate, and more socially engaged and engaging robots will be needed. As the authors show, the quest to build machines that are capable of telling right from wrong has begun. -/- Moral Machines is the first book to examine the challenge of building artificial moral agents, probing deeply into the nature of human decision making and ethics. (shrink)

When he hears about automation, automatic factories, and unmanned manufacture, the worker wonders with a certain anxiety what will be his fate in an industry which is undergoing transformation and whether the trade from which he draws his livelihood today does not risk becoming useless tomorrow and leaving him without work. No doubt he has been told that the machine will never be able to replace man entirely, that there is no danger of unemployment, since new machines create new (...) jobs, and that he will be freed of heavy labor and fatigue, thanks to the automatized factory. But these arguments are not all valid for the man whose job is eliminated or modified and who must find another situation or adapt himself to a different kind of work. It makes no difference if the over-all perspectives are reassuring—they are less so on the individual's level. The economy may continue to experience a cycle of full employment, but the individual may have to face the depressing hunt for a new job or start at the bottom in a different trade. (shrink)

Functional neurological disorder (FND), historically referred to as hysteria, is a contested illness characterised by heterogeneous and often co-occurring neurological symptoms, such as seizures, abnormal movements, and paralysis. Its diagnosis remains challenging due to the disorder’s complexity and lack of standardised procedures. Recent neuroimaging research has sought to link FND symptoms to brain dysfunction, with three pioneering studies using resting-state functional magnetic resonance imaging (fMRI) to train machine learning (ML) classifiers for diagnostic purposes. These studies have aimed to identify (...) biomarkers for future clinical application, while also providing new insights into FND’s neuropathophysiology that have eluded traditional data analyses. Combining perspectives from science and technology studies and media studies, this paper critically examines how researchers navigate the technical constraints of resting-state fMRI when constructing the datasets to train ML classifiers for diagnosing FND. Through a close reading of the published studies, it analyses operations performed during patient selection, resting-state data acquisition, preprocessing, and connectivity modelling to translate FND’s clinical complexity into ML-ready datasets. Drawing on Annemarie Mol’s concept of ontological politics, it argues that these operations enact specific versions of FND as a condition diagnosable through individual patients’ brain connectivity patterns, while excluding multiple key aspects of the disorder as algorithmically illegible. Whereas the studies present ML classifiers as tools to support future clinical decision-making, they overlook the political implications of inadvertent diagnostic exclusions these systems enact. The paper concludes that the development of neuroimaging diagnostic ML classifiers for FND and related conditions demands critical scrutiny of the new diagnostic realities they enact and their implications for patients. (shrink)

In the past few years, machine learning (ML) tools have been implemented with success in the medical context. However, several practitioners have raised concerns about the lack of transparency—at the algorithmic level—of many of these tools; and solutions from the field of explainable AI (XAI) have been seen as a way to open the ‘black box’ and make the tools more trustworthy. Recently, Alex London has argued that in the medical context we do not need machine learning tools (...) to be interpretable at the algorithmic level to make them trustworthy, as long as they meet some strict empirical desiderata. In this paper, we analyse and develop London’s position. In particular, we make two claims. First, we claim that London’s solution to the problem of trust can potentially address another problem, which is how to evaluate the reliability of ML tools in medicine for regulatory purposes. Second, we claim that to deal with this problem, we need to develop London’s views by shifting the focus from the opacity of algorithmic details to the opacity of the way in which ML tools are trained and built. We claim that to regulate AI tools and evaluate their reliability, agencies need an explanation of how ML tools have been built, which requires documenting and justifying the technical choices that practitioners have made in designing such tools. This is because different algorithmic designs may lead to different outcomes, and to the realization of different purposes. However, given that technical choices underlying algorithmic design are shaped by value-laden considerations, opening the black box of the design process means also making transparent and motivating (technical and ethical) values and preferences behind such choices. Using tools from philosophy of technology and philosophy of science, we elaborate a framework showing how an explanation of the training processes of ML tools in medicine should look like. (shrink)

Cognitive theories of consciousness should provide effective frameworks to implement machine consciousness. The Global Workspace Theory is a leading theory of consciousness which postulates that the primary function of consciousness is a global broadcast that facilitates recruitment of internal resources to deal with the current situation as well as modulate several types of learning. In this paper, we look at architectures for machine consciousness that have the Global Workspace Theory as their basis and discuss the requirements in such (...) architectures to bring about both functional and phenomenal aspects of consciousness in machines. (shrink)

In this paper, I shall review the reasons that let Putnam to propose functionalism and the reasons that subsequently led him to abandon it. I would like to discuss Putnam's views belonging to early Putnam and later Putnam. First, let us focus on early Putnam. Early Putnam tries to show the possibility of robot consciousness. As a functionalist, Putnam shows that the human being is an autonomous: that is, human mind is a computing machine. Later, he changes his (...) position to demonstrate not just his intellectual confidence, but also the virtue of seeing the bigger picture. Later Putnam realized this because this sort of utopianism is an illustration of what is called ‘scientism’. It is based on speculations regarding scientific possibilities. The problem is that it is completely unclear just what possibility has been envisaged when one speaks of robotic consciousness. While arguing against Turing machine, the later Putnam said that pessimism about the success of artificial intelligence (AI) in simulating human intelligence amounts to pessimism about possibility of describing the functioning of the brain. He raised the question: what connection is there between simulating intelligence and describing the brain? Even if the computer model of the brain is correct, it does not follow that AI will succeed. Simulation is just simulation, it does not mean exact replications of human mind. (shrink)

The machine conception of the organism (MCO) is one of the most pervasive notions in modern biology. However, it has not yet received much attention by philosophers of biology. The MCO has its origins in Cartesian natural philosophy, and it is based on the metaphorical redescription of the organism as a machine. In this paper I argue that although organisms and machines resemble each other in some basic respects, they are actually very different kinds of systems. I submit (...) that the most significant difference between organisms and machines is that the former are intrinsically purposive whereas the latter are extrinsically purposive. Using this distinction as a starting point, I discuss a wide range of dissimilarities between organisms and machines that collectively lay bare the inadequacy of the MCO as a general theory of living systems. To account for the MCO’s prevalence in biology, I distinguish between its theoretical, heuristic, and rhetorical functions. I explain why the MCO is valuable when it is employed heuristically but not theoretically, and finally I illustrate the serious problems that arise from the rhetorical appeal to the MCO. (shrink)

The properties of Turing’s famous ‘universal machine’ has long sustained functionalist intuitions about the nature of cognition. Here, I show that there is a logical problem with standard functionalist arguments for multiple realizability. These arguments rely essentially on Turing’s powerful insights regarding computation. In addressing a possible reply to this criticism, I further argue that functionalism is not a useful approach for understanding what it is to have a mind. In particular, I show that the difficulties involved in (...) distinguishing implementation from function make multiple realizability claims untestable and uninformative. As a result, I conclude that the role of Turing machines in philosophy of mind needs to be reconsidered. (shrink)

Machine learning is the study of algorithms that enable computers to improve their performance and increase their knowledge base. Research in machine learning has taken place since the beginning of artificial intelligence in the mid‐1950s. The first notable success was Arthur Samuel's program that learned to play checkers well enough to beat skilled humans. The program estimated the best move in a situation by using a mathematical function whose sixteen parameters describe board positions, and it improved its performance (...) by adjusting weights on the parameters in order to generalize about what moves were successful. (shrink)

According to the most popular theories of intentionality, a family of theories we will refer to as “functional intentionality,” a machine can have genuine intentional states so long as it has functionally characterizable mental states that are causally hooked up to the world in the right way. This paper considers a detailed description of a robot that seems to meet the conditions of functional intentionality, but which falls victim to what I call “the composition problem.” One obvious way to (...) escape the problem (arguably, the only way) is if the robot can be shown to be a moral patient – to deserve a particular moral status. If so, it isn’t clear how functional intentionality could remain plausible (something like “phenomenal intentionality” would be required). Finally, while it would have seemed that a reasonable strategy for establishing the moral status of intelligent machines would be to demonstrate that the machine possessed genuine intentionality, the composition argument suggests that the order of precedence is reversed: The machine must first be shown to possess a particular moral status before it is a candidate for having genuine intentionality. (shrink)

This study develops a structural diagnosis of contemporary transformations in scientific knowledge production under conditions of synthetic intelligence. It introduces the Structural Diagnosis of Scientific Integrity (SDSI) framework, a multi-layer analytical architecture designed to examine how algorithmic mediation may reconfigure the epistemic infrastructure of science. The framework integrates philosophical analysis, metascientific insights, and qualitative observation of generative AI systems. At the centre of the framework lies the concept of Machine Epistemic Singularity (MES) , defined not as a prediction of (...) collapse but as a diagnostic horizon—a conceptual limit clarifying what is at stake when algorithmic systems become deeply embedded in scientific practice. MES denotes a hypothetical regime in which empirical correction becomes structurally marginal, recursive closure becomes self-sustaining, representational drift becomes resistant to reversal, and institutional mechanisms lose their capacity to maintain what the study terms material accountability: the property of epistemic systems whose outputs remain corrigible through empirical engagement with the world. The SDSI framework consists of four interconnected components. First, a diagnostic grammar grounded in the representation–referent distinction identifies referential attenuation, the progressive weakening of causal and evidential links between scientific claims and empirical origins. Second, the regulative ideal of material accountability provides a normative anchor for assessing epistemic stability across hybrid human–machine assemblages. Third, four structural tendencies—referential attenuation, structural opacity, recursive closure, and epistemic lock in—describe mechanisms through which algorithmic mediation may weaken epistemic accountability. Fourth, fifteen axes of epistemic vulnerability, distributed across empirical, representational, institutional, and algorithmic layers, function as systemic stress vectors within a multi layer epistemic network. To explore whether these mechanisms are observable in practice, the study employs qualitative diagnostic probes examining contemporary generative AI systems. Five probes—unattributed recombination, performance without understanding, authoritative posture without accountability, synthetic drift, and recursive reinforcement—indicate that the mechanisms theorised in the framework are operational in micro form within systems increasingly used in research adjacent workflows. The analysis is then extended to system level dynamics by modelling science as a multi layer epistemic network. Three propagation mechanisms—amplification, accumulation, and structural coupling—are identified, along with three threshold conditions—empirical dilution, validation saturation, and recursive dependence—proposed as empirically monitorable indicators of when localised vulnerabilities may become self reinforcing. The study advances five contributions: (1) material accountability as a normative anchor for evaluating epistemic stability; (2) algorithmic mediation as a distinct epistemic layer within scientific infrastructure; (3) four structural tendencies describing pathways of epistemic drift; (4) a systemic stress model derived from fifteen axes of vulnerability; and (5) MES as a diagnostic horizon integrating these mechanisms. The framework is offered as a diagnostic lens rather than a predictive or prescriptive programme. Its aim is not to determine the future of science, but to illuminate the structural pressures that may shape scientific knowledge production as algorithmic systems become increasingly integrated into its epistemic architecture. (shrink)

Implementing sensitivity to norms, laws, and human values in computational systems has transitioned from philosophical reflection to an actual engineering challenge. The “value alignment” approach to dealing with superintelligent AIs tends to employ computationally friendly concepts such as utility functions, system goals, agent preferences, and value optimizers, which, this chapter argues, do not have intrinsic ethical significance. This chapter considers what may be lost in the excision of intrinsically ethical concepts from the project of engineering moral machines. It argues that (...) human-level AI and superintelligent systems can be assured to be safe and beneficial only if they embody something like virtue or moral character and that virtue embodiment is a more appropriate long-term goal for AI safety research than value alignment. (shrink)

Existing approaches to machine ethics harbor an unquestioned commitment to the development of ethical machines and an unreflective optimism that ethical principles can be executable by machines. The first part of this paper raises two challenges to such dogmas: the hypocrisy challenge and the disagreement challenge. The first challenge is that, aside from finding the right machine ethics program, machine ethicists must consider whether their development of such machines is consistent with the precepts of their adopted ethical (...) theory. The second challenge concerns whether the focus on particular ethical theories (especially rationalist and codifiable ones) in the field of machine ethics involves a biased representation of ethical traditions that contradicts commitments to inclusion and pluralism. In the second part of the paper, we offer a possible candidate for machine ethics we call the “skillful-expert model.” This model is based on the assumption that, for machines that have well-defined functions, the requirement of ethical goodness is equivalent to the requirement of skillful performance. We argue that this model has strong support from the neglected, virtue-ethical and phenomenological traditions of ethics, and sidesteps the major challenges raised in the first part. (shrink)

PurposeThe primary angle-closure glaucoma is an irreversible blinding eye disease in the world. Previous neuroimaging studies demonstrated that PACG patients were associated with cerebral changes. However, the effect of optic atrophy on local and remote brain functional connectivity in PACG patients remains unknown.Materials and MethodsIn total, 23 patients with PACG and 23 well-matched Health Controls were enrolled in our study and underwent resting-state functional magnetic resonance imaging scanning. The regional homogeneity method and functional connectivity method were used to evaluate the (...) local and remote brain functional connectivity. Moreover, support vector machine method was applied to constructing PACG classification model.ResultsCompared with the HC, PACG patients showed increased ReHo values in right cerebellum _8, left CER_4-5, and right CER_8. In contrast, PACG patients showed decreased ReHo values in the bilateral lingual gyrus /calcarine /superior occipital gyrus and right postcentral gyrus. The ReHo value exhibited an accuracy of 91.30% and area under curve of 0.95 for distinguishing the PACG patients from HC.ConclusionOur study demonstrated that the PACG patients showed abnormal ReHo value in the cerebellum, visual cortex, and supplementary motor area, which might be reflect the neurological mechanisms underlying vision loss and eye pain in PACG patients. Moreover, the ReHo values can be used as a useful biomarker for distinguishing the PACG patients from HCs. (shrink)

Alcohol use disorder is a burden to society creating social and health problems. Detection of AUD and its effects on the brain are difficult to assess. This problem is enhanced by the comorbid use of other substances such as nicotine that has been present in previous studies. Recent machine learning algorithms have raised the attention of researchers as a useful tool in studying and detecting AUD. This work uses AUD and controls samples free of any other substance use to (...) assess the performance of a set of commonly used machine learning classifiers detecting AUD from resting state functional network connectivity derived from independent component analysis. The cohort used included 51 alcohol dependent subjects and 51 control subjects. Despite alcohol, none of the 102 subjects reported use of nicotine, cannabis or any other dependence or habit formation substance. Classification features consisted of whole brain rsFNC estimates undergoing a feature selection process using a random forest approach. Features were then fed to 10 different machine learning classifiers to be evaluated based on their classification performance. A neural network classifier showed the highest performance with an area under the curve of 0.79. Other good performers with similar AUC scores were logistic regression, nearest neighbor, and support vector machine classifiers. The worst results were obtained with Gaussian process and quadratic discriminant analysis. The feature selection outcome pointed to functional connections between visual, sensorimotor, executive control, reward, and salience networks as the most relevant for classification. We conclude that AUD can be identified using machine learning classifiers in the absence of nicotine comorbidity. (shrink)

Brain-machine interfaces are a growing field of research and application. The increasing possibilities to connect the human brain to electronic devices and computer software can be put to use in medicine, the military, and entertainment. Concrete technologies include cochlear implants, Deep Brain Stimulation, neurofeedback and neuroprosthesis. The expectations for the near and further future are high, though it is difficult to separate hope from hype. The focus in this paper is on the effects that these new technologies may have (...) on our ‘symbolic order’—on the ways in which popular categories and concepts may change or be reinterpreted. First, the blurring distinction between man and machine and the idea of the cyborg are discussed. It is argued that the morally relevant difference is that between persons and non-persons, which does not necessarily coincide with the distinction between man and machine. The concept of the person remains useful. It may, however, become more difficult to assess the limits of the human body. Next, the distinction between body and mind is discussed. The mind is increasingly seen as a function of the brain, and thus understood in bodily and mechanical terms. This raises questions concerning concepts of free will and moral responsibility that may have far reaching consequences in the field of law, where some have argued for a revision of our criminal justice system, from retributivist to consequentialist. Even without such a (unlikely and unwarranted) revision occurring, brain-machine interactions raise many interesting questions regarding distribution and attribution of responsibility. (shrink)

The prospect of consciousness in artificial systems is closely tied to the viability of functionalism about consciousness. Even if consciousness arises from the abstract functional relationships between the parts of a system, it does not follow that any digital system that implements the right functional organization would be conscious. Functionalism requires constraints on what it takes to properly implement an organization. Existing proposals for constraints on implementation relate to the integrity of the parts and states of the realizers (...) of roles in a functional organization. This paper presents and motivates three novel integrity constraints on proper implementation not satisfied by current neural network models. It is proposed that for a system to be conscious, there must be a straightforward relationship between the material entities that compose the system and the realizers of functional roles, that the realizers of the functional roles must play their roles due to internal causal powers, and that they must continue to exist over time. (shrink)

Many foundational concepts in machine learning have been criticized as inadequate. Philosophers have therefore taken it upon themselves to sort out the conceptual terrain—with conceptual engineering being the method of choice. This paper takes a step back to provide theoretical and methodological grounding for future work on conceptual engineering in machine learning. To this end, we consider the functional roles of concepts in machine learning, the underlying causes and types of deficiency, and map out criteria for the (...) successful propagation of reengineered concepts within and beyond the machine learning community. Moreover, we discuss how the space of viable conceptual revisions in machine learning is constrained by the need for operationalization, and how tensions can occur between the sociopolitical desirability and the computational implementability of relevant conceptual engineering projects. Overall, our goal is to delineate how conceptual work in philosophy ought to be if the goal is for our contribution to permeate through the science and practice of machine learning. (shrink)

his article will focus on the mechanistic origins of the computer metaphor, which forms the conceptual framework for the methodology of the cognitive sciences, some areas of artificial intelligence and the philosophy of mind. The connection between the history of computing technology, epistemology and the philosophy of mind is expressed through the metaphorical dictionaries of the philosophical discourse of a particular era. The conceptual clarification of this connection and the substantiation of the mechanistic components of the computer metaphor is the (...) main goal of this article. The statement is substantiated that the invention of mechanical computing devices, having a long history in the European engineering tradition, formed the prerequisites for the emergence of machine functionalism in the modern philosophy of mind. The idea of multiple implementation stems from the principle that a formal symbol system prescribes rules for the use of rational abstractions through the physical architecture of a computational engine. The article considers the reasons for the conceptual shift and reveals the semantic foundations for the metaphorical transfer of the properties of abstract objects from the theory of automata to the field of modern philosophy of mind. The criticism and ways of protecting the philosophical program of machine functionalism are analyzed by changing the content of the metaphor “Mind as machine”. The reasons for the stability of the information-computer approach in cognitive sciences are also disclosed and explained. (shrink)

Table of Contents Foreword .................................................................................................... ......................................... xiv Preface .................................................................................................... .............................................. xv Acknowledgment .................................................................................................... .......................... xxiii Section 1 On the Cusp: Critical Appraisals of a Growing Dependency on Intelligent Machines Chapter 1 Algorithms versus Hive Minds and the Fate of Democracy ................................................................... 1 Rick Searle, IEET, USA Chapter 2 We Can Make Anything: Should We? .................................................................................................. 15 Chris Bateman, University of Bolton, UK Chapter 3 Grounding Machine Ethics within the Natural System ........................................................................ 30 Jared Gassen, JMG Advising, USA Nak Young Seong, Independent Scholar, (...) South Korea Section 2 From the Outside In: Intelligent Machine Technologies as a Window on Human Morality both as Evolved and as Evident in Internet Discourse, Today Chapter 4 The Emergence of Arti cial Autonomy: A View from the Foothills of a Challenging Climb ............. 51 Fernando da Costa Cardoso, Universidade Nova de Lisboa, Portugal Luís Moniz Pereira, Universidade Nova de Lisboa, Portugal Chapter 5 Semantic Analysis of Bloggers Experiences as a Knowledge Source of Average Human Morality .... 73 Rafal Rzepka, Hokkaido University, Japan Kenji Araki, Hokkaido University, Japan Section 3 From the Inside Out: The Ethics of Human Enhancement from Moral Perception to Competition in the Workplace Chapter 6 Machine Ethics Interfaces: An Ethics of Perception of Nanocognition ............................................... 97 Melanie Swan, Kingston University, UK Chapter 7 Ethical Concerns in Human Enhancement: Advantages in Corporate/Organizational Settings ......... 124 Ben Tran, Alliant International University, USA Section 4 From Far to Near and Near to Far: The Ethics of Distancing Technologies in Education and Warfare Chapter 8 Responsibility and War Machines: Toward a Forward-Looking and Functional Account ................. 152 Jai Galliott, Macquarie University, Australia Chapter 9 Ethical Responsibilities of Preserving Academicians in an Age of Mechanized Learning: Balancing the Demands of Educating at Capacity and Preserving Human Interactivity ................... 166 James E. Willis III, Indiana University, USA Viktoria Alane Strunk, Independent Scholar, USA Section 5 Wrapping Things Up, then Unwrapping Them Again: Integral Visions of Morality in a Technological World, Over Evolutionary Time, with Revolutionary Means, and with Open Questions about the Final Purpose of It All Chapter 10 Bridging Two Realms of Machine Ethics ........................................................................................... 197 Luís Moniz Pereira, Universidade Nova de Lisboa, Portugal Ari Saptawijaya, Universidade Nova de Lisboa, Portugal & Universitas Indonesia, Indonesia Chapter 11 Robots in Warfare and the Occultation of the Existential Nature of Violence ................................... 225 Rick Searle, IEET, USA Chapter 12 Self-Referential Complex Systems and Aristotle’s Four Causes ........................................................ 239 Aleksandar Malecic, University of Nis, Serbia Related References .................................................................................................... ........................ 261 Compilation of References .................................................................................................... ........... 292 About the Contributors .................................................................................................... ................ 325 Index .................................................................................................... ............................................... 329 . (shrink)

Is ethics a computable function? Can machines learn ethics like humans do? If teaching consists in no more than programming, training, indoctrinating… and if ethics is merely following a code of conduct, then yes, we can teach ethics to algorithmic machines. But if ethics is not merely about following a code of conduct or about imitating the behavior of others, then an approach based on computing outcomes, and on the reduction of ethics to the compilation and application of a set (...) of rules, either a priori or learned, misses the point. Our intention is not to solve the technical problem of machine ethics, but to learn something about human ethics, and its rationality, by reflecting on the ethics that can and should be implemented in machines. Any machine ethics implementation will have to face a number of fundamental or conceptual problems, which in the end refer to philosophical questions, such as: what is a human being (or more generally, what is a worthy being); what is human intentional acting; and how are intentional actions and their consequences morally evaluated. We are convinced that a proper understanding of ethical issues in AI can teach us something valuable about ourselves, and what it means to lead a free and responsible ethical life, that is, being good people beyond merely “following a moral code”. In the end we believe that rationality must be seen to involve more than just computing, and that value rationality is beyond numbers. Such an understanding is a required step to recovering a renewed rationality of ethics, one that is urgently needed in our highly technified society. (shrink)

The extent to which machine metaphors are used in synthetic biology is striking. These metaphors contain a specific perspective on organisms as well as on scientific and technological progress. Expressions such as “genetically engineered machine”, “genetic circuit”, and “platform organism”, taken from the realms of electronic engineering, car manufacturing, and information technology, highlight specific aspects of the functioning of living beings while at the same time hiding others, such as evolutionary change and interdependencies in ecosystems. Since these latter (...) aspects are relevant for, for example, risk evaluation of uncontained uses of synthetic organisms, it is ethically imperative to resist the thrust of machine metaphors in this respect. In addition, from the perspective of the machine metaphor viewing an entity as a moral agent or patient becomes dubious. If one were to regard living beings, including humans, as machines, it becomes difficult to justify ascriptions of moral status. Finally, the machine metaphor reinforces beliefs in the potential of synthetic biology to play a decisive role in solving societal problems, and downplays the role of alternative technological, and social and political measures. (shrink)