Clinical ethics can be viewed as a practical discipline that provides a structured approach to assist healthcare practitioners in identifying, analysing and resolving ethical issues that arise in practice. Clinical ethics can therefore promote ethically sound clinical and organisational practices and decision-making, thereby contributing to health organisation and system quality improvement. In order to develop students’ decision-making skills, as well as prepare them for practice, we decided to introduce a clinical ethics strand within an undergraduate medical curriculum. We designed a (...) programme of clinical ethics activities for teaching and assessment purposes that involved using ethical frameworks to analyse hypothetical and real-life cases in uni- and inter- professional groups. In this paper, we draw on medical student feedback collected over 6 years to illustrate the appeal to students of learning clinical ethics. We also outline the range of benefits for students, healthcare organisations, and the field of clinical ethics arising from tomorrow’s doctors experiencing clinical ethics early in their training. We conclude by briefly reflecting on how including clinical ethics within tomorrow’s doctors curricular can secure and continue future engagement in clinical ethics support services in the UK, alongside the dangers of preparing students for organisational cultures that might not exist. We anticipate the findings presented in the paper will contribute to wider debates examining the impact of ethics teaching, and its ability to inform future doctors’ practice. (shrink)

Although the analogy between macroscopic machines and biological molecular devices plays an important role in the conceptual framework of both neo-mechanistic accounts and nanotechnology, it has recently been claimed that certain complex molecular devices cannot be considered machines since they are subject to physicochemical forces that are different from those of macroscopic machines. However, the structural and physicochemical conditions that allow both macroscopic machines and microscopic devices to work and perform new functions, through a combination (...) of elemental functional parts, have not yet been examined. In order to fill this void, this paper has a threefold aim: first, to clarify the structural and organisational conditions of macroscopic machines and microscopic devices; second, to determine whether the machine-like analogy fits nanoscale devices; and third, to assess whether the machine-like analogy is appropriate for describing the behaviour of some biological macromolecules. Finally, the paper gives an account of ‘machine’ which, while acknowledging the physicochemical and organisational differences between man-made machines and biological microscopic devices, nevertheless identifies a common conceptual core that allows us to consider the latter ‘machines’. (shrink)

As disparities and data science researchers, we write in response to Char and colleagues paper on “Identifying Ethical Considerations for Machine Learning Healthcare Applications.” While the...

What is it to interpret the outputs of an opaque machine learning model? One approach is to develop interpretable machine learning techniques. These techniques aim to show how machine learning models function by providing either model-centric local or global explanations, which can be based on mechanistic interpretations (revealing the inner working mechanisms of models) or non-mechanistic approximations (showing input feature–output data relationships). In this paper, we draw on social philosophy to argue that interpreting machine learning outputs in certain normatively salient (...) domains could require appealing to a third type of explanation that we call “socio-structural” explanation. The relevance of this explanation type is motivated by the fact that machine learning models are not isolated entities but are embedded within and shaped by social structures. Socio-structural explanations aim to illustrate how social structures contribute to and partially explain the outputs of machine learning models. We demonstrate the importance of socio-structural explanations by examining a racially biased healthcare allocation algorithm. Our proposal highlights the need for transparency beyond model interpretability: understanding the outputs of machine learning systems could require a broader analysis that extends beyond the understanding of the machine learning model itself. (shrink)

(1997). Organisms, machines, and societies: From the vertical structure of adaptability to the management of information. World Futures: Vol. 50, No. 1-4, pp. 667-687.

BackgroundWithholding and withdrawing treatment are deemed ethically equivalent by most Bioethicists, but intensivists often find withdrawing more difficult in practice. This can lead to futile treatment being prolonged. Time-limited trials have been proposed as a way of promoting timely treatment withdrawal whilst giving the patient the greatest chance of recovery. Despite being in UK guidelines, time-limited trials have been infrequently implemented on Intensive Care Units. We will explore the role of time in Intensive Care Unit decision-making and provide a UK (...) perspective on debates surrounding time-limited trials.MethodsThis qualitative study recruited 18 participants (nine doctors, nine nurses) from two Intensive Care Units in North West England for in-depth, one-to-one semi-structured interviews. A thematic analysis was performed of the data.ResultsOur findings show time is utilised by Intensive Care Unit staff in a variety of ways including managing uncertainty when making decisions about a patient’s prognosis or the reversibility of a disease, constructing relationships with patients’ relatives, communicating difficult messages to patients’ relatives, justifying resource allocation decisions to colleagues, and demonstrating compassion towards patients and their families.ConclusionsTime shifts the balance towards greater certainty in Intensive Care Unit decision-making, by demonstrating futility, and can ease the difficult transition for staff and families from active treatment to palliation. However, this requires clear and open communication, both within the Intensive Care Unit team and with the family, being prioritised when time is used in decision-making. (shrink)

This paper aims to consider the main features of the philosophy of linguistics proposed by Deleuze and Guattari, which emerges from the criticisms directed at what in A Thousand Plateaus they call ‘postulates of linguistics’. The paper focuses on the transition from the Saussurean concept of system and from the connected notion of structure to Deleuze and Guattari's concept of machine. More precisely, the purpose of the paper lies, on the one hand, in showing in which sense Deleuze and Guattari (...) claim that language is not a ‘structure’ but a ‘machine’ and why, accordingly, they maintain that the mentioned ‘postulates of linguistics’ must be refused; on the other hand, the paper represents an attempt at placing Deleuze and Guattari's position in the context of contemporary linguistics. (shrink)

Over recent years, the stakes and complexity of online content moderation have been steadily raised, swelling from concerns about personal conflict in smaller communities to worries about effects on public life and democracy. Because of the massive growth in online expressions, automated tools based on machine learning are increasingly used to moderate speech. While ‘design-based governance’ through complex algorithmic techniques has come under intense scrutiny, critical research covering algorithmic content moderation is still rare. To add to our understanding of concrete (...) instances of machine moderation, this article examines Perspective API, a system for the automated detection of ‘toxicity’ developed and run by the Google unit Jigsaw that can be used by websites to help moderate their forums and comment sections. The article proceeds in four steps. First, we present our methodological strategy and the empirical materials we were able to draw on, including interviews, documentation, and GitHub repositories. We then summarize our findings along five axes to identify the various threads Perspective API brings together to deliver a working product. The third section discusses two conflicting organizational logics within the project, paying attention to both critique and what can be learned from the specific case at hand. We conclude by arguing that the opposition between ‘human’ and ‘machine’ in speech moderation obscures the many ways these two come together in concrete systems, and suggest that the way forward requires proactive engagement with the design of technologies as well as the institutions they are embedded in. (shrink)

With 500+ papers and 20+ books spanning many scientific disciplines, Mark Burgin has left an indelible mark and legacy for future explorers of human thought and information technology professionals. In this paper, I discuss his contribution to the evolution of machine intelligence using his general theory of information (GTI) based on my discussions with him and various papers I co-authored during the past eight years. His construction of a new class of digital automata to overcome the barrier posed by the (...) Church–Turing Thesis, and his contribution to super-symbolic computing with knowledge structures, cognizing oracles, and structural machines are leading to practical applications changing the future landscape of information systems. GTI provides a model for the operational knowledge of biological systems to build, operate, and manage life processes using 30+ trillion cells capable of replication and metabolism. The schema and associated operations derived from GTI are also used to model a digital genome specifying the operational knowledge of algorithms executing the software life processes with specific purposes using replication and metabolism. The result is a digital software system with a super-symbolic computing structure exhibiting autopoietic and cognitive behaviors that biological systems also exhibit. We discuss here one of these applications. (shrink)

Applying machine learning algorithms to automatically infer relationships between concepts from large-scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments (“How similar are cats and bears?”), and how these judgments depend on the features that describe concepts (e.g., size, furriness). However, efforts to date have exhibited a substantial discrepancy between algorithm predictions and human empirical judgments. Here, we introduce a novel approach to generating (...) embeddings for this purpose motivated by the idea that semantic context plays a critical role in human judgment. We leverage this idea by constraining the topic or domain from which documents used for generating embeddings are drawn (e.g., referring to the natural world vs. transportation apparatus). Specifically, we trained state-of-the-art machine learning algorithms using contextually-constrained text corpora (domain-specific subsets of Wikipedia articles, 50+ million words each) and showed that this procedure greatly improved predictions of empirical similarity judgments and feature ratings of contextually relevant concepts. Furthermore, we describe a novel, computationally tractable method for improving predictions of contextually-unconstrained embedding models based on dimensionality reduction of their internal representation to a small number of contextually relevant semantic features. By improving the correspondence between predictions derived automatically by machine learning methods using vast amounts of data and more limited, but direct empirical measurements of human judgments, our approach may help leverage the availability of online corpora to better understand the structure of human semantic representations and how people make judgments based on those. (shrink)

Analogies to machines are commonplace in the life sciences, especially in cellular and molecular biology — they shape conceptions of phenomena and expectations about how they are to be explained. This paper offers a framework for thinking about such analogies. The guiding idea is that machine-like systems are especially amenable to decompositional explanation, i.e., to analyses that tease apart underlying components and attend to their structural features and interrelations. I argue that for decomposition to succeed a system must (...) exhibit causal orderliness, which I explicate in terms of differentiation among parts and the significance of local relations. I also discuss what makes a model depict its target as machine-like, suggesting that a key issue is the degree of detail with respect to the target’s parts and their interrelations. (shrink)

Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder, definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls. To enhance the interpretability of the machine (...) learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule and regional gray matter volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases. (shrink)

I Would like to draw attention to the basic defect in the argument used by Mr J. R. Lucas.Mr Lucas there states that Gödel's theorem shows that any consistent formal system strong enough to produce arithmetic fails to prove, within its own structure, theorems that we, as humans, can nevertheless see to be true. From this he argues that ‘minds’ can do more than machines, since machines are essentially formal systems of this same type, and subject to the (...) limitation implied by Godel's theorem. (shrink)

Existing evaluation metrics for machine translation lack crucial robustness: their correlations with human quality judgments vary considerably across languages and genres. We believe that the main reason is their inability to properly capture meaning: A good translation candidate means the same thing as the reference translation, regardless of formulation. We propose a metric that evaluates MT output based on a rich set of features motivated by textual entailment, such as lexical-semantic (in-)compatibility and argument structure overlap. We compare this metric against (...) a combination metric of four state-of-theart scores (BLEU, NIST, TER, and METEOR) in two different settings. The combination metric outperforms the individual scores, but is bested by the entailment-based metric. Combining the entailment and traditional features yields further improvements. (shrink)

This study investigates the performance of 21 monolingual and 56 bilingual children aged 5;6-9;0 on German-LITMUS-sentence-repetition (SRT; Hamann et al., 2013) and nonword-repetition-tasks (NWRT; Grimm et al., 2014), which were constructed according to the LITMUS-principles (Language Impairment Testing in Multilingual Settings; Armon-Lotem et al., 2015). Both tasks incorporate complex structures shown to be cross-linguistically challenging for children with Specific Language Impairment (SLI) and aim at minimizing bias against bilingual children while still being indicative of the presence of language impairment across (...) language combinations. Given the great variability in bilingual language exposure and the potential effect of language experience on language performance in bilingual children, we examined whether background variables related to bilingualism, particularly, the degree language dominance as measured by relative amount of use and exposure, could compromise the diagnostic accuracy of the German-LITMUS-SRT and NWRT. We further investigated whether a combination of the two tasks provides better diagnostic accurracy. To address this, we used an unsupervised machine learning algorithm, the Partitioning-Around-Medoids (PAM), for deriving a clinical category for the children as ±language-impaired based on performance scores on SRT and/or NWRT while withholding information about their clinical status based on standardized assessment in L1/L2. Subsequently, we calculated diagnostic accuracy and investigated which background variables (age of onset, length of exposure, degree of language-dominance, socio-economic-status, and risk factors for SLI) best explained clinical-group-membership yielded from the PAM-analysis based on NWRT and/or SRT scores. Results show that although language-dominance influences the performance of bilingual typically-developing children, especially in the SRT, the diagnostic accuracy of the tools is not compromised by language dominance: while risk factors for SLI were significant predictors for clinical group membership in all models, language dominance did not contribute at all to explaining clinical cluster membership as typically-developing or SLI based on any of the combinations of the SRT and NWRT variables. Additionally, results confirm that a combination of SRT scored by correct target structure and the structurally more complex language-dependent part of the NWRT yields better diagnostic accuracy than single measures and is only sensitive for risk factors for SLI and not to dominance levels or SES. (shrink)

Current developments in fields such as quantum physics, fine arts, robotics, cognitive sciences or defense and security indicate the emergence of creative systems capable of producing new and innovative solutions through combinations of machine learning algorithms. These systems, called machine invention systems, challenge the established invention paradigm in promising the automation of – at least parts of – the innovation process. This paper’s main contribution is twofold. Based on the identified state-of-the-art examples in the above mentioned fields, key components for (...) machine invention systems and their relations are identified, creating a conceptual model as well as proposing a working definition for machine invention systems. The differences and delimitations to other concepts in the field of machine learning and artificial intelligence, such as machine discovery systems are discussed as well. Furthermore, the paper briefly addresses the social and societal implications and limitations that come with the adoption of the technology. Because of their revolutionizing potential, there are widespread implications to consider from ethical and moral implications to policymaking and societal changes, like changes in the job structure. The discussion part approaches some of these implications, as well as solutions to some of the proposed challenges. The paper concludes by discussing some of the systemic benefits that can be accessed through machine invention. (shrink)

The focus of this article is a question that has been neglected in debates about digitalization: Could machines replace human scientists? To provide an intelligible answer to it, we need to answer a further question: What is it that makes (or constitutes) a scientist? I offer an answer to this question by proposing a new demarcation criterion for science which I call “the discoverability criterion”. I proceed as follows: (1) I explain why the target question of this article is (...) important, and (2) show that it leads to a variant of the demarcation problem of science. (3) By arguing that it is probably an essential feature of science that we can make scientific discoveries, I suggest a novel way of dealing with this problem by proposing a new demarcation criterion. Before introducing it, (4) I analyze an exemplary case of a real scientific discovery, and (5) argue that scientific discovery processes have a general underlying structure. (6) I introduce my discoverability criterion for science and present my master argument that helps us understand which criteria have to be fulfilled in order to decide whether machines can replace human scientists or not. (7) I conclude by answering the article’s target question and bringing forward a take-home message. (shrink)

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...) machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models. (shrink)

Along with potential benefits to healthcare delivery, machine learning healthcare applications raise a number of ethical concerns. Ethical evaluations of ML-HCAs will need to structure th...

Rule-based ethical theories like Kant's appear to be promising for machine ethics because of the computational structure of their judgments. On one formalist interpretation of Kant's categorical imperative, for instance, a machine could place prospective actions into the traditional deontic categories (forbidden, permissible, obligatory) by a simple consistency test on the maxim of action. We might enhance this test by adding a declarative set of subsidiary maxims and other "buttressing" rules. The ethical judgment is then an outcome of the consistency (...) test. While this kind of test can generate results, it may be vacuous in the sense that it would do no more than forbid obviously contradictory maxims of action. It is also possible that the kind of inference in such a rule-based system may be non-monotonic. I discuss these challenges to a rule-based machine ethics, starting from the framework of Kantian ethics. (shrink)

The present paper shows how statistical learning theory and machine learning models can be used to enhance understanding of AI-related epistemological issues regarding inductive reasoning and reliability of generalisations. Towards this aim, the paper proceeds as follows. First, it expounds Price’s dual image of representation in terms of the notions of e-representations and i-representations that constitute subject naturalism. For Price, this is not a strictly anti-representationalist position but rather a dualist one (e- and i-representations). Second, the paper links this debate (...) with machine learning in terms of statistical learning theory becoming more viable epistemological tool when it abandons the perspective of object naturalism. The paper then argues that machine learning grounds a form of knowing that can be understood in terms of e- and i-representation learning. Third, this synthesis shows a way of analysing inductive reasoning in terms of reliability of generalisations stemming from a structure of e- and i-representations. In the age of Artificial Intelligence, connecting Price’s dual view of representation with Deep Learning provides an epistemological way forward and even perhaps an approach to how knowing is possible. (shrink)

In philosophical logic and metaphysics there is a long-standing debate around the most appropriate structures to represent indeterministic scenarios concerning the future. We reconstruct here such a debate in a computational setting, focusing on the fundamental difference between moment-based and history-based structures. Our presentation is centered around two versions of an indeterministic scenario in which a programmer wants a machine to perform a given task at some point after a specified time. One of the two versions includes an assumption about (...) the future behaviour of the machine that cannot be encoded in any programming instruction; such version has models over history-based structures but no model over a moment-based structure. Therefore, our work adds a new stance to the debate: moment-based structures can be said to rule out certain indeterministic scenarios that are computationally unfeasible. (shrink)

The ArgumentThe societal context within which science is pursued generally acts as a productive force in the generation of knowledge. To analyze this action it is helpful to consider particular modes of mediation through which societal concerns are projected into the very local and esoteric concerns of a particular domain of research. One such mode of mediation occurs through material systems. Here I treat two such systems – the steam engine and the electric telegraph – in the natural philosophy of (...) William Thomson (Lord Kelvin).The steam engine illustratesconceptualmediation. It simultaneously instantiates “labor value” in political economy and “work” in engineering mechanics, thereby identifying the two concepts in the region of their common reference. The partial identification carries with it a structural analogy between a network of concepts from political economy and a similar network in natural philosophy, providing a potent heuristic for the reformulation and further development of dynamics.The electric telegraph illustratesmethodologicalmediation. It projects the interests of engineering and industry into the interests of electromagnetic theory and vice versa, thereby establishing, in Thomson's view, a Baconian unity of truth and utility. As the common reference of theory and practice, the telegraph locates the truth of theoretical knowledge in its utility and the utility of practical knowledge in its truth.These particular cases of conceptual and methodological mediation indicate how the local practices, concepts, and interests of a research specialty, or subculture, draw on and are adapted to those of the larger culture within which they develop. Thus the analysis of mediation leads to an ecological model of the social construction of scientific knowledge. (shrink)

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)

Human and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on search of an instance space (empirical exploration) and a hypothesis space (generation of theories). In scientific discovery, search must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This paper focuses especially on the processes for finding new (...) problem representations and new concepts, which are relatively new domains for research on discovery.Scientific discovery has usually been studied as an activity of individual investigators, but these individuals are positioned in a larger social structure of science, being linked by the blackboard of open publication (as well as by direct collaboration). Even while an investigator is working alone, the process is strongly influenced by knowledge and skills stored in memory as a result of previous social interaction. In this sense, all research on discovery, including the investigations on individual processes discussed in this paper, is social psychology, or even sociology. (shrink)

Symmetric instruction machines (SIAs) and symmetric Turing machines (STMs) are models of computation involving concepts derived from those of classical Turing machines such as tape (memory) and head (processor), but with different functional and structural characteristics. The former model (SIAs) introduced in this paper and preferred by Mark Burgin is a result of a reformulation of the latter model (STMs) published in several articles by the second author in the past. The properties of both models are (...) analyzed and compared. The word “symmetric” in both cases represents the feature of the design which is distinct from classical Turing machines where only cells on the tape change under the action of the head. In both models, symmetric computing involves changes of the tape and parallel (“symmetric”) changes of instructions listed in the head. The key difference between SIAs and STMs is in the dynamic of the changes, which in the former model has the form of compound one-way actions and in the latter model, it has the form of uniform mutual interactions, which only in specific realizations can be separated into a pair of actions. Because of the untimely passing of Mark Burgin, the discussion of the two models and cooperation on the paper has never been finished. For this reason, the arguments of both authors are reported even though, in some cases, they are mutually inconsistent or even contradictory. (shrink)

This research provides a contextual description concerning an existential and structural analysis of ‘Relations’ between human beings and machines. Subsequently, it will focus on the conceptual and epistemological analysis of (i) my own semantics-based framework [for human meaning construction] and of (ii) a well-structured machine concept learning framework. Accordingly, I will, semantically and epistemologically, focus on linking those two frameworks for logical analysis of concept learning in the context of human-machine interrelationships. It will be demonstrated that the proposed (...) framework provides a supportive structure over the described contextualisation of ‘relations’ between human beings and machines within concept learning processes. (shrink)

Wants, preferences, and cares are physical things or events, not ideas or propositions, and therefore no chain of pure logic can conclude with a want, preference, or care. It follows that no pure-logic machine will ever want, prefer, or care. And its behavior will never be driven in the way that deliberate human behavior is driven, in other words, it will not be motivated or goal directed. Therefore, if we want to simulate human-style interactions with the world, we will need (...) to first understand the physical structure of goal-directed systems. I argue that all such systems share a common nested structure, consisting of a smaller entity that moves within and is driven by a larger field that contains it. In such systems, the smaller contained entity is directed by the field, but also moves to some degree independently of it, allowing the entity to deviate and return, to show the plasticity and persistence that is characteristic of goal direction. If all this is right, then human want-driven behavior probably involves a behavior-generating mechanism that is contained within a neural field of some kind. In principle, for goal directedness generally, the containment can be virtual, raising the possibility that want-driven behavior could be simulated in standard computational systems. But there are also reasons to believe that goal-direction works better when containment is also physical, suggesting that a new kind of hardware may be necessary. (shrink)

L'article met en cause l'efficacité des différentes machines à communiquer dans la mesure où elles ne sont pas centrées sur les besoins humains. D'où le paradoxe d'un lien social effiloché, voire détruit, au fur et à mesure que les outils techniques se multiplient. Les fractures sociales ainsi engendrées se rencontrent en Amérique latine où l'identité culturelle issue des luttes coloniales se heurte de plein fouet aux structures verticales à sens unique des dispositifs de communication. Mais la déchirure s'observe aussi (...) en Europe, où l'on rencontre l'«illectronisme» dans toutes les classes sociales. S'observe aussi une montée des « suspicions » envers les contenus diffusés dont l'authenticité et la neutralité sont mises en doute.The article calls into question the effectiveness of different communication devices insofar as they are not focused on human needs. Hence the paradox of social bonds frayed or destroyed, as and as technical tools multiply. Social divisions thus generated is found in Latin America where cultural identity after colonial struggles collides with vertical structures-way communication devices. But tearing is also observed in Europe, where we meet the "illectronisme" in all social classes. Is also observed a rise in "suspicions" to broadcasting content whose authenticity and neutrality are in doubt. (shrink)

I Would like to draw attention to the basic defect in the argument used by Mr J. R. Lucas.Mr Lucas there states that Gödel's theorem shows that any consistent formal system strong enough to produce arithmetic fails to prove, within its own structure, theorems that we, as humans, can nevertheless see to be true. From this he argues that ‘minds’ can do more than machines, since machines are essentially formal systems of this same type, and subject to the (...) limitation implied by Godel's theorem. (shrink)

“Captured” in the hands of twenty-first-century structural biologists, “life itself” is taking on new form. The current trend towards molecularization in the life sciences is revealing that “life itself” is denser than the one-dimensional logic of a genetic code: it has a multidimensional material body, and its molecular structures, forces, and movements carry out the regulated work of the cell. Researchers are no longer satisfied reducing the organism to the coding systems embedded in computer software ; the organism now (...) has a mechanical architecture, and its molecular mechanisms have come to resemble the many kinds of machines with which we currently live and work. These include electronic circuitry, and the levers, switches, hinges, springs, locks, and clamps of modern day mechanical devices. These are the molecular machines that populate what is fast becoming the factory-floor of the cell... (shrink)

We human beings do have the power to modify our deep structure, through drugs and surgery. But we cannot yet use this power with enough precision to make deep changes to our neural structure without high risk of death or disability. There are two reasons why we find ourselves in this position. First, our instruments of self-modification are crude. Second, we have very limited knowledge about where and how to apply our instruments to get specific desirable effects. For the same (...) reason, we don't even have good knowledge about what effects are physically possible. (shrink)

In this article I try to show in what sense Emmanuel Levinas’ ‘ethics as first philosophy’ moves our ethical thinking away from what has been called ‘centrist ethics’. Proceeding via depictions of the structure of Levinasian ethics and including references to examples as well as to some empirical research, I try to argue that human beings always already find themselves within an ethical universe, a space of meaning. Critically engaging with the writings of David Gunkel and Lucas Introna, I try (...) to argue that these thinkers, rather than clarifying, distort our ethical understanding of how we stand in relation to artefacts. Drawing a distinction between how pervasive our ethical relationship to other human beings, and living animals, is and how the nature of artefacts is tied to us, I conclude by indicating that the aspiration to give artefacts an ethical face suggests a fantasy to avoid ethical responsibility and generates what I call a ‘compensatory logic’. (shrink)

Practical ability manifested through robust and reliable task performance, as well as information relevance and well-structured representation, are key factors indicative of understanding in the philosophical literature. We explore these factors in the context of deep learning, identifying prominent patterns in how the results of these algorithms represent information. While the estimation applications of modern neural networks do not qualify as the mental activity of persons, we argue that coupling analyses from philosophical accounts with the empirical and theoretical basis for (...) identifying these factors in deep learning representations provides a framework for discussing and critically evaluating potential machine understanding given the continually improving task performance enabled by such algorithms. (shrink)

Well-Structured Mathematical Logic does for logic what Structured Programming did for computation: make large-scale work possible. From the work of George Boole onward, traditional logic was made to look like a form of symbolic algebra. In this work, the logic undergirding conventional mathematics resembles well-structured computer programs. A very important feature of the new system is that it structures the expression of mathematics in much the same way that people already do informally. In this way, the new system is simultaneously (...) machine-parsable and user-friendly, just as Structured Programming is for algorithms. Unlike traditional logic, the new system works with you, not against you, as you use it to structure--and understand--the mathematics you work with on a daily basis. The book provides a complete guide to its subject matter. It presents the major results and theorems one needs to know in order to use the new system effectively. Two chapters provide tutorials for the reader in the new way that symbols move when logical calculations are performed in the well-structured system. Numerous examples and discussions are provided to illustrate the system's many results and features. Well-Structured Mathematical Logic is accessible to anyone who has at least some knowledge of traditional logic to serve as a foundation, and is of interest to all who need a system of pliant, user-friendly mathematical logic to use in their work in mathematics and computer science. (shrink)

An early work that lays the foundation for establishing a “polemical” dimension to psychoanalysis. We certainly have the unconscious that we deserve, an unconscious for specialists, ready-made for an institutionalized discourse. I would rather see it as something that wraps itself around us in everyday objects, something that is involved with day-to-day problems, with the world outside. It would be the possible itself, open to the socius, to the cosmos...—from The Machinic Unconscious: Essays in Schizoanalysis In his seminal solo-authored work (...) The Machinic Unconscious, Félix Guattari lays the groundwork for a general pragmatics capable of resisting the semiotic enslavement of subjectivity. Concluding that psychoanalytic theory had become part and parcel of a repressive, capitalist social order, Guattari here outlines a schizoanalytic theory to undo its capitalist structure and set the discipline back on its feet. Combining theoretical research from fields as diverse as cybernetics, semiotics, ethnology, and ethology, Guattari reintroduces into psychoanalysis a “polemical” dimension, at once transhuman, transsexual, and transcosmic, that brings out the social and political—the “machinic”—potential of the unconscious. To illustrate his theory, Guattari turns to literature and analyzes the various modes of subjectivization and semiotization at work in Proust's In Search of Lost Time, examining the novel as if he were undertaking a scientific exploration in the style of Freud or Newton. Casting Proust's figures as abstract mental objects, Guattari maps the separation between literature and science, elaborating along the way such major Deleuze-Guattarian concepts as “faciality” and “refrain,” which would be unpacked in their subsequent A Thousand Plateaus: Capitalism and Schizophrenia. Never before available in English, The Machinic Unconscious has for too long been the missing chapter from Deleuze and Guattari's Anti-Oedipus project: the most important political extension of May 1968 and one of the most important philosophical contributions of the twentieth century. (shrink)

In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. We call this new approach Gaussian Process Panel Modeling (GPPM). GPPM provides great flexibility because of the large number of models it can represent. It allows classical statistical inference as well as machine learning inspired predictive modeling. GPPM offers frequentist and Bayesian inference without the need to resort to Markov chain Monte Carlo-based approximations, which makes the approach exact and fast. (...) GPPMs are defined using the kernel-language, which can express many traditional modeling approaches for longitudinal data, such as linear structural equation models, multilevel models, or state-space models but also various commonly used machine learning approaches. As a result, GPPM is uniquely able to represent hybrid models combining traditional parametric longitudinal models and nonparametric machine learning models. In the present paper, we introduce GPPM and illustrate its utility through theoretical arguments as well as simulated and empirical data. (shrink)

In this paper, I offer an account of the conceptual shift that occurs between the work completed by Gilles Deleuze prior to 1969 and his later work with Félix Guattari, beginning in 1972 with Anti-Oedipus. Against previous interpretations, which have concentrated on the developments initiated by Deleuze, I argue for the primary importance of Guattari's influence, especially his insistence on a theory of “machinic processes.” The importance of these processes is made manifest in Deleuze and Guattari's move away from theories (...) of structuralism. In order to carry out this task, I offer a close reading of Guattari's essay “Machine and Structure.” This essay was first written as a review of Deleuze's acclaimed work in Difference and Repetition and Logic of Sense and formed the basis for Deleuze and Guattari's first meeting. In the concluding sections of the paper, I show how the integration of the concept of the machine allows Deleuze and Guattari to develop a theory of the unconscious that operates outside of the boundaries traditionally set by structuralist analysis. (shrink)

Synthetic biology is an emerging discipline that aims to apply rational engineering principles in the design and creation of organisms that are exquisitely tailored to human ends. The creation of artificial life raises conceptual, methodological and normative challenges that are ripe for philosophical investigation. This special issue examines the defining concepts and methods of synthetic biology, details the contours of the organism–artifact distinction, situates the products of synthetic biology vis-à-vis this conceptual typology and against historical human manipulation of the living (...) world, and explores the normative implications of these conclusions. In addressing the challenges posed by emerging biotechnologies, new light can be thrown on old problems in the philosophy of biology, such as the nature of the organism, the structure of biological teleology, the utility of engineering metaphors and methods in biological science, and humankind’s relationship to nature. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with the (...) goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

This thesis shows morality to be broader and more diverse than its human instantiation. It uses the idea of machine morality to argue for this position. Specifically, it contrasts the possibilities open to humans with those open to machines to meaningfully engage with the moral domain. -/- This contrast identifies distinctive characteristics of human morality, which are not fundamental to morality itself, but constrain our thinking about morality and its possibilities. It also highlights the inherent potential of machine morality (...) to be radically different from its human counterpart and the implications this has for the moral significance of machines. -/- My argument is particularly focussed on moral theory, which is the study of the observable and hypothetical conceptual structures of morality. By identifying structures that are recognisably moral in nature but which sit outside the boundaries of human realisation, we have tangible proof that a meaningful distinction exists between human morality and the wider moral domain. -/- This is achieved by showing that certain essentially human limits restrict the conceptual possibilities open to human realisation. The tight coupling between these limits and the existing conceptual structures of human morality also explains why it is unjustifiable to assume that the same structures would be suitable for machines. They do not share these same limits with us, which leads me to conclude that many conceptual structures are quite distinctive to human morality and that the structures of machine morality would be significantly different. Four examples illustrate these conclusions concretely. -/- The first, supererogation, is an example of a moral concept that doesn't easily extend to machines. Human limits dictate what it is reasonable to expect from one another and restrict our ability to pursue aspirational moral goals. I show that machine supererogation, if it is at all possible, would require a very different justificatory basis to be coherent. -/- The second, agency, is an example of a concept whose structures extend beyond the bounds of human realisation. The greater flexibility of artificial identity allows machines to experiment with novel forms of inter- and intra-agency. In comparison, human agency structures are limited by their tight coupling with human conceptions of identity. -/- The third, moral aspiration, is a concept with a distinctive function in human morality. Certain aspirational ends are peculiar in that they are obviously unrealisable and even undesirable, yet their pursuit is instrumentally justifiable. This justification depends on cognitive limits that aren't shared by machines, which leads me to conclude that the role of moral aspiration in machine morality, if there is any, would necessarily differ from its human counterpart. -/- The fourth, moral responsibility, is an example of a concept whose existing practices don't translate over to machines. We don't understand machine agency well enough to be able to judge a machine's culpability or effectively blame it. Consequently, I suggest that a responsibility conception prioritising understanding over blame is a more promising avenue for a shared conception suitable for both humans and machines. -/- This thesis does not speculate about the existence of moral machines. This remains an open question, and one that is largely irrelevant for my conclusions as the idea alone is enough to advance our thinking. It does this by helping us identify the boundaries of human morality, and then, to think beyond them by recognising the possibilities of machine morality. (shrink)

We certainly have the unconscious that we deserve, an unconscious for specialists, ready-made for an institutionalized discourse. I would rather see it as something that wraps itself around us in everyday objects, something that is involved with day-to-day problems, with the world outside. It would be the possible itself, open to the socius, to the cosmos...--from The Machinic Unconscious: Essays in SchizoanalysisIn his seminal solo-authored work The Machinic Unconscious, Félix Guattari lays the groundwork for a general pragmatics capable of resisting (...) the semiotic enslavement of subjectivity. Concluding that psychoanalytic theory had become part and parcel of a repressive, capitalist social order, Guattari here outlines a schizoanalytic theory to undo its capitalist structure and set the discipline back on its feet. Combining theoretical research from fields as diverse as cybernetics, semiotics, ethnology, and ethology, Guattari reintroduces into psychoanalysis a "polemical" dimension, at once transhuman, transsexual, and transcosmic, that brings out the social and political--the "machinic"--potential of the unconscious.To illustrate his theory, Guattari turns to literature and analyzes the various modes of subjectivization and semiotization at work in Proust's In Search of Lost Time, examining the novel as if he were undertaking a scientific exploration in the style of Freud or Newton. Casting Proust's figures as abstract mental objects, Guattari maps the separation between literature and science, elaborating along the way such major Deleuze-Guattarian concepts as "faciality" and "refrain," which would be unpacked in their subsequent A Thousand Plateaus: Capitalism and Schizophrenia.Never before available in English, The Machinic Unconscious has for too long been the missing chapter from Deleuze and Guattari's Anti-Oedipus project: the most important political extension of May 1968 and one of the most important philosophical contributions of the twentieth century. (shrink)

The increasing precision of observations of the large-scale structure of the universe has created a problem for simulators: running the simulations necessary to interpret these observations has become impractical. Simulators have thus turned to machine learning (ML) algorithms instead. Though ML decreases computational expense, one might be worried about the use of ML for scientific investigations: How can algorithms that have repeatedly been described as black-boxes deliver scientific understanding? In this paper, I investigate how cosmologists employ ML, arguing that in (...) this context, ML algorithms should not be considered black-boxes and can deliver genuine scientific understanding. Accordingly, understanding the methodological role of ML algorithms is crucial to understanding the types of questions they are capable of, and ought to be responsible for, answering. (shrink)

As modern deep networks become more complex, and get closer to human‐like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present a diagnostic test dataset to examine the degree of abstract composable structure represented. Analyzing performance on these diagnostic tests indicates a lack of systematicity in representations (...) and decision rules, and reveals a set of heuristic strategies. We then investigate the effect of training distribution on learning these heuristic strategies, and we study changes in these representations with various augmentations to the training set. Our results reveal parallels to the analogous representations in people. We find that these systems can learn abstract rules and generalize them to new contexts under certain circumstances—similar to human zero‐shot reasoning. However, we also note some shortcomings in this generalization behavior—similar to human judgment errors like belief bias. Studying these parallels suggests new ways to understand psychological phenomena in humans as well as informs best strategies for building artificial intelligence with human‐like language understanding. (shrink)