Training artificial intelligence (AI) systems requires vast quantities of data, and AI developers face a variety of barriers to accessing the information they need. Synthetic data has captured researchers’ and industry’s imagination as a potential solution to this problem. While some of the enthusiasm for synthetic data may be warranted, in this short paper we offer critical counterweight to simplistic narratives that position synthetic data as a cost-free solution to every data-access challenge—provocations (...) highlighting ethical, political, and governance issues the use of synthetic data can create. We question the idea that synthetic data, by its nature, is exempt from privacy and related ethical concerns. We caution that framing synthetic data in binary opposition to “real” measurement data could subtly shift the normative standards to which data collectors and processors are held. And we argue that by promising to divorce data from its constituents—the people it represents and impacts—synthetic data could create new obstacles to democratic data governance. (shrink)

Researchers and practitioners are increasingly using machine‐generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while—potentially—mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue (...) of synthetic data is often understood to be its detachment from the data subjects whose measurement data is used to generate it. However, we argue that addressing the ethical issues synthetic data raises might require bringing data subjects back into the picture, finding ways that researchers and data subjects can be more meaningfully engaged in the construction and evaluation of datasets and in the creation of institutional safeguards that promote responsible use. (shrink)

In this paper we propose an non-machine learning artificial intelligence (AI) based approach for telecom data analysis, with a special focus on clique detection. Clique detection can be used to identify households, which is a major challenge in telecom data analysis and predictive analytics. Our approach does not use any form of machine learning, but another type of algorithm: satisfiability for propositional logic. This is a neglected approach in modern AI, and we aim to demonstrate that for certain (...) tasks, it may be a good alternative to machine learning-based approaches. We have used a simple DPLL satisfiability solver over an artificially generated telecom dataset (due to GDPR regulations), but our approach can be implemented on any telecom data by following the SAT encoding we have developed, and the DPLL solver can be substituted by a more advanced alternative such as CDCL. This paper extends the method presented in [1] for banking logs to data containing caller information, and proposes a more efficient encoding. -/- . (shrink)

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include (...) class='Hi'>data conversion, data correction, data interpolation, data scaling, data fusion, data assimilation, and synthetic data. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than the so-called raw data, and hence beneficially serve the epistemic aims of science. By illuminating the methods by which raw data are turned into scientifically useful data sets, this taxonomy provides a foundation for developing a more adequate philosophy of data. (shrink)

The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. Extensive simulations are conducted using synthetic and real-world datasets to evaluate the performance of the proposed algorithm. The results demonstrate that the optimized load balancing approach outperforms traditional algorithms in terms of SLA compliance, resource utilization, and energy efficiency. The findings suggest that the integration of optimization techniques into load balancing algorithms can significantly (...) enhance the operational efficiency of data centers, paving the way for future advancements in autonomous and self-optimizing data centers. (shrink)

The emergence of new techniques in both microbial biotechnology and artificial intelligence (AI) is opening up a completely new field for monitoring and sometimes even controlling the evolution of pathogens. However, the now famous generative AI extracts and reorganizes prior knowledge from large datasets, making it poorly suited to making predictions in an unreliable future. In contrast, an unfamiliar perspective can help us identify key issues related to the emergence of new technologies, such as those arising from synthetic biology, (...) whilst revisiting old views of AI or including generative AI as a generator of abduction as a resource. This could enable us to identify dangerous situations that are bound to emerge in the not-too-distant future, and prepare ourselves to anticipate when and where they will occur. Here, we emphasize the fact that amongst the many causes of pathogen outbreaks, often driven by the explosion of the human population, laboratory accidents are a major cause of epidemics. This review, limited to animal pathogens, concludes with a discussion of potential epidemic origins based on unusual organisms or associations of organisms that have rarely been highlighted or studied. (shrink)

Many philosophers take the distinguishing mark of their subject to be its a priori status. In their view, where empirical science is based on the data of experience, philosophy is founded on a priori intuitions. In this paper I shall argue that there is no good sense in which philosophical knowledge is informed by a priori intuitions. Philosophical results have just the same a posteriori status as scientific theories. My strategy will be to pose a familiar dilemma for the (...) friends of a priori philosophical intuitions. I shall ask them whether their intuitions are supposed to be analytic or synthetic. And then I shall argue that, if the intuitions are analytic, they may be a priori, but will be philosophically uninteresting; while, if they are synthetic, they may be philosophically interesting, but will not merit being treated as a priori in the context of philosophical debate. At the end of the paper I explain that this does not mean that philosophy should abandon ‘armchair’ methods. We still need to uncover and examine the often unrecognized intuitions that drive our thinking. However this is not because they provide a distinctive source of a priori knowledge, but on the contrary because they may be leading us astray. (shrink)

The pace of technological change continues to outstrip the evolution of democratic institutions, creating an urgent need for innovative approaches to democratic reform. However, the experimentation bottleneck - characterized by slow speed, high costs, limited scalability, and ethical risks - has long hindered progress in democracy research. This paper proposes a novel solution: employing generative artificial intelligence (GenAI) to create synthetic data through the simulation of digital homunculi, GenAI-powered entities designed to mimic human behavior in social contexts. By (...) enabling rapid, low-risk experimentation with alternative institutional designs, this approach could significantly accelerate democratic innovation. I examine the potential of GenAI-assisted research to mitigate current limitations in democratic experimentation, including the ability to simulate large-scale societal interactions and test complex institutional mechanisms. While acknowledging potential risks such as algorithmic bias, reproducibility challenges, and AI alignment issues, I argue that the benefits of synthetic data are likely to outweigh their drawbacks if implemented with proper caution. To address existing challenges, I propose a range of technical, methodological, and institutional adaptations. The paper concludes with a call for interdisciplinary collaboration in the development and implementation of GenAI-assisted methods in democracy research, highlighting their potential to bridge the gap between democratic theory and practice in an era of rapid technological change. (shrink)

Abstract: This research explores the application of artificial neural networks (ANNs) in predicting cancer using a synthetically generated dataset designed for research purposes. The dataset comprises 10,000 pseudo-patient records, each characterized by gender, age, smoking history, fatigue, and allergy status, along with a binary indicator for the presence or absence of cancer. The 'Gender,' 'Smoking,' 'Fatigue,' and 'Allergy' attributes are binary, while 'Age' spans a range from 18 to 100 years. The study employs a three-layer ANN architecture to develop a (...) predictive model. The achieved accuracy of 97.24% and a low loss value of 0.02 indicate promising performance. Further analysis involves a comprehensive examination of the confusion matrix, precision, recall, and F1 score metrics, along with the ROC curve and AUC score, to provide a detailed understanding of the model's strengths and weaknesses. Additionally, considerations for cross-validation, hyperparameter tuning, and interpretability are discussed. It is emphasized that while the synthetic nature of the dataset allows for controlled experimentation, results should be cautiously interpreted and validated on authentic clinical data before extrapolation to real-world medical scenarios. This research serves as a valuable contribution to the exploration of predictive models for cancer detection, encouraging continued investigation and development in the field. (shrink)

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of (...) nodes. This results in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality. (shrink)

At least since W. V. O. Quine's famous critique of the analytic/synthetic distinction, philosophers have been deeply divided over whether there are any analytic truths. One line of thought suggests that the simple fact that people have ' intuitions of analyticity' might provide an independent argument for analyticities. If defenders of analyticity can explain these intuitions and opponents cannot, then perhaps there are analyticities after all. We argue that opponents of analyticity have some unexpected resources for explaining these intuitions (...) and that, accordingly, the argument from intuition fails. (shrink)

In this article, I shall argue that AI’s likely developments and possible challenges are best understood if we interpret AI not as a marriage between some biological-like intelligence and engineered artefacts, but as a divorce between agency and intelligence, that is, the ability to solve problems successfully and the necessity of being intelligent in doing so. I shall then look at five developments: (1) the growing shift from logic to statistics, (2) the progressive adaptation of the environment to AI rather (...) than of AI to the environment, (3) the increasing translation of difficult problems into complex problems, (4) the tension between regulative and constitutive rules underpinning areas of AI application, and (5) the push for synthetic data. (shrink)

Often associated with themes in political philosophy and aesthetics, the work of Jean-François Lyotard is most known for his infamous definition of the postmodern in his best-known book, La condition postmoderne, as incredulity towards metanarratives. The claim of this article is that this famous claim of Lyotard is actually embedded in a philosophy of technology, one that is, moreover, still relevant for understanding present technoscience. The first part of the article therefore sketches Lyotard’s philosophy of technology, mainly by correcting three (...) common misconceptions: that La condition postmoderne would only be about metanarratives, whereas in fact, it is mainly about what replaces them, namely performativity; that performativity would be shorthand for capitalism, whereas in reality, capitalism is the latest instance of a longer history of performativity; and that Lyotard’s reflections on science and technology would be restricted to this book alone, whereas in reality, a well-articulated philosophy of technology, centered around the concept of technoscience, is found in his later work. The second part of the article then aims to highlight the contemporary relevance of this philosophy of technoscience through a brief examination of two contemporary technosciences: synthetic biology and data science. (shrink)

Generative AI is reshaping sectors like banking and healthcare by enabling innovative applications such as personalized service offerings, predictive analytics, and automated content generation. In banking, generative AI drives customer engagement through tailored financial advice, fraud detection, and streamlined customer service. Meanwhile, in healthcare, it enhances medical imaging analysis, drug discovery, and patient diagnostics, significantly impacting patient care and operational efficiency. This paper presents an experimental study examining the implementation and effectiveness of generative AI in these sectors.

The epistemic position of an agent often depends on their position in a larger network of other agents who provide them with information. In general, agents are better off if they have diverse and independent sources. Sullivan et al. [19] developed a method for quantitatively characterizing the epistemic position of individuals in a network that takes into account both diversity and independence; and presented a proof-of-concept, closed-source implementation on a small graph derived from Twitter data [19]. This paper reports (...) on an open-source reimplementation of their algorithm in Python, optimized to be usable on much larger networks. In addition to the algorithm and package, we also show the ability to scale up our package to large synthetic social network graph profiling, and finally demonstrate its utility in analyzing real-world empirical evidence of ‘echo chambers’ on online social media, as well as evidence of interdisciplinary diversity in an academic communications network. (shrink)

In the United States, the Presidential Commission for the Study of Bioethical Issues has proposed deliberative democracy as an approach for dealing with ethical issues surrounding synthetic biology. Deliberative democracy might similarly help us as we update the regulation of human subjects research. This paper considers how the values that deliberative democratic engagement aims to realize can be realized in a human subjects research context. Deliberative democracy is characterized by an ongoing exchange of ideas between participants, and an effort (...) to justify decisions that bind participants by appeal to reasons that the participants can understand and share. Even when unanimous agreement is not reached, the active participation of everyone along with the requirement that reasons be made accessible enhances the legitimacy of the ultimate outcome. Importantly, deliberative democratic structures avoid strict hierarchies and place participants, as much as possible, in the position of equals. Human subjects research has some features that may make deliberative democratic principles seem initially unappealing. For one, there are asymmetries in knowledge between expert researchers conducting the research and participants in the research process. For another, statistical validity is made easiest by research paradigms that produce standardized, quantitative data, which can be difficult to achieve if research participants are given the power to deliberatively reshape the research design as it progresses. These and other problems have tended to produce a human subjects research process where subjects do not actively participate in shaping research, but rather consent to a predefined set of interventions designed by expert researchers and vetted by Institutional Review Boards (IRBs). In this paper, I suggest some ways in which human subjects research could do more to realize deliberative democratic values, and, in particular, how a revised Common Rule might help to realize these values. First, research participants could be treated not as passive subjects but instead involved in research design, ethical review, and the ongoing conduct and dissemination of research. Such participation might involve, for instance, including people who have served as research subjects on IRBs, or replacing IRB oversight for certain forms of research exempted from IRB oversight under a revised Common Rule with oversight by a body of community members or research subjects. It might also involve having the oversight of research that uses more participatory models be more participatory and less hierarchical in nature. I also raise questions about the exemption of research on public benefit programs from any research-level oversight and from consent requirements. While IRBs are likely not the correct overseers, there may be good reason to view such research with a critical eye, because of its potential for long-range impacts on the lives of participants. By giving research subjects a greater voice in research that aims at fine-tuning public benefit programs on which subjects rely, a deliberative oversight process has the potential to recast research participation as a form of active democratic participation and to address a “democratic deficit” in public health. Numerous proposals regarding health care have called for greater participation by laypeople and a more nonhierarchical approach to setting health priorities. Involving lay research subjects in the conduct of public benefits research and other forms of public health research may help to further these goals. (shrink)

In this review, we describe some of the central philosophical issues facing origins-of-life research and provide a targeted history of the developments that have led to the multidisciplinary field of origins-of-life studies. We outline these issues and developments to guide researchers and students from all fields. With respect to philosophy, we provide brief summaries of debates with respect to (1) definitions (or theories) of life, what life is and how research should be conducted in the absence of an accepted theory (...) of life, (2) the distinctions between synthetic, historical, and universal projects in origins-of-life studies, issues with strategies for inferring the origins of life, such as (3) the nature of the first living entities (the “bottom up” approach) and (4) how to infer the nature of the last universal common ancestor (the “top down” approach), and (5) the status of origins of life as a science. Each of these debates influences the others. Although there are clusters of researchers that agree on some answers to these issues, each of these debates is still open. With respect to history, we outline several independent paths that have led to some of the approaches now prevalent in origins-of-life studies. These include one path from early views of life through the scientific revolutions brought about by Linnaeus (von Linn.), Wöhler, Miller, and others. In this approach, new theories, tools, and evidence guide new thoughts about the nature of life and its origin.We also describe another family of paths motivated by a” circularity” approach to life, which is guided by such thinkers as Maturana & Varela, Gánti, Rosen, and others. These views echo ideas developed by Kant and Aristotle, though they do so using modern science in ways that produce exciting avenues of investigation. By exploring the history of these ideas, we can see how many of the issues that currently interest us have been guided by the contexts in which the ideas were developed. The disciplinary backgrounds of each of these scholars has influenced the questions they sought to answer, the experiments they envisioned, and the kinds of data they collected. We conclude by encouraging scientists and scholars in the humanities and social sciences to explore ways in which they can interact to provide a deeper understanding of the conceptual assumptions, structure, and history of origins-of-life research. This may be useful to help frame future research agendas and bring awareness to the multifaceted issues facing this challenging scientific question. (shrink)

Based on the analysis of the accumulated experimental data and on the informational concepts of the Informational Model of Consciousness (IMC), in this article is presented an informational modeling of the operability of the posterior cingulate cortex (PCC). Examination of the experimental results obtained with the modern non-destructive, high spatial resolution investigation tools to study the functional characteristics of the PCC and associate metabolic processes, shows mainly that this is involved in the large scale default mode network (DMN), composed (...) primarily by PCC, medial prefrontal cortex (MPFC), and the inferior parietal lobe (IPL), displaying an increased activity under passive task conditions (i.e. negative mode). This operability mode is in an opposing balance with attention and task performances mode, acting thus as a disruption process. One of the main conclusions is that the PCC cognitive orientation is primarily focused on the self person or on the “self” (I) projected from others, in “daydreaming” or “mind-wandering" wakeful rest, involved in present or future virtual projects. In spite of these advances, the particular role and specific functions of PCC are not yet fully understood, PCC remaining an enigmatic/contradictory structure of the brain, not included up to date in any brain model, so the PCC informational modeling presented here in terms of IMC covers this lack. IMC assigns to the brain the fundamental role of informational processor, composed by various operating structures according to specific objectives synthetically expressed by seven main cognitive centers of “self" (I) as: Iknow (memory), Iwant (decision), Ilove (emotions), Iam (self-status), Icreate (genetic transmission), Icreated (genetic inheritance), Ibelive (info-selection), assuring the info-connectivity with the body and external/internal world, the adaptability (learning process) and survival. It is deduced in this way that the specific behavior of PCC according to its intermediary architectural position between the vital central region of the brain, connecting the mind with the external/internal reality, is that of a moderator/integrator hub/informational YES/NO switcher from the external/awake captured focusing information to the internal accumulated experience of life, used as a valid reference. Therefore, the specific operability of PCC in terms of cognitive centers is that of an informational switcher, mainly operating with Iknow/Iwant to explore the self-status reflected in Iam, within day-dreaming/imaginary virtual sceneries. (shrink)

(Introduction) The question of heterogeneity does not appear at first glance to be a genuinely phenomenological problem and not even a problem in general. It seems to go without saying that there is “coupling” (Paarung), association, fusion, synthesis or in general any form connection between different data of consciousness, all as it seems obvious (at least from Husserl) that there must be objectities so that we can talk about knowledge and truth. After Kant we got so used to (...) class='Hi'>synthetic formations that we hardly think of their possibilities in principle and the problems that these are called upon to resolve. Phenomenology does nothing else but describing the synthetic process, starting with its most elementary (pre-reflective) levels, aiming thus to completely elucidate the constitution of the objectity and, by that, the functioning and the rights of reason. Now, such elucidation does not only involve an approach aimed at the how, but also, the why of this process. In other words, our attention and our theoretical interest must be directed not only towards the course of the constitutive synthesis, but also on what motivates her intrinsically. Questions like "why should he be there in general an objectity? " or "is it principally possible to associate in one form or another data belonging to to the same class or to different classes? ”, questions which are de jure and which therefore refer rather to the legality of the constitution than its more or less contingent functioning, cannot in any way be excluded from the scope of the phenomenological questioning. As much as the epistemological ideal of phenomenological description required above all that it avoid any form of abstraction and idealization, this class of questions is, if not excluded, at least left in shadow. (shrink)

This chapter challenges previous interpretations of the essay by analyzing the essential features of the method it describes. It argues that, even if Kant fails to present a perfectly clear and defensible account of a method for metaphysics, the manner in which he describes this method and the kind of transformation of metaphysical inquiry that he hopes it will accomplish do not testify to a broadly rationalist approach but, in fact, embody the anti-rationalist spirit of Bacon and Newton. While Kant (...) does indeed believe that metaphysics will one day be able to re-adopt the synthetic method, I argue that this is perfectly consistent with the views of Bacon and Newton as he understood them. In contrast with Friedman’s claim, I also show that experience does play a role in Kant’s conception of data and that this is particularly clear if we take into account other texts of the same period. (shrink)

Background Previous analyses of democracy and population health have focused on broad measures, such as life expectancy at birth and child and infant mortality, and have shown some contradictory results. We used a panel of data spanning 170 countries to assess the association between democracy and cause-specific mortality and explore the pathways connecting democratic rule to health gains. -/- Methods We extracted cause-specific mortality and HIV-free life expectancy estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study (...) 2016 and information on regime type from the Varieties of Democracy project. These data cover 170 countries and 46 years. From the Financing Global Health database, we extracted gross domestic product (GDP) per capita, also covering 46 years, and Development Assistance for Health estimates starting from 1990 and domestic health spending estimates starting from 1995. We used a diverse set of empirical methods—synthetic control, within-country variance decomposition, structural equation models, and fixed-effects regression—which together provide a robust analysis of the association between democratisation and population health. -/- Findings HIV-free life expectancy at age 15 years improved significantly during the study period (1970–2015) in countries after they transitioned to democracy, on average by 3% after 10 years. Democratic experience explains 22·27% of the variance in mortality within a country from cardiovascular diseases, 16·53% for tuberculosis, and 17·78% for transport injuries, and a smaller percentage for other diseases included in the study. For cardiovascular diseases, transport injuries, cancers, cirrhosis, and other non-communicable diseases, democratic experience explains more of the variation in mortality than GDP. Over the past 20 years, the average country's increase in democratic experience had direct and indirect effects on reducing mortality from cardiovascular disease (−9·64%, 95% CI −6·38 to −12·90), other non-communicable diseases (−9·14%, −4·26 to −14·02), and tuberculosis (−8·93%, −2·08 to −15·77). Increases in a country's democratic experience were not correlated with GDP per capita between 1995 and 2015 (ρ=–0·1036; p=0·1826), but were correlated with declines in mortality from cardiovascular disease (ρ=–0·3873; p<0·0001) and increases in government health spending (ρ=0·4002; p<0·0001). Removal of free and fair elections from the democratic experience variable resulted in loss of association with age-standardised mortality from non-communicable diseases and injuries. -/- Interpretation When enforced by free and fair elections, democracies are more likely than autocracies to lead to health gains for causes of mortality (eg, cardiovascular diseases and transport injuries) that have not been heavily targeted by foreign aid and require health-care delivery infrastructure. International health agencies and donors might increasingly need to consider the implications of regime type in their efforts to maximise health gains, particularly in the context of ageing populations and the growing burden of non-communicable diseases. (shrink)

The paper follows an ontological approach in analyzing sexual experience. Sexual experience is defined as: (i) an experience in action. Correspondingly, its individuals are of two different types: (a) sense-data and (b) gestures. (ii) It is a kind of knowledge—a typical synthetic a posteriori knowledge (a virgin cannot know what sexual experience could be). (iii) It is a kind of anti-realist knowledge—its objects are constructed in the process of knowing. (iv) Sexual action proceeds in judgments that are micro-decisions (...) of how to proceed further. (v) The objects of sexual experience are seen in a specific perspective. This makes their meaning different from the meaning of physical objects. (shrink)

Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of CHD events (...) from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), K- Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance. (shrink)

This research project utilized advanced computer algorithms to predict the outcomes of Premier League soccer matches. The dataset containing match data and odds from seasons was processed to handle missing information, select features and reduce complexity using Principal Component Analysis. To address imbalances, in the target variable Synthetic Minority Over sampling Technique (SMOTE) was employed. Various machine learning models such as RandomForest, DecisionTree, SVM, XGBoost and LightGBM were evaluated.

In pedestrian detection, occlusions are typically treated as an unstructured source of noise and explicit models have lagged behind those for object appearance, which will result in degradation of detection performance. In this paper, a hierarchical co-occurrence model is proposed to enhance the semantic representation of a pedestrian. In our proposed hierarchical model, a latent SVM structure is employed to model the spatial co-occurrence relations among the parent–child pairs of nodes as hidden variables for handling the partial occlusions. Moreover, the (...) visibility statuses of the pedestrian can be generated by learning co-occurrence relations from the positive training data with large numbers of synthetically occluded instances. Finally, based on the proposed hierarchical co-occurrence model, a pedestrian detection algorithm is implemented to incorporate visibility statuses by means of a Random Forest ensemble. The experimental results on three public datasets demonstrate the log-average miss rate of the proposed algorithm has 5% improvement for pedestrians with partial occlusions compared with the state-of-the-arts. (shrink)

In this study, we extracted facial action units (AUs) data during a Hearthstone tournament to investigate behavioural differences between expert, intermediate, and novice players. Our aim was to obtain insights into the nature of expertise and how it may be tracked using non-invasive methods such as AUs. These insights may shed light on the endogenous responses in the player and at the same time may provide information to the opponents during a competition. Our results show that player expertise may (...) be characterised by specific patterns in facial expressions. More specifically, AU17 (chin raiser), AU25 (lips apart), and AU26 (jaws drop) intensity responses during gameplay may vary according to players' expertise. Such results were obtained by training a random forest classifier to test whether we can use these three AUs alone to accurately detect players' expertise. The classifier reached 0.75 accuracy on 5-fold cross-validation, after balancing the class weights, and 0.85 after having applied the Synthetic Minority Over-sampling Technique (SMOTE) function. These results suggest that AUs can be effectively used to discriminate different levels of expertise in competitive video game players. (shrink)

Philosophers and scientists have often adopted the orthodox version of calibration which involves standardizing an instrument using a known phenomenon. The essential link between theoretical concepts and empirical data, in the philosophy of measurement, has generated the formulation of principles of coordination, synthetic a priori, and revisables. Operationalist thinking wanted to limit the scope of concepts to operations of measurement that are actually achievable. The coherentist perspective in the philosophy of measurement has operated a recovery of coordinationist epistemology (...) and operationalism, relying on a minimal number of ontological principles. Models of an instrument involve a commitment to separation between idealized theories and material things. However, philosophers and metrologists have advocated the requirement of a rich theoretical content in the modeling of measuring instruments. According to other contributions, the epistemic privilege of measurement precedes a background theory and its robustness lies in the free contact with empirical data. Moreover, the applicability regime of a theory dictates its boundary conditions, which guide the experimenter in the design of measuring instruments and provide the basis for operationalizing the meaning of theoretical terms. I support operational pluralism, measurement operations involving different physical indicators, accompanied by dynamic coherentism. The James Webb Space Telescope calibration program is a significant case. Observations of calibration stars by various methods are used to calculate the factors that convert a measurement in instrumental units to physical units. (shrink)

Should we do speculative cognitive science? - In present day philosophy, I see a fashion that uses empirical facts (data) to support positions that are not philosophical but empirical in nature. The argumentative structure is classical philosophy, saying that ‘this has to be that way because …’ where the ‘this’ refers to some empirical state of affairs. This kind of philosophy speculates about empirical facts in areas where we do not yet know the facts – the arguments are a (...) priori, supported by a posteriori data. This is precisely what the speculative philosophy of German Idealism was doing, e.g. in the works of Schelling or Hegel. (shrink)

Fixing the problem won’t be easy, but humans’ sharpened focus on an emerging philosophy of data might give us some clue about where we will be heading for.

Biologists are nearing the creation of the first fully synthetic eukaryotic genome. Does this mean that we still soon be able to create genomes that are parts of an existing genetic lineage? If so, it might be possible to bring back extinct species. But do genomes that are synthetically assembled, no matter how similar they are to native genomes, really belong to the genetic lineage on which they were modelled? This article will argue that they are situated within the (...) same genetic lineage. To see why requires closely examining whether material overlap between parents and offspring is a necessary feature of biological reproduction. The processes used to create synthetic genomes shows that these processes are a form of scaffolded reproduction because they use external machinery and take ownership of the material parts used to create synthetic genomes. Closely examining these processes also reveals, surprisingly, that ‘synthetic reproduction’ can take place between entities that don’t participate in the same biological lineages. 1Introduction2The Argument for Lineage-less Genomes3Synthetic Eukaryotic Chromosomes and Material Overlap4Biological Reproduction, Material, and Information5Synthetic Reproductive Processes and Their Implications. (shrink)

The guiding thread of the paper is the diagnosis that the advanced division of cognitive labor (that is, intellectual specialization) engenders a set of perennial, political and epistemic challenges (Millgram 2015) that, simultaneously, also generate opportunities for philosophy. In this paper, I re-characterize the nature of synthetic philosophy as a means to advance and institutionalize philosophy. For my definition of synthetic philosophy see section 2. In section 1, I treat Plato’s Republic as offering two models to represent philosophy's (...) relationship to the other sciences within the advanced division of labor. I highlight that for Plato intellectual specialization is central not just to economic, but also to political life. And, yet, that the very dispersion of scientific expertise, and its esoteric nature, also generates non-trivial challenges to the recognition and political utilization of knowledge. From Plato we can infer that in imperfect circumstances, philosophy’s self-constitution is, in part, a response to these challenges in political epistemology. However, how philosophy is institutionalized differs through time. In section 2, I re-introduce my conceptualization of synthetic philosophy and restate it. This makes visible that synthetic philosophy is already widely practiced in the profession as philosophy of the special sciences, PPE, formal modelling, public philosophy, etc. I use recent work by Dorst (2023) to illustrate synthetic philosophy and to identify some of the processes that give rise to the need for it. In sections 2-3, I contrast my account with the evolving ways that Philip Kitcher has conceptualized synthetic philosophy in order to make more precise the version promoted here. I do so not just because Kitcher and I use the same term, ‘synthetic philosophy,’ but because the temptations inherent in Kitcher’s approach should be resisted. -/- . (shrink)

The recent publications reported in 2022 reveal the possibility of obtaining mouse embryos without the need for egg or sperm. These ‘artificial embryos’ can recapitulate some stages of development ex utero – from neurulation to organogenesis – without implantation. Synthetic mouse embryos might serve as a valuable model to gain further insights into early developmental stages. Indeed, it is expected for these models to be replicated by employing human cells. This promising research raises ethical issues and expands the horizon (...) of ethics in regard to the development of the human embryo. From this point of view, we state some of the new open venues for bioethical research. (shrink)

If humans eventually attain the ability to create new life forms, how will it affect the value of life? This is one of several questions that can be sources of concern when discussing synthetic life, but is the concern justified? In an attempt to answer this question, I have analyzed some possible reasons why an ability to create synthetic life would threaten the value of life in general (that is, not just of the synthetic creations), to see (...) if they really give us reason to worry. The main conclusion is that it is unlikely that a future human ability to create life will really have a great negative impact on these characteristics of life. It therefore seems unlikely that the value of life will be negatively affected by the ability to create synthetic life, though it is possible that the properties in question will be less salient in the synthetic life and thus that the value of the synthetic life will be lower than that of existing life, which in turn can lead to a disturbing difference in value between different kinds of life. (shrink)

This text answers the question, posed by the editor, on the philosophical and social issues resulting from the synthetic assembly of a modified bacterial genome that was introduced in an existing bacterial species (M.mycoides)and so it was claimed to represent the first ever kind of synthetic life produced by human manipulation.

The recent discussion of fictional models has focused on imagination, implicitly considering fictions as something nonconcrete. We present two cases from synthetic biology that can be viewed as concrete fictions. Both minimal cells and alternative genetic systems are modal in nature: they, as well as their abstract cousins, can be used to study unactualized possibilia. We approach these synthetic constructs through Vaihinger’s notion of a semi-fiction and Goodman’s notion of semifactuality. Our study highlights the relative existence of such (...) concrete fictions. Before their realizations neither minimal cells nor alternative genetic systems were any well-defined objects, and the subsequent experimental work has given more content to these originally schematic imaginings. But it is as yet unclear whether individual members of these heterogeneous groups of somewhat functional synthetic constructs will eventually turn out to be fully realizable, remain only partially realizable, or prove outright impossible. (shrink)

Probably the most distinctive feature of synthetic biology is its being “synthetic” in some sense or another. For some, synthesis plays a unique role in the production of knowledge that is most distinct from that played by analysis: it is claimed to deliver knowledge that would otherwise not be attained. In this contribution, my aim is to explore how synthetic biology delivers knowledge via synthesis, and to assess the extent to which this knowledge is distinctly synthetic. (...) On the basis of distinctions between knowledge-how and knowledge-why, and between syntheses that succeed and syntheses that fail, I argue that the contribution of synthesis to knowledge is best understood when syntheses are construed as experimental interventions that aim at probing causal relationships between properties of the entities that are combined through these syntheses and properties of their target products. The distinctiveness of synthetic biology in its quest for knowledge through synthesis stems from its ability to sample at will a space of empirical possibilities that is not only huge but also that has been so scarcely sampled by nature. (shrink)

Synthetic biologists aim to generate biological organisms according to rational design principles. Their work may have many beneficial applications, but it also raises potentially serious ethical concerns. In this article, we consider what attention the discipline demands from bioethicists. We argue that the most important issue for ethicists to examine is the risk that knowledge from synthetic biology will be misused, for example, in biological terrorism or warfare. To adequately address this concern, bioethics will need to broaden its (...) scope, contemplating not just the means by which scientific knowledge is produced, but also what kinds of knowledge should be sought and disseminated. (shrink)

Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis’s (2012) statement that, “the scientific process demands the highest standards of quality, ethics and rigour,” they are worth implementing. More importantly, they are feasible to work (...) on and likely will help to restore plausibility to social sciences research. Therefore, I feel it likely that the triplet of open data, open review and open dialogue will gradually emerge to become policy requirements regardless of the research funding source. (shrink)

Deepfakes and other forms of synthetic media are widely regarded as serious threats to our knowledge of the world. Various technological responses to these threats have been proposed. The reactive approach proposes to use artificial intelligence to identify synthetic media. The proactive approach proposes to use blockchain and related technologies to create immutable records of verified media content. I argue that both approaches, but especially the reactive approach, are vulnerable to a problem analogous to the ancient problem of (...) the criterion—a line of argument with skeptical implications. I argue that, while the proactive approach is relatively resistant to this objection, it faces its own serious challenges. In short, the proactive approach would place a heavy burden on users to verify their own content, a burden that is exacerbated by and is likely to exacerbate existing inequalities. (shrink)

It is a most commonly accepted hypothesis that life originated from inanimate matter, somehow being a synthetic product of organic aggregates, and as such, a result of some sort of prebiotic synthetic biology. In the past decades, the newly formed scientific discipline of synthetic biology has set ambitious goals by pursuing the complete design and production of genetic circuits, entire genomes or even whole organisms. In this paper, I argue that synthetic biology might also shed some (...) novel and interesting perspectives on the question of the origin of life, and that, in addition, it might challenge our most commonly accepted definitions of life, thereby changing the ways we might think about life and its origin. (shrink)

Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to nonhuman domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with nonhumans may not make us less human, but they promise to challenge fundamental views of what it is to be human.

This theme issue has the founding ambition of landscaping Data Ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing, and use), algorithms (including AI, artificial agents, machine learning, and robots), and corresponding practices (including responsible innovation, programming, hacking, and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data Ethics builds on the foundation provided by (...) Computer and Information Ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the Level of Abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even the data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations — the interactions among hardware, software, and data — rather than on the variety of digital technologies that enables them. And it emphasises the complexity of the ethical challenges posed by Data Science. Because of such complexity, Data Ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of Data Science and its applications within a consistent, holistic, and inclusive framework. Only as a macroethics Data Ethics will provide the solutions that can maximise the value of Data Science for our societies, for all of us, and for our environments. (shrink)

We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides (...) insight into the common but little-discussed practices of iteratively reusing and repurposing data, which result in many datasets’ having a phylogeny—an origin and complex evolutionary history—that is relevant to their evaluation and future use. We relate these insights to the open-data and data-rescue movements, and highlight several future avenues of research that build on the PR view of data. (shrink)

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their functionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adaptations, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, the (...) analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an opportunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would suggest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to implement novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engineering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engineering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. (shrink)

Here we will see that contrary to the party line, Hume’s notion of a relation should be understood, in all cases, as a peculiar non-necessary synthetic relation; unique, but similar in a certain constructive sense to what I characterize as a mathematical notion of synthesis. And, most controversially, I argue that this non-necessary synthetic notion of a relation includes Hume’s arithmetical relations, which have typically been interpreted as either “analytic,” necessary, or both. In this general respect, Hume anticipates (...) Quine’s attack on the analytic/synthetic distinction. (shrink)

Recently, Bechtel and Abrahamsen have argued that mathematical models study the dynamics of mechanisms by recomposing the components and their operations into an appropriately organized system. We will study this claim through the practice of combinational modeling in circadian clock research. In combinational modeling, experiments on model organisms and mathematical/computational models are combined with a new type of model—a synthetic model. We argue that the strategy of recomposition is more complicated than what Bechtel and Abrahamsen indicate. Moreover, synthetic (...) modeling as a kind of material recomposition strategy also points beyond the mechanistic paradigm. (shrink)

Synthetic biology is a field of research that concentrates on the design, construction, and modification of new biomolecular parts and metabolic pathways using engineering techniques and computational models. By employing knowledge of operational pathways from engineering and mathematics such as circuits, oscillators, and digital logic gates, it uses these to understand, model, rewire, and reprogram biological networks and modules. Standard biological parts with known functions are catalogued in a number of registries (e.g. Massachusetts Institute of Technology Registry of Standard (...) Biological Parts). Biological parts can then be selected from the catalogue and assembled in a variety of combinations to construct a system or pathway in a microbe. Through the innovative re-engineering of biological circuits and the optimization of certain metabolic pathways, biological modules can be designed to reprogram organisms to produce products or behaviors. Synthetic biology is what is known as a “platform technology”. That is, it generates highly transferrable theoretical models, engineering principles, and know-how that can be applied to create potential products in a wide variety of industries. Proponents suggest that applications of synthetic biology may be able to provide scientific and engineered solutions to a multitude of worldwide problems from health to energy. Synthetic biology research has already been successful in constructing microbial products which promise to offer cheaper pharmaceuticals such as the antimalarial synthetic drug artemisinin, engineered microbes capable of cleaning up oil spills, and the engineering of biosensors that can detect the presence of high concentrations of arsenic in drinking water. One of the potential benefits of synthetic biology research is in its application to biofuel production. It is this application which is the focus of this entry. The term “biofuel” has referred generally to all liquid fuels that are sourced from plant or plant byproducts and are used for energy necessary for transportation vehicles (Thompson 2012). Biofuels that are produced using synthetic biological techniques re-engineer microbes into biofuel factories are a subset of these. (shrink)

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood (...) class='Hi'>data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)

The concept of truth has many aims but only one source. The article describes the primary concept of truth, here called the synthetic concept of truth, according to which truth is the objective result of the synthesis of us and nature in the process of rational cognition. It is shown how various aspects of the concept of truth -- logical, scientific, and mathematical aspect -- arise from the synthetic concept of truth. Also, it is shown how the paradoxes (...) of truth arise. (shrink)