555 research outputs found

    Subjectivity, nature, existence: Foundational issues for enactive phenomenology

    Get PDF
    This thesis explores and discusses foundational issues concerning the relationship between phenomenological philosophy and the enactive approach to cognitive science, with the aim of clarifying, developing, and promoting the project of enactive phenomenology. This project is framed by three general ideas: 1) that the sciences of mind need a phenomenological grounding, 2) that the enactive approach is the currently most promising attempt to provide mind science with such a grounding, and 3) that this attempt involves both a naturalization of phenomenology and a phenomenologization of the concept of nature. More specifically, enactive phenomenology is the project of pursuing mutually illuminative exchanges between, on the one hand, phenomenological investigations of the structures of lived experience and embodied existence and, on the other, scientific accounts of mind and life – in particular those framed by theories of biological self-organization. The thesis consists of two parts. Part one is an introductory essay that seeks to clarify some of enactive phenomenology’s overarching philosophical commitments by tracing some of its historical roots. Part two is a compilation of four articles, each of which intervenes in a different contemporary debate relevant to the dissertation’s project

    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)

    Get PDF
    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!

    Natural Language Reasoning on ALC knowledge bases using Large Language Models

    Get PDF
    Τα προεκπαιδευμένα γλωσσικά μοντέλα έχουν κυριαρχήσει στην επεξεργασία φυσικής γλώσσας, αποτελώντας πρόκληση για τη χρήση γλωσσών αναπαράστασης γνώσης για την περιγραφή του κόσμου. Ενώ οι γλώσσες αυτές δεν είναι αρκετά εκφραστικές για να καλύψουν πλήρως τη φυσική γλώσσα, τα γλωσσικά μοντέλα έχουν ήδη δείξει σπουδαία αποτελέσματα όσον αφορά την κατανόηση και την ανάκτηση πληροφοριών απευθείας σε δεδομένα φυσικής γλώσσας. Διερευνούμε τις επιδόσεις των γλωσσικών μοντέλων για συλλογιστική φυσικής γλώσσας στη περιγραφική λογική ALC. Δημιουργούμε ένα σύνολο δεδομένων από τυχαίες βάσεις γνώσης ALC, μεταφρασμένες σε φυσική γλώσσα, ώστε να αξιολογήσουμε την ικανότητα των γλωσσικών μοντέλων να λειτουργούν ως συστήματα απάντησης ερωτήσεων πάνω σε βάσεις γνώσης φυσικής γλώσσας.Pretrained language models have dominated natural language processing, challenging the use of knowledge representation languages to describe the world. While these lan- guages are not expressive enough to fully cover natural language, language models have already shown great results in terms of understanding and information retrieval directly on natural language data. We explore language models’ performance at the downstream task of natural language reasoning in the description logic ALC. We generate a dataset of random ALC knowledge bases, translated in natural language, in order to assess the language models’ ability to function as question-answering systems over natural language knowledge bases

    Vitalism and Its Legacy in Twentieth Century Life Sciences and Philosophy

    Get PDF
    This Open Access book combines philosophical and historical analysis of various forms of alternatives to mechanism and mechanistic explanation, focusing on the 19th century to the present. It addresses vitalism, organicism and responses to materialism and its relevance to current biological science. In doing so, it promotes dialogue and discussion about the historical and philosophical importance of vitalism and other non-mechanistic conceptions of life. It points towards the integration of genomic science into the broader history of biology. It details a broad engagement with a variety of nineteenth, twentieth and twenty-first century vitalisms and conceptions of life. In addition, it discusses important threads in the history of concepts in the United States and Europe, including charting new reception histories in eastern and south-eastern Europe. While vitalism, organicism and similar epistemologies are often the concern of specialists in the history and philosophy of biology and of historians of ideas, the range of the contributions as well as the geographical and temporal scope of the volume allows for it to appeal to the historian of science and the historian of biology generally

    A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

    Full text link
    Large language models (LLMs) are a special class of pretrained language models obtained by scaling model size, pretraining corpus and computation. LLMs, because of their large size and pretraining on large volumes of text data, exhibit special abilities which allow them to achieve remarkable performances without any task-specific training in many of the natural language processing tasks. The era of LLMs started with OpenAI GPT-3 model, and the popularity of LLMs is increasing exponentially after the introduction of models like ChatGPT and GPT4. We refer to GPT-3 and its successor OpenAI models, including ChatGPT and GPT4, as GPT-3 family large language models (GLLMs). With the ever-rising popularity of GLLMs, especially in the research community, there is a strong need for a comprehensive survey which summarizes the recent research progress in multiple dimensions and can guide the research community with insightful future research directions. We start the survey paper with foundation concepts like transformers, transfer learning, self-supervised learning, pretrained language models and large language models. We then present a brief overview of GLLMs and discuss the performances of GLLMs in various downstream tasks, specific domains and multiple languages. We also discuss the data labelling and data augmentation abilities of GLLMs, the robustness of GLLMs, the effectiveness of GLLMs as evaluators, and finally, conclude with multiple insightful future research directions. To summarize, this comprehensive survey paper will serve as a good resource for both academic and industry people to stay updated with the latest research related to GPT-3 family large language models.Comment: Preprint under review, 58 page

    Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

    Get PDF
    This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1011198) , (Institute for Information & communications Technology Planning & Evaluation) (IITP) grant funded by the Korea government (MSIT) under the ICT Creative Consilience Program (IITP-2021-2020-0-01821) , and AI Platform to Fully Adapt and Reflect Privacy-Policy Changes (No. 2022-0-00688).Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI mode ľs decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data.National Research Foundation of Korea Ministry of Science, ICT & Future Planning, Republic of Korea Ministry of Science & ICT (MSIT), Republic of Korea 2021R1A2C1011198Institute for Information amp; communications Technology Planning amp; Evaluation) (IITP) - Korea government (MSIT) under the ICT Creative Consilience Program IITP-2021-2020-0-01821AI Platform to Fully Adapt and Reflect Privacy-Policy Changes2022-0-0068

    Knowledge graph embedding enhancement using ontological knowledge in the biomedical domain

    Get PDF
    The biomedical field is a critical area for natural language processing (NLP) applications because it involves a vast amount of unstructured data, including clinical notes, medical publications, and electronic health records. NLP techniques can help extract valuable information from these documents, such as disease symptoms, medication usage, and treatment outcomes, which can improve patient care and clinical decision-making. MAPS S.p.A. currently produces Clinika, a software that extracts knowledge from clinical corpora. Clinika performs the task of Named Entity Recognition (NER) by linking entities to medical concepts from an established knowledge base, in this case, the Unified Medical Language System (UMLS). This dissertation details how we approached designing and implementing a component for the new version of Clinika, specifically a mention embedder that uses embeddings to perform entity linking with UMLS concepts. We focused on enhancing existing dense contextual embeddings by injecting ontological knowledge, using two parallel approaches: (1) taking the embeddings as a by-product of an entity alignment model aided by an ontology, and (2) fine-tuning a contextual language model with custom sampling strategies. We evaluated both approaches with suitable experiments from the relevant literature. After testing, we discontinued the first approach but found more significant results using the second. The results on the tasks chosen to evaluate the embeddings were not promising, we address the causes in the Error Analysis section, and discuss further work on this topic

    Silence that matters: HIV Nondisclosure and the limits of Consent

    Get PDF
    This dissertation explores the legal and sociocultural linguistic implications of the Supreme Court of Canada’s decision in R. v. Cuerrier (1998) where it was ruled that the nondisclosure of HIV-positive status could vitiate otherwise freely given consent, resulting in the sexual act being deemed aggravated assault or aggravated sexual assault. Specifically, I am interested in how the logic of HIV nondisclosure law is deeply interwoven with heteronormative assumptions about sexuality and how consent is negotiated in practice. To interrogate the often-unstated assumptions underlying the Court’s decision, I examine how the legal imperative to speak about one’s HIV status is resolved within gay sexual spaces (where consent is customarily negotiated wordlessly). My goal, in doing this, is to identify how these competing imperatives (i.e., the legal obligation to speak and a custom of staying silent) are resolved within cultural and linguistic practice. In this study, I use autoethnography, semi-structured interviews, and legal analysis to examine the legal and political implications of the criminalization of HIV nondisclosure. My findings suggest that existing approaches to HIV nondisclosure in criminal law are insufficiently attentive to how regulatory apparatuses, including social norms, shape the interpretation of sexual practices. This often results in courts confounding sexual diversity with sexual violence, which continues a long-held tradition of criminalizing sexual minorities. Guided by these insights, my legal analysis challenges the logic of HIV nondisclosure law more directly. Specifically, I argue that privileging putatively “rational” faculties, like autonomy, in the regulation of sexualities fails to adequately capture the complexities embodied in sex and negotiations of sexual consent. As an alternative, I offer a new model—what I call bodily subjectivity—to more fully capture the visceral harm enacted by acts of sexual assault
    corecore