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  1. Political Footprints: Political Discourse Analysis Using Pre-Trained Word Vectors.Christophe Bruchansky - manuscript
    How political opinions are spread on social media has been the subject of many academic researches recently, and rightly so. Social platforms give researchers a unique opportunity to understand how public discourses are perceived, owned and instrumentalized by the general public. This paper is instead focussing on the political discourses themselves, and how a specific machine learning technique - vector space models (VSMs) -, can be used to make systematic and more objective discourse analysis. Political footprints are vector-based representation of (...)
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  2. Neural-Symbolic Cognitive Reasoning.Artur D'Avila Garcez, Luis Lamb & Dov Gabbay - 2009 - New York: Springer.
    Humans are often extraordinary at performing practical reasoning. There are cases where the human computer, slow as it is, is faster than any artificial intelligence system. Are we faster because of the way we perceive knowledge as opposed to the way we represent it? -/- The authors address this question by presenting neural network models that integrate the two most fundamental phenomena of cognition: our ability to learn from experience, and our ability to reason from what has been learned. This (...)
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  3. Convergence to the Truth and Nothing but the Truth.Kevin T. Kelly & Clark Glymour - 1989 - Philosophy of Science 56 (2):185-220.
    One construal of convergent realism is that for each clear question, scientific inquiry eventually answers it. In this paper we adapt the techniques of formal learning theory to determine in a precise manner the circumstances under which this ideal is achievable. In particular, we define two criteria of convergence to the truth on the basis of evidence. The first, which we call EA convergence, demands that the theorist converge to the complete truth "all at once". The second, which we call (...)
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  4. Autonomy, Allostasic Mechanisms, and AI: A Biomimetic Perspective.Ioan Muntean & Cory Wright - 2007 - Pragmatics and Cognition 15 (3):489–513.
    We argue that the concepts of mechanism and autonomy appear to be antagonistic when autonomy is conflated with agency. Once these concepts are disentangled, it becomes clearer how autonomy emerges from complex forms of control. Subsequently, current biomimetic strategies tend to focus on homeostatic regulatory systems; we propose that research in AI and robotics would do well to incorporate biomimetic strategies that instead invoke models of allostatic mechanisms as a way of understanding how to enhance autonomy in artificial systems.
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  5. Inductive Logic, Verisimilitude, and Machine Learning.Ilkka Niiniluoto - 2005 - In Petr Hájek, Luis Valdés-Villanueva & Dag Westerståhl (eds.), Logic, methodology and philosophy of science. London: College Publications. pp. 295/314.
    This paper starts by summarizing work that philosophers have done in the fields of inductive logic since 1950s and truth approximation since 1970s. It then proceeds to interpret and critically evaluate the studies on machine learning within artificial intelligence since 1980s. Parallels are drawn between identifiability results within formal learning theory and convergence results within Hintikka’s inductive logic. Another comparison is made between the PAC-learning of concepts and the notion of probable approximate truth.
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  6. The Orbital Space Environment and Space Situational Awareness Domain Ontology – Towards an International Information System for Space Data.Robert J. Rovetto - 2016 Sept - In Proceedings of The Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference.
    The orbital space environment is home to natural and artificial satellites, debris, and space weather phenomena. As the population of orbital objects grows so do the potential hazards to astronauts, space infrastructure and spaceflight capability. Orbital debris, in particular, is a universal concern. This and other hazards can be minimized by improving global space situational awareness (SSA). By sharing more data and increasing observational coverage of the space environment we stand to achieve that goal, thereby making spaceflight safer and expanding (...)
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  7. Preliminaries of a Space Situational Awareness Ontology.Robert J. Rovetto & T. S. Kelso - 2016 Feb - In Renato Zanetti, Ryan P. Russell, Martin T. Oximek & Angela L. Bowes (eds.), Proceedings of AAS/AIAA Spaceflight Mechanics Meeting, in Advances in the Astronautical Sciences. Univelt Inc.. pp. 4177-4192.
    Space situational awareness (SSA) is vital for international safety and security, and for the future of space travel. The sharing of SSA data and information should improve the state of global SSA for planetary defense and spaceflight safety. I take steps toward a Space Situational Awareness (SSA) Ontology, and outline some central objectives, requirements and desiderata in the ontology development process for this domain. The purpose of this ontological system is to explore the potential for the ontology research topic to (...)
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  8. Learning Measurement Models for Unobserved Variables.Ricardo Silva, Richard Scheines, Clark Glymour & Peter Spirtes - unknown
  9. The Role of Imagination in Social Scientific Discovery: Why Machine Discoverers Will Need Imagination Algorithms.Michael T. Stuart - forthcoming - In Mark Addis, Fernand Gobet & Peter Sozou (eds.), Scientific Discovery in the Social Sciences. Springer.
    When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them from (...)
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