{"id":8,"date":"2019-06-15T19:37:35","date_gmt":"2019-06-15T17:37:35","guid":{"rendered":"http:\/\/calculemus.org\/fi5\/?page_id=8"},"modified":"2019-11-24T21:35:05","modified_gmt":"2019-11-24T20:35:05","slug":"referaty","status":"publish","type":"page","link":"https:\/\/calculemus.org\/fi5\/referaty\/","title":{"rendered":"Referaty"},"content":{"rendered":"<div class='stb-container stb-style-anons stb-caption-box stb-no-caption stb-collapsed'><div class='stb-caption'><div class='stb-logo'><img class='stb-logo__image' src='' alt='img'\/><\/div><div class='stb-caption-content'>Piotr Giza (UMCS): C&lt;em&gt;reativity in Computer Science.&lt;\/em&gt;<\/div><div class='stb-tool'><\/div><\/div><div class='stb-content'><\/p>\n<p>The aim of this paper is to explore creative thinking in computer science and compare it to natural sciences, mathematics or engineering. It is also meant as polemics with some theses of the pioneering work under the same title by Daniel Saunders and Paul Thagard (2005) because I point to important motivations in computer science the authors do not mention and give examples of the origins of problems they explicitly deny. No doubt, creative activity in computer science differs from that in other sciences. Some authors even express general worries that computer science runs counter creative thinking, that as a consequence of the computer revolution, humans will become so lazy that they may lose their power of creative thinking (Gardner, 1978).<br \/>\nCreativity in computer science has different nature and origins of problems, motivations, and methods, then those of natural sciences and engineering. Computer science is a very specific field for it relates the abstract, theoretical discipline &#8211; mathematics, on the one hand, and engineering, often concerned with very practical tasks of building computers, on the other. It is like engineering in that it is concerned with solving practical problems or implementing solutions, often with strong financial reasons, eg. increasing a company&#8217;s income. It is like mathematics in that is deals with abstract symbols, logical relations, algorithms, computability problems, etc.<br \/>\nSaunders and Thagard analyze rich experimental material from historical and contemporary work in computer science and argue that, contrary to natural sciences, computer science &#8222;[&#8230;] is not concerned with empirical questions involving naturally observed phenomena, nor with theoretical why-questions aimed at providing explanations of such phenomena&#8221;. Now, I argue that there is a field of research in artificial intelligence (which, in turn, is a branch of computer science), called Machine Discovery, where the explanation of natural phenomena, finding experimental laws and explanatory models is the primary goal. This goal is achieved by constructing computer systems whose job is to simulate various processes involved in scientific discovery done by human researchers and help them in making new discoveries.<br \/>\nOn the other hand, motivations that give rise to ingenious projects in computer science can be very strange and include curiosity, fun or attempts to be famous out of boring, stable life of a successful programmer in a big corporation. A good example is the phenomenon of Open Source software, especially the development of the Linux operating system and its applications when, from the economical point of view, Microsoft absolutely dominated the software market of PC computers.<\/p>\n<p><strong>Keywords: <\/strong>creativity, computer science, technology, natural sciences<\/p>\n<p><strong>Email:<\/strong> pgiza@bacon.umcs.lublin.pl<\/div><\/div>\n<div class='stb-container stb-style-anons stb-caption-box stb-no-caption stb-collapsed'><div class='stb-caption'><div class='stb-logo'><img class='stb-logo__image' src='' alt='img'\/><\/div><div class='stb-caption-content'>Hajo Greif (PW): &lt;em&gt;Justifying Black Box Models in Artificial Intelligence.&lt;\/em&gt;<\/div><div class='stb-tool'><\/div><\/div><div class='stb-content'><\/p>\n<p>The renaissance of Artificial Intelligence (AI) is marked by two seemingly countervailing developments: more powerful and sophisticated computational resources on the one hand, and increasing modesty concerning the endeavor of modeling human intelligence on the other. The first development has epistemologically relevant implications with respect to the \u2018epistemic opacity\u2019 incurred by computational complexity (a.k.a. the \u2018Black Box Problem\u2019). The second development manifests itself in distinct strategies of coping with opacity: a return to the original aim of AI of making computers solve problems that would require intelligence from humans, without seeking to provide insights into human intelligence or a deliberate self-restriction to modeling prima facie simple and epistemically tractable, but embodied and environmentally situated activities.<\/p>\n<p>The first, expository aim of this paper is to demonstrate that these alternative routes away from the claims to the cognitive stimulation that dominated classical AI (or \u2018GOFAI\u2019) amount to two distinct strategies of coping with the opacity brought about by computational complexity. These strategies are exemplified by Behaviour-based AI and Evolutionary Robotics (BBAI) and Deep Neural Networks (DNNs). In BBAI (Brooks 1991), complexity is reduced in order to gain transparency and biological plausibility \u2013 which is bought at the cost of the \u2018scaling problem\u2019: will the model also be able to explain complex cognitive abilities, and still remain tractable? In DNNs (LeCun et al. 2015), epistemic opacity is accepted in order to make most of the computational complexity that can now be technologically mastered \u2013 which is bought at the cost of limitations on what the observer will be able to learn from and about a model that successfully produces a solution to a given problem.<\/p>\n<p>The second, critical aim of this paper is a defence of epistemic opacity in modelling. Pragmatist views of modeling and simulation that highlight the methodologically \u2018motley\u2019 and epistemically \u2018opaque\u2019 character of computer simulations drop the requirements that models must be \u2018analytically tractable\u2019 in terms of the \u2018ability to decompose the process between model inputs and outputs into modular steps\u2019 (Humphreys 2004). The sanctioning of models is \u2018holistic\u2019 instead, in that it is based on the \u2018simultaneous\u2028 confluence\u2019 of theory, available mathematics, previous results and background knowledge (Winsberg 2010). Analytical intractability need not conflict with, or may even subserve, the representational properties of the model as a whole. Arguably, the requirement of epistemic transparency understood as analytical tractability of models has not been a constituent of scientific practice throughout most of the history of the empirical sciences. It may actually owe to the invention of computational methods of modeling that rely on breaking down complex mathematical structures into elementary, and essentially tractable, computational steps. This latter development has been particularly pertinent to the methods of modeling in AI.<\/p>\n<p><strong>Keywords:<\/strong> models in Artificial Intelligence, black box problem, behaviour-based Artificial Intelligence, deep neural networks<\/p>\n<p>Brooks, R. Intelligence Without Representation. Artificial Intelligence 47 (1991), 139\u2013159.<br \/>\nHumphreys, P. Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford University Press, Oxford, 2004.<br \/>\nLeCun, Y., Bengio, Y., and Hinton, G. Deep Learning. Nature 521 (2015), 436\u2013444.<br \/>\nWinsberg, E. B. Science in the Age of Computer Simulation. University of Chicago Press, Chicago, 2010.<\/p>\n<p><strong>Email: <\/strong>h.greif@ans.pw.edu.pl<\/div><\/div>\n<div class='stb-container stb-style-anons stb-caption-box stb-no-caption stb-collapsed'><div class='stb-caption'><div class='stb-logo'><img class='stb-logo__image' src='' alt='img'\/><\/div><div class='stb-caption-content'>Aleksandra Ko\u0142tun (UMCS): &lt;em&gt;Writing as distributed socio-material practice \u2013 a case study.&lt;\/em&gt;<\/div><div class='stb-tool'><\/div><\/div><div class='stb-content'><\/p>\n<p>The aim of the presentation is to provide an empirically-based case study of writing understood as a hybrid task distributed in a complex socio-material setting (see O\u2019Hara, Kenton et al. 2002). In the case presented writing takes place in a dynamic environment in which people have to find, share and integrate information from several documents for making decisions and generating text.<br \/>\nThe case under scrutiny concerns the process of preparing formal regulations for participatory budgeting in selected Polish municipalities. I will focus on the course of workshops for civil servants and citizens in which the contents of regulations are discussed and established. Firstly, I will provide background information on how the workshops setting is organized in order to ensure open, but goal-oriented communication between the participants. The context of work is structured thanks to 1\/ entry points that made some information more available than other as well as helped to keep track of the workflow, 2\/ activity landscapes for tasks such as information search and debate, 3\/ mechanisms that allow coordination between people and documents (see Kirsh 2001). Secondly, I will present a detailed account of the dynamics of writing activities across various material media: from flipcharts containing handwritten, followed by various versions of the regulations that are reviewed and modified until reaching a black-boxed, neat, and officially appraised draft of the final document.<br \/>\nThe case demonstrated can also be treated as a supplement to the field of distributed cognition. So far most research conducted in this framework has dealt with environments that strongly support human-computer interaction. The task described here is performed in a setting that is rich in artifacts and external representations, but involves almost no technological devices.<\/p>\n<p><strong>Keywords:<\/strong> writing, distributed cognition, information search and sharing, office work<\/p>\n<p>Hollan, J. D., Hutchins, E., &amp; Kirsh, D. (2000). Distributed cognition: Towards a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interaction, 7(2), 174\u2013176.<br \/>\nKirsh, D. (2001). The context of work. Human\u2013Computer Interaction, 16.2-4, 305-322.<br \/>\nKirsh, D. (2010). Thinking with external representations. AI &amp; Society, 25(4), 441-454.<br \/>\nO&#8217;Hara, K., Taylor, A., Newman, W., &amp; Sellen, A. J. (2002). Understanding the materiality of writing from multiple sources. International Journal of Human-Computer Studies, 56(3), 269-305.<br \/>\nRogers, Y., &amp; Ellis, J. (1994). Distributed cognition: an alternative framework for analysing and explaining collaborative working. Journal of Information Technology, 9(2), 119-128.<\/p>\n<p><strong>Email:<\/strong> aleksandra.koltun@gmail.com<\/div><\/div>\n<div class='stb-container stb-style-anons stb-caption-box stb-no-caption stb-collapsed'><div class='stb-caption'><div class='stb-logo'><img class='stb-logo__image' src='' alt='img'\/><\/div><div class='stb-caption-content'>Rafa\u0142 Maci\u0105g (UJ): &lt;em&gt;Discursive space as a theory of knowledge and information.&lt;\/em&gt;<\/div><div class='stb-tool'><\/div><\/div><div class='stb-content'><\/p>\n<p>Badania dotycz\u0105ce informacji znajduj\u0105 si\u0119 w kryzysie nadprodukcji. Ju\u017c w 2011 roku Yan poda\u0142 172 dyscypliny naukowe oparte na fenomenie informacji. Wydaje si\u0119, \u017ce interesuj\u0105ca w tym \u015bwietle jest idea Dodig-Crnkovic i Burgina (2019), kt\u00f3rzy zaproponowali w\u0142asne podej\u015bcie do bada\u0144 informacji nazwane Study of Information (SOI), kt\u00f3re ma z za\u0142o\u017cenia charakter transdyscyplinarny. Takie podej\u015bcie ma po pierwsze odzwierciedla\u0107 stan refleksji, a po drugie znajduje korespondencj\u0119 z takimi dziedzinami jak Library and Information Science (LIS), Knowledge Organization (KO) czy Information behavior. SOI jest do pewnego stopnia podej\u015bciem do nich komplementarnym, kt\u00f3re w odr\u00f3\u017cnieniu do refleksji obecnej w LIS, IS czy KO, rozumie informacj\u0119 b\u0119d\u0105c\u0105 przedmiotem badania jako autonomiczny byt. Takie podej\u015bcie pozwala dalej formu\u0142owa\u0107 warunki, realizacje i skutki takiej autonomiczno\u015bci. Podej\u015bcie konkurencyjne wychodzi z za\u0142o\u017cenia, \u017ce fenomeny takie jak informacja czy wiedza s\u0105 immanentnie zwi\u0105zane z cz\u0142owiekiem i jego \u015bwiatem. Jest to jednak podej\u015bcie z trudem obejmuj\u0105ce najnowsze osi\u0105gni\u0119cia technologiczne, kt\u00f3re np. naruszaj\u0105 w powa\u017cnym stopniu hegemoni\u0119 cz\u0142owieka opart\u0105 na za\u0142o\u017ceniu wy\u0142\u0105czno\u015bci jego kompetencji kognitywnych. Podobny problem \u2013 w s\u0142abszej wersji \u2013 pojawia si\u0119 w wypadku pewnych proces\u00f3w biologicznych, kt\u00f3re mo\u017cna zinterpretowa\u0107 jako procesy obliczeniowe (computational).<br \/>\nW opisanej sytuacji, gwa\u0142townie powi\u0119kszaj\u0105cego si\u0119 i komplikuj\u0105cego pola refleksji po\u015bwi\u0119conej informacji i wiedzy, znajduj\u0105 uzasadnienie podej\u015bcia metateoretyczne, dobrze ugruntowane w istniej\u0105cej refleksji oraz oferuj\u0105ce mo\u017cliwo\u015b\u0107 formalizacji. Ta ostatnia w\u0142a\u015bciwo\u015b\u0107 nie tylko otwiera szans\u0119 na swoiste pogodzenie podej\u015bcia jako\u015bciowego i ilo\u015bciowego, ale przede wszystkim pozwala w dalszej perspektywie u\u017cywa\u0107 narz\u0119dzi implementacyjnych w\u0142a\u015bciwych dla obszaru informatyki np. do tworzenia odpowiednich algorytm\u00f3w. Takie podej\u015bcie reprezentuje teoria tzw. przestrzeni dyskursywnej, kt\u00f3rej podstawowe za\u0142o\u017cenia opublikowa\u0142em w roku 2018.<br \/>\nS\u0142aba, pocz\u0105tkowa wersja tej teorii dotyczy wiedzy przejawiaj\u0105cej si\u0119 poprzez dyskursy rozumiane jako zaawansowane struktury j\u0119zykowe (Foucault) i jest oparta na ontologicznych za\u0142o\u017ceniach zaczerpni\u0119tych z Armstronga, a za jego po\u015brednictwem z Wittgensteina. Jej g\u0142\u00f3wnym wk\u0142adem jest propozycja zinterpretowania rzeczywisto\u015bci dyskurs\u00f3w za pomoc\u0105 konstrukcji zapo\u017cyczonej z fizyki, jak\u0105 jest przestrze\u0144 dynamiczna. Dyskursy przebiegaj\u0105 w\u0142a\u015bciwe sobie trajektorie w abstrakcyjnej przestrzeni, kt\u00f3ra nie jest ograniczona pod wzgl\u0119dem liczby wymiar\u00f3w. Paradygmatycznej podstawy teoretycznej do interpretacji takiego stanu dostarcza teoria z\u0142o\u017cono\u015bci. Ekstrapolacja przestrzeni dynamicznej jako podstawy opisowej przestrzeni dyskursywnej prowadzi dalej do u\u017cycia rozmaito\u015bci w celu opisania rzeczywisto\u015bci dyskurs\u00f3w. Jej idea odwo\u0142uje si\u0119 do \u017ar\u00f3d\u0142owego pomys\u0142u Riemanna (1854) oraz Husserla (1906\/1907), nieco odmiennych od p\u00f3\u017aniejszych formalnych interpretacji tego konstruktu.<br \/>\nSilna, rozwini\u0119ta teoria przestrzeni dyskursywnej uog\u00f3lnia poj\u0119cie dyskursu na ka\u017cd\u0105 realizacj\u0119 (retencj\u0119\/artykulacj\u0119) wiedzy, tak\u017ce poza obszarem j\u0119zykowym. Oznacza to ogarni\u0119cie modelem przestrzeni dynamicznej i rozmaito\u015bci wszelkich przejaw\u00f3w wiedzy, kt\u00f3re mog\u0105 przyjmowa\u0107 realizacj\u0119 j\u0119zykow\u0105, chemiczn\u0105, fizykaln\u0105 itp. W pewnym zakresie wykazuje ona podobie\u0144stwo do teorii system\u00f3w Luhmana (1984). Teoria ta z konieczno\u015bci w fundamentalny spos\u00f3b przekonstruowuje ide\u0119 informacji, odwracaj\u0105c dotychczasow\u0105, przewa\u017caj\u0105c\u0105 hierarchi\u0119 informacji i wiedzy. Teoria ta ma charakter holistyczny, co wynika z zaproponowanego dualizmu wyj\u015bciowego, opartego na idei ca\u0142o\u015bci i cz\u0119\u015bci. Chocia\u017c idea ta odwo\u0142uje si\u0119 ona do pewnej podstawy ontologicznej, wyposa\u017conej w bogat\u0105 refleksj\u0119 filozoficzn\u0105, posiada jednocze\u015bnie swoj\u0105 interpretacj\u0119 formaln\u0105, teoriomnogo\u015bciow\u0105 (Le\u015bniewski). W tej cz\u0119\u015bci teoria znajduje si\u0119 w stadium rozwoju, kt\u00f3ry ma w intencji zmierza\u0107 w stron\u0119 jej formalizacji. Prace te s\u0105 finansowane z grantu NCN.<\/p>\n<p><strong>S\u0142owa kluczowe:<\/strong> przestrze\u0144 dyskursywna, wiedza, informacja, z\u0142o\u017cono\u015b\u0107, przestrze\u0144 dynamiczna<\/p>\n<p><strong>Email:<\/strong> rafal.maciag@uj.edu.pl<\/div><\/div>\n<div class='stb-container stb-style-anons stb-caption-box stb-no-caption stb-collapsed'><div class='stb-caption'><div class='stb-logo'><img class='stb-logo__image' src='' alt='img'\/><\/div><div class='stb-caption-content'>Pawe\u0142 Polak and Roman Krzanowski (UPJPII): &lt;em&gt;Information \u2013 Abstract or Concrete?.&lt;\/em&gt;<\/div><div class='stb-tool'><\/div><\/div><div class='stb-content'><\/p>\n<p>The challenge to science is to figure out how to couple abstract information to the concrete world of physical objects\u201d .<br \/>\nInformation is thought of as either abstract or concrete. The dilemma of the nature of information arises as information may be conceptualized as knowledge so it is abstract not concrete or it may be conceptualized as a form of the physical entities so it is concrete not abstract. Paul Davis (and a few other writers) asks whether we have two kinds of information or one. Davis claims that the resolution of this incoherence is critical to the understanding of information. The paper discusses the nature and the source of this dichotomy and inquires whether this dichotomy really exists or is it just the effect of the conceptual framework that we use? Then, the paper evaluates arguments used by Davis and probes what are the metaphysical sources of this controversy. Finally, the paper proposes a way to resolve this dilemma. The proposed resolution requires the acceptance of information in any form to be an integral part of the physical world. So, it essentially mandates the physical nature of information, but not physical reductionism. The paper also discusses how the proposed solution impacts on the definitions of information that we generally accept, how it challenges the concepts of data, information, and knowledge and how it strains the notions of the minimally cognitive systems, the mind and computing.<\/p>\n<p><strong>Keywords:<\/strong> information dichotomy, abstract, concrete<\/p>\n<p><strong>Email:<\/strong> atpolakp@cyf-kr.edu.pl, rmkrzan@gmail.com<\/div><\/div>\n<div class='stb-container stb-style-anons stb-caption-box stb-no-caption stb-collapsed'><div class='stb-caption'><div class='stb-logo'><img class='stb-logo__image' src='' alt='img'\/><\/div><div class='stb-caption-content'>Paula Quinon (PW): &lt;em&gt;Deviant encodings and what \u201ccomputing\u201d means.&lt;\/em&gt;<\/div><div class='stb-tool'><\/div><\/div><div class='stb-content'><\/p>\n<p>My main objective is to design a common background for various philosophical discussions about adequate conceptual analysis of \u201ccomputation\u201d.<\/p>\n<p>The core of the problem discussed in this paper is the following: the Church-Turing Thesis states that Turing Machines formally explicate the intuitive concept of computability. The description of Turing Machines requires description of the notation used for the input and for the output. The notation used by Turing in the original account and also notations used in contemporary handbooks of computability all belong to the most known, common, widespread notations, such as standard Arabic notation for natural numbers, binary encoding of natural numbers or stroke notation. The choice is arbitrary and left unjustified. In fact, providing such a justification and providing a general definition of notations, which are acceptable for the process of computations, causes problems. This is so because the comprehensive definition states that such notation or encoding has to be computable. Yet, using the concept of computability in a definition of a notation, which will be further used in a definition of the concept of computability yields an obvious vicious circle.<\/p>\n<p>This argument appears in discussions about what is an adequate or correct conceptual analysis of the concept of computability. Its exact form depends on the underlying picture of mathematics that an author is working with. After presenting several contexts in which deviant encodings are problematized explicitly, I focus on philosophical examples where the phenomenon appears implicitly, in some \u201cdisguised\u201d version, for instance in the analysis of the concept of natural number. In parallel, I develop the idea that Carnapian explications provide a much more adequate framework for understanding the concept of computation, than the classical philosophical analysis. Intensional differences between formal models of computation can (and hence should) be directly correlated with different possible clarifications (in Carnapian terms) of the intuitive concept and hence retraced to different intuitions guiding the formalization process.<\/p>\n<p><strong>Keywords:<\/strong> the Church-Turing thesis, deviant encodings, fixed points of conceptual analysis<\/p>\n<p>Benacerraf, P. (1996). Recantation, or: Any Old \u03c9-Sequence Would Do After All. Philosophia Mathematica (4), 184\u2013189<br \/>\nCopeland, J., Proudfoot, D. (2010). Deviant encodings and Turing\u2019s analysis of computability. Studies in History and Philosophy of Sciences 41, 247\u2013252.<br \/>\nQuinon, P., Zdanowski, K. (2007): Intended Model of Arithmetic. Argument from Tennenbaum\u2019s Theorem. In: Cooper, S., Loewe, B., Sorbi, A. (eds.) Computation and Logic in the Real World, CiE Proceedings<br \/>\nRescorla, M. (2007): Church\u2019s Thesis and the Conceptual Analysis of Computability. Notre Dame Journal of Formal Logic 48, 253\u2013280.<br \/>\nShapiro, S. (1982). Acceptable Notation. Notre Dame Journal of Formal Logic 23(1), 14\u201320.<\/p>\n<p><strong>Email:<\/strong>\u00a0paula.quinon@gmail.com<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"onecolumn-page.php","meta":[],"_links":{"self":[{"href":"https:\/\/calculemus.org\/fi5\/wp-json\/wp\/v2\/pages\/8"}],"collection":[{"href":"https:\/\/calculemus.org\/fi5\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/calculemus.org\/fi5\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/calculemus.org\/fi5\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/calculemus.org\/fi5\/wp-json\/wp\/v2\/comments?post=8"}],"version-history":[{"count":26,"href":"https:\/\/calculemus.org\/fi5\/wp-json\/wp\/v2\/pages\/8\/revisions"}],"predecessor-version":[{"id":262,"href":"https:\/\/calculemus.org\/fi5\/wp-json\/wp\/v2\/pages\/8\/revisions\/262"}],"wp:attachment":[{"href":"https:\/\/calculemus.org\/fi5\/wp-json\/wp\/v2\/media?parent=8"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}