Search (158 results, page 8 of 8)

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Series

Reviews of concepts in knowledge organization

Source

Knowledge organization. 46(2019) no.1, S.33-46

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Abstract

Purpose Arts-informed, visual research was conducted to document the pictorial metaphors that appear among original drawings of information. The purpose of this paper is to report the diversity of these pictorial metaphors, delineate their formal qualities as drawings, and provide a fresh perspective on the concept of information. Design/methodology/approach The project utilized pre-existing iSquare drawings of information that were produced by iSchool graduate students during a draw-and-write activity. From a data set of 417 images, 125 of the strongest pictorial metaphors were identified and subjected to cognitive metaphor theory. Findings Overwhelmingly, the favored source domain for envisioning information was nature. The most common pictorial metaphors were: Earth, web, tree, light bulb, box, cloud, and fishing/mining, and each brings different qualities of information into focus. The drawings were often canonical versions of objects in the world, leading to arrays of pictorial metaphors marked by their similarity. Research limitations/implications Less than 30 percent of the data set qualified as pictorial metaphors, making them a minority strategy for representing information as an image. The process to identify and interpret pictorial metaphors was highly subjective. The arts-informed methodology generated tensions between artistic and social scientific paradigms. Practical implications The pictorial metaphors for information can enhance information science education and fortify professional identity among information professionals. Originality/value This is the first arts-informed, visual study of information that utilizes cognitive metaphor theory to explore the nature of information. It strengthens a sense of history, humanity, nature, and beauty in our understanding of information today, and contributes to metaphor research at large.

Source

Journal of documentation. 72(2016) no.5, S.794-812

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Abstract

The purpose of this paper is to formulate an analytical framework for the information concept based on the semiotic theory. Design/methodology/approach The paper is motivated by the apparent controversy that still surrounds the information concept. Information, being a key concept within LIS, suffers from being anchored in various incompatible theories. The paper suggests that information is signs, and it demonstrates how the concept of information can be understood within C.S. Peirce's phenomenologically rooted semiotic. Hence, from there, certain ontological conditions as well epistemological consequences of the information concept can be deduced. Findings The paper argues that an understanding of information, as either objective or subjective/discursive, leads to either objective reductionism and signal processing, that fails to explain how information becomes meaningful at all, or conversely, information is understood only relative to subjective/discursive intentions, agendas, etc. To overcome the limitations of defining information as either objective or subjective/discursive, a semiotic analysis shows that information understood as signs is consistently sensitive to both objective and subjective/discursive features of information. It is consequently argued that information as concept should be defined in relation to ontological conditions having certain epistemological consequences. Originality/value The paper presents an analytical framework, derived from semiotics, that adds to the developments of the philosophical dimensions of information within LIS.

Source

Journal of documentation. 74(2018) no.2, S.372-382

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Abstract

Purpose The purpose of this paper is to treat disinformation and misinformation (intentionally deceptive and unintentionally inaccurate misleading information, respectively) as a socio-cultural technology-enabled epidemic in digital news, propagated via social media. Design/methodology/approach The proposed disinformation and misinformation triangle is a conceptual model that identifies the three minimal causal factors occurring simultaneously to facilitate the spread of the epidemic at the societal level. Findings Following the epidemiological disease triangle model, the three interacting causal factors are translated into the digital news context: the virulent pathogens are falsifications, clickbait, satirical "fakes" and other deceptive or misleading news content; the susceptible hosts are information-overloaded, time-pressed news readers lacking media literacy skills; and the conducive environments are polluted poorly regulated social media platforms that propagate and encourage the spread of various "fakes." Originality/value The three types of interventions - automation, education and regulation - are proposed as a set of holistic measures to reveal, and potentially control, predict and prevent further proliferation of the epidemic. Partial automated solutions with natural language processing, machine learning and various automated detection techniques are currently available, as exemplified here briefly. Automated solutions assist (but not replace) human judgments about whether news is truthful and credible. Information literacy efforts require further in-depth understanding of the phenomenon and interdisciplinary collaboration outside of the traditional library and information science, incorporating media studies, journalism, interpersonal psychology and communication perspectives.

Source

Journal of documentation. 75(2019) no.5, S.1013-1034

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Abstract

This article proposes a theory of information need for information retrieval (IR). Information need traditionally denotes the start state for someone seeking information, which includes information search using an IR system. There are two perspectives on information need. The dominant, computer science perspective is that the user needs to find an answer to a well-defined question which is easy for the user to formulate into a query to the system. Ironically, information science's best known model of information need (Taylor, 1968) deems it to be a "black box"-unknowable and nonspecifiable by the user in a query to the information system. Information science has instead devoted itself to studying eight adjacent or surrogate concepts (information seeking, search and use; problem, problematic situation and task; sense making and evolutionary adaptation/information foraging). Based on an analysis of these eight adjacent/surrogate concepts, we create six testable propositions for a theory of information need. The central assumption of the theory is that while computer science sees IR as an information- or answer-finding system, focused on the user finding an answer, an information science or user-oriented theory of information need envisages a knowledge formulation/acquisition system.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.7, S.1216-1231

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Abstract

In this article we advance the conjecture that conscious awareness is equivalent to data compression. Algorithmic information theory supports the assertion that all forms of understanding are contingent on compression (Chaitin, 2007). Here, we argue that the experience people refer to as consciousness is the particular form of understanding that the brain provides. We therefore propose that the degree of consciousness of a system can be measured in terms of the amount of data compression it carries out.

Content

Vgl. auch die weiteren Artikel in ResearchGate.

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"Overflowing inboxes, endlessly topped up by incoming emails. Constant alerts, notifications and text messages on your smartphone and computer. Infinitely scrolling streams of social-media posts. Access to all the music ever recorded, whenever you want it. And a deluge of high-quality television, with new series released every day on Netflix, Amazon Prime and elsewhere. The bounty of the internet is a marvellous thing, but the ever-expanding array of material can leave you feeling overwhelmed, constantly interrupted, unable to concentrate or worried that you are missing out or falling behind. No wonder some people are quitting social media, observing "digital sabbaths" when they unplug from the internet for a day, or buying old-fashioned mobile phones in an effort to avoid being swamped. This phenomenon may seem quintessentially modern, but it dates back centuries, as Ann Blair of Harvard University observes in "Too Much to Know", a history of information overload. Half a millennium ago, the printing press was to blame. "Is there anywhere on Earth exempt from these swarms of new books?" moaned Erasmus in 1525. New titles were appearing in such abundance, thousands every year. How could anyone figure out which ones were worth reading? Overwhelmed scholars across Europe worried that good ideas were being lost amid the deluge. Francisco Sanchez, a Spanish philosopher, complained in 1581 that 10m years was not long enough to read all the books in existence. The German polymath Gottfried Wilhelm Leibniz grumbled in 1680 of "that horrible mass of books which keeps on growing"."

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Series

Studies in history and philosophy of science ; 34

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Studies in history and philosophy of science ; 34

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Studies in history and philosophy of science ; 34

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Abstract

Die Künstliche Intelligenz kann uns gefährlich werden - dann nämlich, wenn sie autonom wird. Mit Rücksicht dürfen Unterlegene in diesem Fall nicht rechnen, der Mensch wäre ein Auslaufmodell. Ein Gastbeitrag.

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Abstract

In a new study, computer scientists found that artificial intelligence systems fail a vision test a child could accomplish with ease. "It's a clever and important study that reminds us that 'deep learning' isn't really that deep," said Gary Marcus , a neuroscientist at New York University who was not affiliated with the work. The result takes place in the field of computer vision, where artificial intelligence systems attempt to detect and categorize objects. They might try to find all the pedestrians in a street scene, or just distinguish a bird from a bicycle (which is a notoriously difficult task). The stakes are high: As computers take over critical tasks like automated surveillance and autonomous driving, we'll want their visual processing to be at least as good as the human eyes they're replacing. It won't be easy. The new work accentuates the sophistication of human vision - and the challenge of building systems that mimic it. In the study, the researchers presented a computer vision system with a living room scene. The system processed it well. It correctly identified a chair, a person, books on a shelf. Then the researchers introduced an anomalous object into the scene - an image of elephant. The elephant's mere presence caused the system to forget itself: Suddenly it started calling a chair a couch and the elephant a chair, while turning completely blind to other objects it had previously seen. Researchers are still trying to understand exactly why computer vision systems get tripped up so easily, but they have a good guess. It has to do with an ability humans have that AI lacks: the ability to understand when a scene is confusing and thus go back for a second glance.

Source

https://www.quantamagazine.org/machine-learning-confronts-the-elephant-in-the-room-20180920/

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Abstract

This article presents an updated account of integrated information theory of consciousness (IIT) and some of its implications. IIT stems from thought experiments that lead to phenomenological axioms (existence, compositionality, information, integration, exclusion) and corresponding ontological postulates. The information axiom asserts that every experience is specific - it is what it is by differing in its particular way from a large repertoire of alternatives. The integration axiom asserts that each experience is unified - it cannot be reduced to independent components. The exclusion axiom asserts that every experience is definite - it is limited to particular things and not others and flows at a particular speed and resolution. IIT formalizes these intuitions with postulates. The information postulate states that only "differences that make a difference" from the intrinsic perspective of a system matter: a mechanism generates cause-effect information if its present state has selective past causes and selective future effects within a system. The integration postulate states that only information that is irreducible matters: mechanisms generate integrated information only to the extent that the information they generate cannot be partitioned into that generated within independent components. The exclusion postulate states that only maxima of integrated information matter: a mechanism specifies only one maximally irreducible set of past causes and future effects - a concept. A complex is a set of elements specifying a maximally irreducible constellation of concepts, where the maximum is evaluated over elements and at the optimal spatio-temporal scale. Its concepts specify a maximally integrated conceptual information structure or quale, which is identical with an experience. Finally, changes in information integration upon exposure to the environment reflect a system's ability to match the causal structure of the world. After introducing an updated definition of information integration and related quantities, the article presents some theoretical considerations about the relationship between information and causation and about the relational structure of concepts within a quale. It also explores the relationship between the temporal grain size of information integration and the dynamic of metastable states in the corticothalamic complex. Finally, it summarizes how IIT accounts for empirical findings about the neural substrate of consciousness, and how various aspects of phenomenology may in principle be addressed in terms of the geometry of information integration.

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Pages

S.15-41

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Abstract

We provide a model for the reception of knowledge from externalized information sources. The model is based on a cognitive understanding of information processing and draws up ideas of an exchange of information in communication processes. Karl Popper's three-world theory with its orientation on falsifiable scientific knowledge is extended by John Searle's concept of collective intentionality. This allows a consistent description of externalization and reception of knowledge including scientific knowledge as well as everyday knowledge.

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Source

Journal of the American Society for Information Science and Technology. 62(2011) no.10, S.2038-2044

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Source

Journal of documentation. 70(2014) no.1, S.74-92

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Pages

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