On Semantic Information in Nature

Johannsen, Wolfgang

doi:10.3390/info6030411

Cited by 8 publications

(11 citation statements)

References 27 publications

(24 reference statements)

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“…Reviews are discussed to frame necessary concepts and recommended solutions. Connected resources range from Johannson's 2015 semantic inquiry [6] to Weinstein et al's 2016 study of adolescent behavior in the digital environment [7] to Pew Research Center reports on Information Overload [4] and The Future of Well-Being in a Tech-Saturated World [5]. The extant survey work done by information scholars emphasizes the need to build on empirical studies to form connections between theoretical discussions and real-world applications.…”

Section: Methods and Limitationsmentioning

confidence: 99%

“…Because there is recent work aggregating overload resources by authorities in many professions, the review finds unexpected intersections in the metaphysical concept of information as a basis for evolution [6]. A tech blog post on smartphone usage discusses the adaptation of human consciousness to digital society [14], and Sthapit also finds evidence of successful information seeking due to digital adaptation [15].…”

Section: Recent Summariesmentioning

confidence: 99%

“…Johannsen also offers a framework and an in-depth review of literature related to Semantic Information in Nature (2015). The "model [takes] into account the common denominators for all information, which are Syntax, Semantics, and Pragmatics, as defined by semiotician and philosopher Morris" [6]. These definitions reach out to discussions of artificial intelligence modelling with Furlan, et al [19], studies in decision science, linguistics, and more; however the syntax and semantics of information are highly relevant to discussions of the quality element to overload.…”

Section: Recent Summariesmentioning

confidence: 99%

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A Theoretical Conversation about Responses to Information Overload

Lehman

Miller

2020

Information

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In this study, information overload is viewed through the lenses of Library & Information Science and Communication Theory in order to offer recommended solutions for individuals experiencing overload. The purpose of this research was to apply LIS and COMM theories to the pathologies and symptoms of information overload as experienced by individuals in an increasingly digital world. Extant survey work was reviewed and updated with literature collected through limited keyword searches. The authors framed active responses to information overload through dimensions selected from the European Commission’s Digital Competence Framework as applied to Al-Shboul & Abrizah’s (2016) Modes of Information Seeking. Further study should focus on international perspectives and addressing disparities in access to information.

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Section: Methods and Limitationsmentioning

confidence: 99%

Section: Recent Summariesmentioning

confidence: 99%

Section: Recent Summariesmentioning

confidence: 99%

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A Theoretical Conversation about Responses to Information Overload

Lehman

Miller

2020

Information

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“…The right most bracket in (10) can be viewed as an advantage where words resulting in a higher semantic similarity are given a positive reward, while those with a low semantic similarity are therefore punished (see Fig. 2(b)).…”

Section: Critic Part Actor Partmentioning

confidence: 99%

“…As a parallel work, Bao et al [3] investigated a general case of semantic information, and theoretically analyzed certain key factors like semantic coding and semantic communication. Aside from quantitatively measuring the semantic information, there are also some works that explain from the view of biological system and physical world like [10] and [11]. As another line of work, researches on semantic similarity have also seen a dramatic progress.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-powered Semantic Communication via Semantic Similarity

Lu¹,

Li²,

Chen³

et al. 2021

Preprint

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We introduce a new semantic communication mechanism, whose key idea is to preserve the semantic information instead of strictly securing the bit-level precision. Starting by analyzing the defects of existing joint source channel coding (JSCC) methods, we show that the commonly used bit-level metrics are vulnerable of catching important semantic meaning and structures. To address this problem, we take advantage of learning from semantic similarity, instead of relying on conventional paired bit-level supervisions like cross entropy and bit error rate. However, to develop such a semantic communication system is indeed a nontrivial task, considering the nondifferentiability of most semantic metrics as well as the instability from noisy channels. To further resolve these issues, we put forward a reinforcement learning (RL)-based solution which allows us to simultaneously optimize any user-defined semantic measurement by using the policy gradient technique, and to interact with the surrounding noisy environment in a natural way. We have testified the proposed method in the challenging European-parliament dataset. Experiments on both AWGN and phase-invariant fading channel have confirmed the superiority of our method in revealing the semantic meanings, and better handling the channel noise especially in low-SNR situations. Apart from the experimental results, we further provide an indepth look at how the semantics model behaves, along with its superb generalization ability in real-life examples. As a brand new method in learning-based JSCC tasks, we also exemplify an RL-based image transmission paradigm, both to prove the generalization ability, and to leave this new topic for future discussion.

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Einführung und Überblick

Johannsen¹

2016

Information Und Ihre Bedeutung in Der Natur

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On Semantic Information in Nature

Cited by 8 publications

References 27 publications

A Theoretical Conversation about Responses to Information Overload

A Theoretical Conversation about Responses to Information Overload

Reinforcement Learning-powered Semantic Communication via Semantic Similarity

Einführung und Überblick

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