What is digital about abstraction?

• URL: http://arxiv.org/abs/2508.18181v1
• Date: Mon, 25 Aug 2025 16:35:07 GMT
• Title: What is digital about abstraction?
• Authors: Bernhard Rieder,
• Abstract summary: chapter examines abstraction as a central principle of computing.<n> tracing abstraction through historical developments in programming, operating systems, and networking.<n>From open-source projects to platform capitalism and cloud infrastructures, abstraction emerges as both a technical device and a locus of power.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This chapter examines abstraction as a central principle of computing, not merely as a cognitive skill or epistemological category, but as a material and organizational practice that structures how software is built, used, and embedded in society. By tracing abstraction through historical developments in programming, operating systems, and networking, the text highlights its dual role in enabling modularity and layering while simultaneously shaping cultural, economic, and organizational forms. From open-source projects to platform capitalism and cloud infrastructures, abstraction emerges as both a technical device and a locus of power, producing dependencies and interdependencies that reconfigure labor, governance, and control in digital environments. The chapter argues for understanding abstraction as a socio-technical process whose effects extend far beyond efficiency or convenience, influencing how computing infrastructures evolve and how power relations crystallize around them.

Related papers

• Vibe Coding, Interface Flattening [0.0]
  'vibe coding' is the development of softwares through natural-language interaction with model-driven toolchains.<n>This article argues that vibe coding is best understood as interface flattening.<n>It shows how remote compute infrastructures, latency and connectivity, structured outputs, function/tool calling, and interoperability standards relocate control and meaning-making power to model and protocol providers.
  arXiv  Detail & Related papers  (2025-12-31T16:00:59Z)
• High-level reasoning while low-level actuation in Cyber-Physical Systems: How efficient is it? [6.40412293456886]
  This study compares C++, Java, Jade, Jason, and fuzzy Jason BDI with both loosely and tightly coupled integration.<n>The study highlights concrete trade-offs between engineering workload and execution efficiency.<n>Overall, the study contributes evidence-based guidance for selecting software technologies in industrial informatization.
  arXiv  Detail & Related papers  (2025-11-16T10:37:37Z)
• Fundamentals of Physical AI [0.0]
  This work will elaborate the fundamental principles of physical artificial intelligence (Physical AI) from a scientific and systemic perspective.<n>The aim is to create a theoretical foundation that describes the physical embodiment, sensory perception, ability to act, learning processes, and context sensitivity of intelligent systems.
  arXiv  Detail & Related papers  (2025-11-12T17:16:14Z)
• Revisiting Abstractions for Software Architecture and Tools to Support Them [0.9558392439655016]
  We present a conceptual view of software architecture based on abstractions used in practice to organize software systems.<n>We reflect on the paper's principal ideas about system-level abstractions.<n>We describe current manifestations of architectural ideas and current challenges.
  arXiv  Detail & Related papers  (2025-03-06T01:48:25Z)
• Finding structure in logographic writing with library learning [55.63800121311418]
  We develop a computational framework for discovering structure in a writing system. Our framework discovers known linguistic structures in the Chinese writing system. We demonstrate how a library learning approach may help reveal the fundamental computational principles that underlie the creation of structures in human cognition.
  arXiv  Detail & Related papers  (2024-05-11T04:23:53Z)
• Neural Causal Abstractions [63.21695740637627]
  We develop a new family of causal abstractions by clustering variables and their domains. We show that such abstractions are learnable in practical settings through Neural Causal Models. Our experiments support the theory and illustrate how to scale causal inferences to high-dimensional settings involving image data.
  arXiv  Detail & Related papers  (2024-01-05T02:00:27Z)
• Emergence and Function of Abstract Representations in Self-Supervised Transformers [0.0]
  We study the inner workings of small-scale transformers trained to reconstruct partially masked visual scenes. We show that the network develops intermediate abstract representations, or abstractions, that encode all semantic features of the dataset. Using precise manipulation experiments, we demonstrate that abstractions are central to the network's decision-making process.
  arXiv  Detail & Related papers  (2023-12-08T20:47:15Z)
• Learnability with Time-Sharing Computational Resource Concerns [65.268245109828]
  We present a theoretical framework that takes into account the influence of computational resources in learning theory. This framework can be naturally applied to stream learning where the incoming data streams can be potentially endless. It may also provide a theoretical perspective for the design of intelligent supercomputing operating systems.
  arXiv  Detail & Related papers  (2023-05-03T15:54:23Z)
• On Binding Objects to Symbols: Learning Physical Concepts to Understand Real from Fake [155.6741526791004]
  We revisit the classic signal-to-symbol barrier in light of the remarkable ability of deep neural networks to generate synthetic data. We characterize physical objects as abstract concepts and use the previous analysis to show that physical objects can be encoded by finite architectures. We conclude that binding physical entities to digital identities is possible in finite time with finite resources.
  arXiv  Detail & Related papers  (2022-07-25T17:21:59Z)
• Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization [49.00409552570441]
  We study the role of conceptualization in commonsense reasoning, and formulate a framework to replicate human conceptual induction. We apply the framework to ATOMIC, a large-scale human-annotated CKG, aided by the taxonomy Probase.
  arXiv  Detail & Related papers  (2022-06-03T12:24:49Z)
• AIGenC: An AI generalisation model via creativity [1.933681537640272]
  Inspired by cognitive theories of creativity, this paper introduces a computational model (AIGenC) It lays down the necessary components to enable artificial agents to learn, use and generate transferable representations. We discuss the model's capability to yield better out-of-distribution generalisation in artificial agents.
  arXiv  Detail & Related papers  (2022-05-19T17:43:31Z)
• Introduction to the Artificial Intelligence that can be applied to the Network Automation Journey [68.8204255655161]
  The "Intent-Based Networking - Concepts and Definitions" document describes the different parts of the ecosystem that could be involved in NetDevOps. The recognize, generate intent, translate and refine features need a new way to implement algorithms.
  arXiv  Detail & Related papers  (2022-04-02T08:12:08Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.