48,612 research outputs found
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks
Despite the basic premise that next-generation wireless networks (e.g., 6G)
will be artificial intelligence (AI)-native, to date, most existing efforts
remain either qualitative or incremental extensions to existing ``AI for
wireless'' paradigms. Indeed, creating AI-native wireless networks faces
significant technical challenges due to the limitations of data-driven,
training-intensive AI. These limitations include the black-box nature of the AI
models, their curve-fitting nature, which can limit their ability to reason and
adapt, their reliance on large amounts of training data, and the energy
inefficiency of large neural networks. In response to these limitations, this
article presents a comprehensive, forward-looking vision that addresses these
shortcomings by introducing a novel framework for building AI-native wireless
networks; grounded in the emerging field of causal reasoning. Causal reasoning,
founded on causal discovery, causal representation learning, and causal
inference, can help build explainable, reasoning-aware, and sustainable
wireless networks. Towards fulfilling this vision, we first highlight several
wireless networking challenges that can be addressed by causal discovery and
representation, including ultra-reliable beamforming for terahertz (THz)
systems, near-accurate physical twin modeling for digital twins, training data
augmentation, and semantic communication. We showcase how incorporating causal
discovery can assist in achieving dynamic adaptability, resilience, and
cognition in addressing these challenges. Furthermore, we outline potential
frameworks that leverage causal inference to achieve the overarching objectives
of future-generation networks, including intent management, dynamic
adaptability, human-level cognition, reasoning, and the critical element of
time sensitivity
Using resource graphs to represent conceptual change
We introduce resource graphs, a representation of linked ideas used when
reasoning about specific contexts in physics. Our model is consistent with
previous descriptions of resources and coordination classes. It can represent
mesoscopic scales that are neither knowledge-in-pieces or large-scale concepts.
We use resource graphs to describe several forms of conceptual change:
incremental, cascade, wholesale, and dual construction. For each, we give
evidence from the physics education research literature to show examples of
each form of conceptual change. Where possible, we compare our representation
to models used by other researchers. Building on our representation, we
introduce a new form of conceptual change, differentiation, and suggest several
experimental studies that would help understand the differences between
reform-based curricula.Comment: 27 pages, 14 figures, no tables. Submitted for publication to the
Physical Review Special Topics Physics Education Research on March 8, 200
Towards ending the animal cognition war: a three-dimensional model of causal cognition
Debates in animal cognition are frequently polarized between the romantic view that some species have human-like causal understanding and the killjoy view that human causal reasoning is unique. These apparently endless debates are often characterized by conceptual confusions and accusations of straw-men positions. What is needed is an account of causal understanding that enables researchers to investigate both similarities and differences in cognitive abilities in an incremental evolutionary framework. Here we outline the ways in which a three-dimensional model of causal understanding fulfills these criteria. We describe how this approach clarifies what is at stake, illuminates recent experiments on both physical and social cognition, and plots a path for productive future research that avoids the romantic/killjoy dichotomy.Introduction Dissecting disagreement - Principles of interpretation - A big misunderstanding and the conceptual question The conceptual space of causal cognition - Causal information -- Difference‑making accounts of causality -- Geometrical–mechanical accounts - Difference‑making and geometrical–mechanical aspects of human concept of causation - Understanding causality - Parameters of causal cognition -- a) Sources of causal information -- b) Integration -- c) Explicitness From causal cognition to causal understanding - A three‑dimensional model of causal cognition - The evolution of causal cognition and the nature of causal understanding - The metrics of the model and future research Conclusio
Counterfactual Causality from First Principles?
In this position paper we discuss three main shortcomings of existing
approaches to counterfactual causality from the computer science perspective,
and sketch lines of work to try and overcome these issues: (1) causality
definitions should be driven by a set of precisely specified requirements
rather than specific examples; (2) causality frameworks should support system
dynamics; (3) causality analysis should have a well-understood behavior in
presence of abstraction.Comment: In Proceedings CREST 2017, arXiv:1710.0277
The Drink You Have When You’re Not Having a Drink
  The Architecture of the Mind is itself built on foundations that deserve probing. In this brief commentary I focus on these foundations—Carruthers’ conception of modularity, his arguments for thinking that the mind is massively modular in structure, and his view of human cognitive architectur
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