Embodied Exploration of Latent Spaces and Explainable AI

• URL: http://arxiv.org/abs/2410.14590v1
• Date: Fri, 18 Oct 2024 16:40:34 GMT
• Title: Embodied Exploration of Latent Spaces and Explainable AI
• Authors: Elizabeth Wilson, Mika Satomi, Alex McLean, Deva Schubert, Juan Felipe Amaya Gonzalez,
• Abstract summary: In this paper, we explore how performers' embodied interactions with a Neural Audio Synthesis model allow the exploration of the latent space of such a model. We provide background and context for the performance, highlighting the potential of embodied practices to contribute to developing explainable AI systems.
• Score: 0.0
• License:
• Abstract: In this paper, we explore how performers' embodied interactions with a Neural Audio Synthesis model allow the exploration of the latent space of such a model, mediated through movements sensed by e-textiles. We provide background and context for the performance, highlighting the potential of embodied practices to contribute to developing explainable AI systems. By integrating various artistic domains with explainable AI principles, our interdisciplinary exploration contributes to the discourse on art, embodiment, and AI, offering insights into intuitive approaches found through bodily expression.

Related papers

• A Mechanistic Explanatory Strategy for XAI [0.0]
  This paper outlines a mechanistic strategy for explaining the functional organization of deep learning systems. According to the mechanistic approach, the explanation of opaque AI systems involves identifying mechanisms that drive decision-making. This research suggests that a systematic approach to studying model organization can reveal elements that simpler (or ''more modest'') explainability techniques might miss.
  arXiv  Detail & Related papers  (2024-11-02T18:30:32Z)
• Diffusion-Based Visual Art Creation: A Survey and New Perspectives [51.522935314070416]
  This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives. Our findings reveal how artistic requirements are transformed into technical challenges and highlight the design and application of diffusion-based methods within visual art creation. We aim to shed light on the mechanisms through which AI systems emulate and possibly, enhance human capacities in artistic perception and creativity.
  arXiv  Detail & Related papers  (2024-08-22T04:49:50Z)
• Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
  Article explores the convergence of connectionist and symbolic artificial intelligence (AI) Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
  arXiv  Detail & Related papers  (2024-07-11T14:00:53Z)
• Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing [51.524108608250074]
  Black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. We perform a systematic review to identify the key trends in the field and shed light on novel explainable AI approaches. We also give a detailed outlook on the challenges and promising research directions.
  arXiv  Detail & Related papers  (2024-02-21T13:19:58Z)
• On the Emergence of Symmetrical Reality [51.21203247240322]
  We introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations. We propose an instance of an AI-driven active assistance service that illustrates the potential applications of symmetrical reality.
  arXiv  Detail & Related papers  (2024-01-26T16:09:39Z)
• Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making [0.0]
  We discuss how active inference can be leveraged to design explainable AI systems. We propose an architecture for explainable AI systems using active inference.
  arXiv  Detail & Related papers  (2023-06-06T21:38:09Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• AI-based artistic representation of emotions from EEG signals: a discussion on fairness, inclusion, and aesthetics [2.6928226868848864]
  We present an AI-based Brain-Computer Interface (BCI) in which humans and machines interact to express feelings artistically. We seek to understand the dynamics of this interaction to reach better co-existence in fairness, inclusion, and aesthetics.
  arXiv  Detail & Related papers  (2022-02-07T14:51:02Z)
• A general framework for scientifically inspired explanations in AI [76.48625630211943]
  We instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations.
  arXiv  Detail & Related papers  (2020-03-02T10:32:21Z)

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.