AI-based artistic representation of emotions from EEG signals: a
discussion on fairness, inclusion, and aesthetics
- URL: http://arxiv.org/abs/2202.03246v1
- Date: Mon, 7 Feb 2022 14:51:02 GMT
- Title: AI-based artistic representation of emotions from EEG signals: a
discussion on fairness, inclusion, and aesthetics
- Authors: Piera Riccio, Kristin Bergaust, Boel Christensen-Scheel, Juan-Carlos
De Martin, Maria A. Zuluaga, Stefano Nichele
- Abstract summary: 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.
- Score: 2.6928226868848864
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While Artificial Intelligence (AI) technologies are being progressively
developed, artists and researchers are investigating their role in artistic
practices. In this work, we present an AI-based Brain-Computer Interface (BCI)
in which humans and machines interact to express feelings artistically. This
system and its production of images give opportunities to reflect on the
complexities and range of human emotions and their expressions. In this
discussion, we seek to understand the dynamics of this interaction to reach
better co-existence in fairness, inclusion, and aesthetics.
Related papers
- 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) - The Good, The Bad, and Why: Unveiling Emotions in Generative AI [73.94035652867618]
We show that EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it.
EmotionDecode reveals that AI models can comprehend emotional stimuli akin to the mechanism of dopamine in the human brain.
arXiv Detail & Related papers (2023-12-18T11:19:45Z) - AIxArtist: A First-Person Tale of Interacting with Artificial
Intelligence to Escape Creative Block [20.96181205379132]
The future of the arts and artificial intelligence (AI) is promising as technology advances.
This workshop pictorial puts forward first-person research that shares interactions between an HCI researcher and AI.
The paper explores two questions: How can AI support artists' creativity, and what does it mean to be explainable in this context.
arXiv Detail & Related papers (2023-08-22T13:15:29Z) - A Portrait of Emotion: Empowering Self-Expression through AI-Generated
Art [0.0]
We investigated the potential and limitations of generative artificial intelligence (AI) in reflecting the authors' cognitive processes through creative expression.
Results show a preference for images based on the descriptions of the authors' emotions over the main events.
Our research framework with generative AIs can help design AI-based interventions in related fields.
arXiv Detail & Related papers (2023-04-26T06:54:53Z) - Pathway to Future Symbiotic Creativity [76.20798455931603]
We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist to a Machine artist in its own right.
In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations.
We propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle.
arXiv Detail & Related papers (2022-08-18T15:12:02Z) - SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network [83.27291945217424]
We propose a novel Scene-Object interreLated Visual Emotion Reasoning network (SOLVER) to predict emotions from images.
To mine the emotional relationships between distinct objects, we first build up an Emotion Graph based on semantic concepts and visual features.
We also design a Scene-Object Fusion Module to integrate scenes and objects, which exploits scene features to guide the fusion process of object features with the proposed scene-based attention mechanism.
arXiv Detail & Related papers (2021-10-24T02:41:41Z) - Human in the Loop for Machine Creativity [0.0]
We conceptualize existing and future human-in-the-loop (HITL) approaches for creative applications.
We examine and speculate on long term implications for models, interfaces, and machine creativity.
We envision multimodal HITL processes, where texts, visuals, sounds, and other information are coupled together, with automated analysis of humans and environments.
arXiv Detail & Related papers (2021-10-07T15:42:18Z) - Generating Music and Generative Art from Brain activity [0.0]
This research work introduces a computational system for creating generative art using a Brain-Computer Interface (BCI)
The generated artwork uses brain signals and concepts of geometry, color and spatial location to give complexity to the autonomous construction.
arXiv Detail & Related papers (2021-08-09T19:33:45Z) - Emotion-aware Chat Machine: Automatic Emotional Response Generation for
Human-like Emotional Interaction [55.47134146639492]
This article proposes a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post.
Experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
arXiv Detail & Related papers (2021-06-06T06:26:15Z) - Enhancing Cognitive Models of Emotions with Representation Learning [58.2386408470585]
We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions.
Our framework integrates a contextualized embedding encoder with a multi-head probing model.
Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions.
arXiv Detail & Related papers (2021-04-20T16:55:15Z) - Understanding and Creating Art with AI: Review and Outlook [12.614901374282868]
Technologies related to artificial intelligence (AI) have a strong impact on the changes of research and creative practices in visual arts.
This paper provides an integrated review of two facets of AI and art: 1) AI is used for art analysis and employed on digitized artwork collections; 2) AI is used for creative purposes and generating novel artworks.
In relation to the role of AI in creating art, we address various practical and theoretical aspects of AI Art and consolidate related works that deal with those topics in detail.
arXiv Detail & Related papers (2021-02-18T01:38:11Z)
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.