A Design-Led Exploration of Material Interactions Between Machine Learning and Digital Portraiture

被引：0
|
作者：
Green, David Philip [1 ]
Lindley, Joseph [1 ]
Mason, Zach [1 ]
Coulton, Paul [1 ]
机构：
[1] Univ Lancaster, Imaginat Lancaster, Lancaster, England
来源：
WITH DESIGN: REINVENTING DESIGN MODES, IASDR 2021 | 2022年
基金：
英国科研创新办公室;
关键词：
Design materials; Materiality; Machine learning; Digital portraiture; Faces; Art;
D O I：
10.1007/978-981-19-4472-7_211
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
Design materials are defined as a combination of what they are, what they do, and the ways they interact with other materials. In this pictorial, we explore the interactions between machine learning (as a design material) and another design material-the human face-in the form of digital portraiture. Employing an exploratory Research through Design approach we consider how machine learning simultaneously enriches and subverts the materiality of the human face. Through a combination of images and text, we offer some considerations and provocations for further research.
引用
收藏
页码：3268 / 3283
页数：16
相关论文
共 50 条
  • [31] A review on the self and dual interactions between machine learning and optimisation
    Heda Song
    Isaac Triguero
    Ender Özcan
    Progress in Artificial Intelligence, 2019, 8 : 143 - 165
  • [32] Prediction of interactions between cell surface proteins by machine learning
    Su, Zhaoqian
    Griffin, Brian
    Emmons, Scott
    Wu, Yinghao
    PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2024, 92 (04) : 567 - 580
  • [33] A review on the self and dual interactions between machine learning and optimisation
    Song, Heda
    Triguero, Isaac
    Ozcan, Ender
    PROGRESS IN ARTIFICIAL INTELLIGENCE, 2019, 8 (02) : 143 - 165
  • [34] Machine learning guided appraisal and exploration of phase design for high entropy alloys
    Ziqing Zhou
    Yeju Zhou
    Quanfeng He
    Zhaoyi Ding
    Fucheng Li
    Yong Yang
    npj Computational Materials, 5
  • [35] IMPROVED DESIGN SPACE EXPLORATION BY MACHINE LEARNING ESTIMATION FOR A PARAMETRIC TURBOFAN MODEL
    Foster, Chad
    Moore, Jack
    PROCEEDINGS OF ASME TURBO EXPO 2022: TURBOMACHINERY TECHNICAL CONFERENCE AND EXPOSITION, GT2022, VOL 10D, 2022,
  • [36] Machine learning guided appraisal and exploration of phase design for high entropy alloys
    Zhou, Ziqing
    Zhou, Yeju
    He, Quanfeng
    Ding, Zhaoyi
    Li, Fucheng
    Yang, Yong
    NPJ COMPUTATIONAL MATERIALS, 2019, 5 (1)
  • [37] Material synthesis and design from first principle calculations and machine learning
    Takahashi, Keisuke
    Tanaka, Yuzuru
    COMPUTATIONAL MATERIALS SCIENCE, 2016, 112 : 364 - 367
  • [38] Culture-Centered Design Enabled by Machine Learning and Digital Transformation
    Clark, Kevin
    Yamazaki, Kazuhiko
    INTELLIGENT HUMAN SYSTEMS INTEGRATION 2019, 2019, 903 : 188 - 193
  • [39] Application of Digital Image Based on Machine Learning in Media Art Design
    Wu, Ciguli
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2021, 2021
  • [40] Unraveling the distinction between depression and anxiety: A machine learning exploration of causal relationships
    Wang T.
    Xue C.
    Zhang Z.
    Cheng T.
    Yang G.
    Computers in Biology and Medicine, 2024, 174
12345