Artifact Compatibility for Enabling Collaboration in the Artificial Intelligence Ecosystem

被引:2
|
作者
Maksimov, Yuliyan, V [1 ,3 ]
Fricker, Samuel A. [1 ,2 ]
Tutschku, Kurt [3 ]
机构
[1] FHNW Univ Appl Sci & Arts Northwestern Switzerlan, Inst Interact Technol, Windisch, Switzerland
[2] Blekinge Inst Technol, Software Engn Res Lab SERL Sweden, Karlskrona, Sweden
[3] Blekinge Inst Technol, Dept Comp Sci & Engn DIDD, Karlskrona, Sweden
来源
关键词
Compatibility; Licensing; Marketplace; Artificial intelligence; Machine learning; Deep learning;
D O I
10.1007/978-3-030-04840-2_5
中图分类号
F [经济];
学科分类号
02 ;
摘要
Different types of software components and data have to be combined to solve an artificial intelligence challenge. An emerging marketplace for these components will allow for their exchange and distribution. To facilitate and boost the collaboration on the marketplace a solution for finding compatible artifacts is needed. We propose a concept to define compatibility on such a marketplace and suggest appropriate scenarios on how users can interact with it to support the different types of required compatibility. We also propose an initial architecture that derives from and implements the compatibility principles and makes the scenarios feasible. We matured our concept in focus group workshops and interviews with potential marketplace users from industry and academia. The results demonstrate the applicability of the concept in a real-world scenario.
引用
收藏
页码:56 / 71
页数:16
相关论文
共 50 条
  • [11] Artificial Intelligence: Enabling Technology to Empower Society
    Lyu, Yue-Guang
    ENGINEERING, 2020, 6 (03) : 205 - 206
  • [12] Enabling Artificial Intelligence Adoption through Assurance
    Freeman, Laura
    Rahman, Abdul
    Batarseh, Feras A.
    SOCIAL SCIENCES-BASEL, 2021, 10 (09):
  • [13] Artificial intelligence to reduce artifact in cardiac electrophysiological signals
    Ruiperez-Campillo, S.
    Deb, B.
    Feng, R.
    Ganesan, P.
    Tjong, F. V. Y.
    Clopton, P.
    Rogers, A. J.
    Narayan, S. M.
    EUROPEAN HEART JOURNAL, 2022, 43 : 422 - 422
  • [14] Sonographer interaction with artificial intelligence: collaboration or conflict?
    Day, T. G.
    Matthew, J.
    Budd, S.
    Hajnal, J. V.
    Simpson, J. M.
    Razavi, R.
    Kainz, B.
    ULTRASOUND IN OBSTETRICS & GYNECOLOGY, 2023, 62 (02) : 167 - 174
  • [15] Artificial intelligence for liver diseases: The urgency of collaboration
    Cesaretti, Manuela
    Izzo, Alessandro
    Mavrothalassitis, Orestes
    Pellegrino, Roberta Anna
    DIGESTIVE AND LIVER DISEASE, 2024, 56 (06) : 1110 - 1111
  • [16] ARTIFICIAL INTELLIGENCE IN OPEN UNIVERSITY ECOSYSTEM CONTEXT
    Buinytska, Oksana
    Terletska, Tetiana
    Smirnova, Valeriia
    Tiutiunnyk, Anastasiia
    Kovalenko, Iryna
    Hrytseliak, Bohdan
    INFORMATION TECHNOLOGIES AND LEARNING TOOLS, 2025, 105 (01) : 204 - 221
  • [17] An Ecosystem for Deploying Artificial Intelligence in Public Administration
    Karamanou, Areti
    Mangou, Evdokia
    Tarabanis, Konstantinos
    ELECTRONIC GOVERNMENT, EGOV 2023, 2023, 14130 : 192 - 207
  • [18] Memory Technology enabling the next Artificial Intelligence revolution
    Godse, Ranjana
    McPadden, Adam
    Patel, Vipin
    Yoon, Jung
    2018 IEEE NANOTECHNOLOGY SYMPOSIUM (ANTS), 2018,
  • [19] Towards Enabling Trusted Artificial Intelligence via Blockchain
    Sarpatwar, Kanthi
    Vaculin, Roman
    Min, Hong
    Su, Gong
    Heath, Terry
    Ganapavarapu, Giridhar
    Dillenberger, Donna
    POLICY-BASED AUTONOMIC DATA GOVERNANCE (PADG 2018), 2019, 11550 : 137 - 153
  • [20] Enabling artificial intelligence in high acuity medical environments
    Kasparick, Martin
    Andersen, Bjoern
    Franke, Stefan
    Rockstroh, Max
    Golatowski, Frank
    Timmermann, Dirk
    Ingenerf, Josef
    Neumuth, Thomas
    MINIMALLY INVASIVE THERAPY & ALLIED TECHNOLOGIES, 2019, 28 (02) : 120 - 126