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Mathematical and Statistical Frameworks Fostering Advances in AI Systems and Computing

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Computational Science and Its Applications – ICCSA 2024 Workshops (ICCSA 2024)

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Abstract

The rapid expansion of artificial intelligence (AI) areas and applications has become an everyday social reality and the development of computing methods and of technology, in order to accomplish for the respective needs, emerge as one of the biggest challenges of this century. However, a strong background in Mathematics and Statistical fundamentals is crucial to foster the robustness of AI Systems and to attain for the correspondent generalization capabilities. Thus, recognizing the importance and benefits of integrate multiple mathematical methodologies that can leverage the unique strengths of each to create synergistic effects, is here discussed. In the literature it is possible to find diverse connections of Mathematical and Statistical frameworks with AI Systems and Computing. For illustration: Algebra - can be used to manipulate data in high-dimensional spaces; Statistical Analysis - helps on providing confidence in decision-making; Linear Regression- can be used on data modelling and accuracy assessment; and Bayesian statistics - can be used in probabilistic programming for AI applications. This work particularly highlights the evolving role of Hadamard Matrices and Coding Theory, illustrating their synergy with AI Systems and Computing, and how such synergy is driving innovation in various fields and approaching the new reality of Quantic paradigms.

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Acknowledgements

The first author was supported by Portuguese funds The Portuguese Foundation for Science and Technology (FCT), through the project UIDB/Multi/00006/2020.

The second author was supported by Portuguese funds The Portuguese Foundation for Science and Technology (FCT), through the Center for Computational and Stochastic Mathematics (CEMAT), University of Lisbon, Portugal, project UIDB/Multi/04621/2020, DOI: (https://doi.org/10.54499/UIDB/04621/2020) and through the Center of Naval Research (CINAV), Naval Academy, Portugal.

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Authors and Affiliations

  1. Departamento de Ciências e Tecnologia, Universidade Aberta, 1269-001, Lisbon, Portugal

    Teresa A. Oliveira

  2. CEAUL, Faculdade de Ciências, Universidade de Lisboa, 1649-014, Lisbon, Portugal

    Teresa A. Oliveira

  3. CINAV, Center of Naval Research, Naval Academy, Portuguese Navy, 2810-001, Almada, Portugal

    Maria Filomena Teodoro

  4. CEMAT, Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, Lisbon University, 1048-001, Lisbon, Portugal

    Maria Filomena Teodoro

Authors
  1. Teresa A. Oliveira
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  2. Maria Filomena Teodoro
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Correspondence to Teresa A. Oliveira .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. School of Engineering, University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Department of Civil and Environmental Engineering and Architecture, University of Cagliari, Cagliari, Italy

    Chiara Garau

  4. Faculty of Information Technology, Clayton Campus, Monash University, Clayton, VIC, Australia

    David Taniar

  5. Algoritmi Research Centre, University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  6. Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy

    Maria Noelia Faginas Lago

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Oliveira, T.A., Teodoro, M.F. (2024). Mathematical and Statistical Frameworks Fostering Advances in AI Systems and Computing. In: Gervasi, O., Murgante, B., Garau, C., Taniar, D., C. Rocha, A.M.A., Faginas Lago, M.N. (eds) Computational Science and Its Applications – ICCSA 2024 Workshops. ICCSA 2024. Lecture Notes in Computer Science, vol 14816. Springer, Cham. https://doi.org/10.1007/978-3-031-65223-3_16

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  • DOI: https://doi.org/10.1007/978-3-031-65223-3_16

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