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Leverage Mathematics’ Capability to Compress and Generalize as Application of ML Embedding Extraction from LLMs and Its Adaptation in the Automotive Space

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  • First Online:
Machine Learning, Optimization, and Data Science (LOD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14505))

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Abstract

Lately, with the public release of Chat GPT-2,3,4 many people agree that not enough attention given to the power and importance of the machinery that Mathematics brings, especially Discrete and Pure Mathematics. From simple ability to capture the essence, capacity to compress the information to its expertise in grasping the representational structure of entities, objects to projection of text blobs into the unified vector space. Mathematics mechanism to generalize can cover as a foundation in making significant progress in the area of AGI (artificial general intelligence), applications of embedding can lead quickly to very short roll out to production environment the varies of recommendation systems.

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References

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  6. Mac Lane, S.: Categories for the Working Mathematician. https://en.wikipedia.org/wiki/Category_theory.10

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Author information

Authors and Affiliations

  1. UCLA, Los Angeles, USA

    Vera Kalinichenko

Authors
  1. Vera Kalinichenko
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Correspondence to Vera Kalinichenko .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Catania, Italy

    Giuseppe Nicosia

  2. Newcastle University, Newcastle upon Tyne, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  6. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Kalinichenko, V. (2024). Leverage Mathematics’ Capability to Compress and Generalize as Application of ML Embedding Extraction from LLMs and Its Adaptation in the Automotive Space. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14505. Springer, Cham. https://doi.org/10.1007/978-3-031-53969-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-53969-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53968-8

  • Online ISBN: 978-3-031-53969-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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