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Knowledge Production – The Limits of Big Data and AI

Knowledge Production – The Limits of Big Data and AI

Presentation slide for Knowledge Production in a Data Driven Society of lecture series; MWS Web Forum Series 'The Digital Transformation' on September 23rd, 2021 online.
https://www.dijtokyo.org/event/knowledge-production-in-a-data-driven-society/

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The presentation will discuss knowledge production in the context of platform business models, big data and artificial intelligence. The cost of generating, storing, and processing data has dropped significantly. However, with such technological advancement, it is possible to realize progress only in restricted areas. Three technological and social elements such as common data architecture, advanced trust framework and ethics by design, are necessary for sustainable knowledge production to realize a data driven society.

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Yoshiaki Fukami

September 25, 2021
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  1. Knowledge Production The Limits of Big Data and AI Knowledge

    Production in a Data Driven Society MWS Web Forum Series ‘The Digital Transformation’ Yoshiaki FUKAMI, Ph. D Gakushuin University/ Keio University yoshiakiˏfukami-lab.com
  2. •Knowledge Production in a Data Driven Society (C)Yoshiaki FUKAMI

  3. Out of scope • Privacy protection • Intellectual property (C)Yoshiaki

    FUKAMI
  4. (C)Yoshiaki FUKAMI

  5. (C)Yoshiaki FUKAMI •Cheap sensors •Cheap network •Cheap processors Photo by

    Louis Reed on Unsplash
  6. (C)Yoshiaki FUKAMI AI Photo by Lukas on Unsplash

  7. (C)Yoshiaki FUKAMI

  8. (C)Yoshiaki FUKAMI

  9. (C)Yoshiaki FUKAMI Context

  10. •Knowledge Production in a Data Driven Society (C)Yoshiaki FUKAMI

  11. •Knowledge Production in a Data Driven Society (C)Yoshiaki FUKAMI

  12. 2020೥౓ֶशӃେֶܦࡁֶ෦ܦӦֶՊ ਂݟՅ໌୲౰तۀࢿྉ •95.0% •2021/Sep/23 10:09

  13. (C)Yoshiaki FUKAMI Photo by Mufid Majnun on Unsplash Data generated

    with non- medical device in hospitals
  14. Better to be than not (C)Yoshiaki FUKAMI

  15. Information necessary to utilize • Common (meta) data model of

    context –Model of devices • Measuring method • Measurement precision –Procedure of data generation • Time • Method • Etc. (C)Yoshiaki FUKAMI
  16. •Knowledge Production in a Data Driven Society (C)Yoshiaki FUKAMI

  17. SEKI model (Nonaka and Takeuchi, 1995) (C)Yoshiaki FUKAMI Socialization Externalization

    Internalization Combination Tacit Knowledge Tacit Knowledge Explicit Knowledge Explicit Knowledge
  18. Prejudice for digital data and knowledge • Is digital data

    explicit knowledge? • It may cost more to convert tacit data to explicit knowledge than human’s one. (C)Yoshiaki FUKAMI
  19. Feature of ordinary data • Unstructured • Insufficient • Lack

    of interoperability (C)Yoshiaki FUKAMI
  20. (C)Yoshiaki FUKAMI Bias Photo by Paul Felberbauer on Unsplash

  21. Law of large numbers (C)Yoshiaki FUKAMI We cannot believe

  22. (C)Yoshiaki FUKAMI

  23. (C)Yoshiaki FUKAMI cyber colonization

  24. •Lack of common data model for recognizing context •Bias in

    data generation, sampling and algorism development •Inequality of data and skilled human resource (C)Yoshiaki FUKAMI
  25. Enabling Environment of Knowledge Production • Pursuit for More Exhaustive

    and Accessible Data Set. • Introducing Broadly Accepted Data Model • Exhaustive Data for Eliminating Bias • Control of Risks Associated with Data Provision • Global Distribution of Human Resources for Utilizing Knowledge Generation (C)Yoshiaki FUKAMI
  26. Thank you. Yoshiaki FUKAMI: yoshiaki@fukami-lab.com