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.

Transcript

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
   

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

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

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4. (C)Yoshiaki FUKAMI
   

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5. (C)Yoshiaki FUKAMI
   •Cheap sensors
   •Cheap network
   •Cheap processors
   Photo by Louis Reed on Unsplash
   

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

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7. (C)Yoshiaki FUKAMI
   

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8. (C)Yoshiaki FUKAMI
   

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9. (C)Yoshiaki FUKAMI
   Context
   

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

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

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

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13. (C)Yoshiaki FUKAMI
    Photo by Mufid Majnun on Unsplash
    Data generated
    with non-
    medical device
    in hospitals
    

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14. Better to be
    than not
    (C)Yoshiaki FUKAMI
    

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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
    

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

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17. SEKI model (Nonaka and Takeuchi, 1995)
    (C)Yoshiaki FUKAMI
    Socialization Externalization
    Internalization Combination
    Tacit
    Knowledge
    Tacit
    Knowledge
    Explicit
    Knowledge
    Explicit
    Knowledge
    

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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
    

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19. Feature of ordinary data
    • Unstructured
    • Insufficient
    • Lack of interoperability
    (C)Yoshiaki FUKAMI
    

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

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

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22. (C)Yoshiaki FUKAMI
    

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23. (C)Yoshiaki FUKAMI
    cyber
    colonization
    

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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
    

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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
    

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26. Thank you.
    Yoshiaki FUKAMI:
    yoshiaki@fukami-lab.com
    

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