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人間と人工知能の協働

 人間と人工知能の協働

2023年1月20日(金)「高校生と大学生のための金曜特別講座」

Yukino Baba
PRO

January 20, 2023
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  1. [email protected]

    @babalablab
    ߴߍੜͱେֶੜͷͨΊͷ༵ۚಛผߨ࠲

    ਓؒͱਓ޻஌ೳͷڠಇ
    ೥݄೔

    ౦ژେֶڭཆֶ෦ֶࡍՊֶՊഅ৔ઇ೫
    https://fontawesome.com https://openmoji.org

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  2. 2
    അ৔ઇ೫ʢ͹͹Ώ͖ͷʣ
    ౦ژେֶ૯߹จԽݚڀՊ޿ҬՊֶઐ߈ɾ।ڭत
    ത࢜ʢ৘ใཧ޻ֶʣ
    ઐ໳˔ਓ޻஌ೳ ػցֶश
    ݚڀτϐοΫ˔)VNBO$PNQVUBUJPO

    ɹɹɹɹɹɹɹ)VNBO"*$PMMBCPSBUJPO
    ɹɹɹɹɹɹɹ)VNBOJOUIF-PPQ.BDIJOF-FBSOJOH
    ஶॻ˔ʮώϡʔϚϯίϯϐϡςʔγϣϯͱΫϥ΢υιʔγϯάʯ
    ɹɹɹɹʢߨஊࣾαΠΤϯςΟϑΟοΫʣ

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  3. Ϣʔβ͕ೖྗͨ͠จষʢϓϩϯϓτʣ͔Βը૾Λੜ੒͢Δ"*͕ొ৔

    ʢ%"--w& .JEKPVSOFZ 4UBCMF%J
    ff
    VTJPOͳͲʣ
    ਓ޻஌ೳʹͰ͖Δ͜ͱ͕૿͍͑ͯΔ
    3
    https://huggingface.co/spaces/stabilityai/stable-di
    ff
    usion
    https://huggingface.co/spaces/stabilityai/stable-di
    ff
    usion

    Λར༻ͯ͠ߨࢣ͕࡞੒

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  4. ਓ޻஌ೳʹͰ͖Δ͜ͱ͕૿͍͑ͯΔ
    4
    ྲྀெͳจষͰ࣭໰ʹ౴͑ͯ͘ΕΔର࿩"*͕ొ৔ʢ$IBU(15ͳͲʣ
    https://chat.openai.com/ Λར༻ͯ͠ߨࢣ͕࡞੒

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  5. ਓ޻஌ೳʹͰ͖Δ͜ͱ͕૿͍͑ͯΔ
    5
    $IBU(15͸1ZUIPOίʔυ΋ग़ྗͰ͖Δ
    https://chat.openai.com/ Λར༻ͯ͠ߨࢣ͕࡞੒

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  6. ͍·ͷਓ޻஌ೳ͸໌Β͔ͳؒҧ͍Λ͢Δ͜ͱ͕͋Δ
    6
    https://twitter.com/mahimahi/status/1599384548571516929

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  7. ͍·ͷਓ޻஌ೳ͸ਓ͕ؒ͠ͳ͍ؒҧ͍Λ͢Δ͜ͱ͕͋Δ
    7
    https://www.bbc.com/news/technology-33347866
    (PPHMF1IPUPͷ"*͕

    ΞϑϦΧܥͷஉঁʹޡͬͯ
    ʮΰϦϥʯͱλά෇͚

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  8. ͍·ͷਓ޻஌ೳ͸ภͬͨ݁ՌΛฦ͢͜ͱ͕͋Δ
    8
    https://twitter.com/favorite_Bonsai/status/1564754888386543616
    ʮαʔϞϯϥϯʢࡪͷ૎্ʣʯͷ
    ࣮ࡍͷࣸਅ
    https://www.worldatlas.com/articles/best-places-to-see-the-salmon-run.html

    View Slide

  9. ͍·ͷਓ޻஌ೳ͸ภͬͨ݁ՌΛฦ͢͜ͱ͕͋Δ
    9
    https://twitter.com/zatazata/status/1572376514364346370

    View Slide

  10. ͍·ͷਓ޻஌ೳ͸ෳࡶͳจষͷཧղ͕ۤखͰ͋Δ
    10
    αϦʔ͸ΧΰΛ͍࣋ͬͯ·͢ɻΞϯ͸ശΛ͍࣋ͬͯ·͢ɻαϦʔ͸
    ϏʔۄΛ͍࣋ͬͯ·͢ɻαϦʔ͸ϏʔۄΛࣗ෼ͷΧΰʹ࢓෣͍·͠
    ͨɻαϦʔ͸֎ʹग़͔͚·ͨ͠ɻΞϯ͸ɺΧΰ͔ΒϏʔۄΛऔΓग़
    ͠ɺࣗ෼ͷശʹ࢓෣͍·ͨ͠ɻαϦʔ͕໭͖ͬͯ·ͨ͠ɻαϦʔ͸
    ϏʔۄͰ༡΅͏ͱ͍ͯ͠·͢ɻͯ͞ɺαϦʔ͸Ͳ͜Λ୳͢Ͱ͠ΐ͏
    ͔ɻ
    S. Baron-Cohen et al.: Does the Autistic Child have a “Theory of mind” ? Cognition, Vol. 21, Issue 1, 1985. ೔ຊޠ༁͸ߨࢣʹΑΔɻ

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  11. ͍·ͷਓ޻஌ೳ͸ෳࡶͳจষͷཧղ͕ۤखͰ͋Δ
    11
    ࣭໰จͷग़యɿS. Baron-Cohen et al.: Does the Autistic Child have a “Theory of mind” ? Cognition, Vol. 21, Issue 1, 1985.


    ճ౴จ͸https://beta.openai.com/playground Λར༻ͯ͠ߨࢣ͕࡞੒ɻ

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  12. ྙཧతͳ໰୊͸ਓ޻஌ೳʹ͸ܾΊͯ΄͘͠ͳ͍
    12
    https://www.moralmachine.net

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  13. 13
    ώϡʔϚϯίϯϐϡςʔγϣϯ
    ਓؒͱਓ޻஌ೳͷڠಇʹΑΓ

    ͲͪΒ͔Ұํ͚ͩͰ͸೉͍͠໰୊Λղܾ͢Δ

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  14. 14
    ௨ৗͷਓ޻஌ೳγεςϜ ώϡʔϚϯίϯϐϡςʔγϣϯ
    ਓؒΛܭࢉࢿݯͱͯ͠औΓࠐΉ

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  15. 7J[8J[ࢹ֮ো͕͍ऀͷ࣭໰Ԡ౴γεςϜ
    15
    -tap to take a photo.
    -tap to begin recording
    your question and again to stop.


    side,
    User ?
    Web Server -
    Database -
    Local Client
    Remote Services and Worker Interface
    4UFQϢʔβ͕࣭໰Λ౤ߘ
    ίʔϯͷ؈͸ͲΕʁ
    4UFQγεςϜ಺෦ͷਓ͕ؒճ౴
    Ұ൪ӈͷ؈Ͱ͢
    J. P. Bigham et al.: VizWiz: Nearly Real-time Answers to Visual Questions. In Proceedings of the 23rd annual ACM symposium on User
    interface software and technology (UIST), 2010.

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  16. ;FOTPSTਓؒΛ࢖ͬͨ؂ࢹγεςϜ
    16
    https://www.youtube.com/watch?v=aYHzG9uXQ6k

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  17. ;FOTPSTਓؒΛ࢖ͬͨ؂ࢹγεςϜ
    17
    G Laput et al. Zensors: Adaptive, Rapidly Deployable, Human-Intelligent Sensor Feeds. In Proceedings of the 33rd Annual ACM Conference on Human
    Factors in Computing Systems (CHI), 2015.
    How many glasses need a re
    fi
    ll?
    or… I can’t Tell
    https://www.dreamstime.com/empty-white-
    wine-glasses-table-restaurant-bar-setting-
    close-up-alcohol-image215682647
    ෼͓͖ʹਓؒʹը૾Λૹ৴ͯ͠

    ໰͍߹ΘͤΔ

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  18. ;FOTPSTਓؒΛ࢖ͬͨ؂ࢹγεςϜ
    18
    ਓؒͷճ౴Λ༻͍ͯ

    ਓ޻஌ೳΛֶश



    े෼ͳਫ਼౓ʹୡͨ͠Β

    ਓؒͰ͸ͳ͘ਓ޻஌ೳΛར༻
    G. Laput et al. Zensors: Adaptive, Rapidly Deployable, Human-Intelligent Sensor Feeds. In Proceedings of the 33rd Annual ACM Conference on Human
    Factors in Computing Systems (CHI), 2015.

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  19. ෆಛఆଟ਺ͷਓʹ୯७࡞ۀΛൃ஫Ͱ͖Δɼ

    ΦϯϥΠϯϓϥοτϑΥʔϜʮΫϥ΢υιʔγϯάʯΛ࢖ͬͯ

    ώϡʔϚϯίϯϐϡςʔγϣϯʹࢀՃ͢ΔਓؒΛूΊΔ
    Ϋϥ΢υιʔγϯάʹΑΓࢀՃऀΛืΔ
    19
    Ϋϥ΢υιʔγϯά
    "NB[PO.FDIBOJDBM5VSL

    ϥϯαʔζͳͲ
    ࡞ۀΛड஫
    ใुΛ੥ٻ
    ࡞ۀΛൃ஫
    අ༻ͷࢧ෷͍

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  20. ࡞ۀը໘
    Ϋϥ΢υιʔγϯάʹΑΓࢀՃऀΛืΔ
    20
    Ϩγʔτͷॻ͖ى͜͠
    ʢʣ
    ֆըͷײ৘ϥϕϧ෇༩
    ʢʣ
    "NB[PO.FDIBOJDBM5VSLͷը໘ྫ
    ґཔҰཡ
    https://www.mturk.com https://www.mturk.com

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  21. Ϋϥ΢υιʔγϯάʹΑΓࢀՃऀΛืΔ
    21
    "NB[PO.FDIBOJDBM5VSLͰ͸"1*͕ఏڙ͞Ε͍ͯͯ

    ਓؒ΁ͷ໰͍߹ΘͤΛࣗಈੜ੒Ͱ͖Δ
    https://qiita.com/ssmsaito/items/c0c514d76abcd532b59e

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  22. ώϡʔϚϯίϯϐϡςʔγϣϯͷ՝୊
    22
    ҙࢥͷ͋ΔਓؒΛώϡʔϚϯίϯϐϡςʔγϣϯʹࢀՃͤ͞Δʹ͸

    ద੾ͳಈػ͚͕ͮඞཁ
    ᶃಈػ͚ͮ
    ਓؒ͸ʮৗʹɾ୭Ͱ΋ʯਖ਼͍͠౴͑Λฦ͢ͱ͸ݶΒͳ͍ͨΊ

    ඼࣭Λอূ͢ΔͨΊͷ޻෉͕ඞཁ
    ᶄ඼࣭อূ

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  23. SF$"15$)"ɿॻ੶ͷจࣈೝࣝγεςϜʹਓؒΛ૊ΈࠐΉ
    23
    morning
    4UFQ݁Ռ͕ෆҰகͷͱ͖ʹਓؒʹ໰͍߹ΘͤΔ
    moroiog
    morpipg
    4UFQॻ੶தͷจࣈΛ̎ͭͷਓ޻஌ೳγεςϜʹೝࣝͤ͞Δ
    L. von Ahn et al.: reCAPTCHA: Human-Based Character Recognition via Web Security Measures. Science, Vol. 321, Issue 589, 2008.
    ਓ޻஌ೳʹ͸೉͍͠จࣈΛਓؒʹೝࣝͤ͞Δ

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  24. ΞΫηεݖΛใुͱͯ͠ਓؒΛจࣈೝࣝ࡞ۀʹࢀՃͤ͞Δ
    24
    ΞΫηεΛ

    ڐՄͯ͠

    ͍ͩ͘͞ʂ
    ਓؒͷΞΫηε͸ڐՄ͠·͢ɽ

    ϘοτͷΞΫηε͸ڐՄ͠·ͤΜɽ
    ͋ͳ͕ͨਓؒͩͱূ໌͢ΔͨΊʹ

    ͜ͷจࣈΛೝࣝ͠ͳ͍͞

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  25. ʮਓ͔ؒϘοτ͔ͷ൑ఆʯͱʮॻ੶ͷจࣈೝࣝʯΛಉ࣌ʹߦ͏
    25
    ᶃਓ͔ؒϘοτ͔ͷ൑ఆɹᶄॻ੶ͷจࣈೝࣝ
    SF$"15$)"ͷ໨త
    Type the word Type the word
    reCAPTCHA
    ೋͭͷจࣈΛೝࣝ͢ΔΑ͏ʹࢦࣔ͢Δ
    ˞ͲͪΒ͕ਖ਼ղط஌͔͸఻͑ͳ͍
    ਖ਼ղط஌ ਖ਼ղະ஌
    L. von Ahn et al.: reCAPTCHA: Human-Based Character Recognition via
    Web Security Measures. Science, Vol. 321, Issue 589, 2008.
    ᶄʮਓؒɹʯͷ݁Ռ͚ͩΛ

    ɹೝࣝ݁Ռͱͯ͠࠾༻
    ᶃɹਖ਼ղͳΒˠਓؒ

    ɹෆਖ਼ղͳΒˠϘοτ

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  26. ඼࣭อূͷͨΊʹʮਓؒʯͷճ౴͚ͩ࠾༻͢Δ
    26
    overlooks morning
    reCAPTCHA
    ਖ਼ղط஌ ਖ਼ղະ஌
    L. von Ahn et al.: reCAPTCHA: Human-Based Character Recognition via Web Security Measures. Science, Vol. 321, Issue 589, 2008.
    ਖ਼ղͳͷͰ

    ʮਓؒɹʯͱ൑ఆ
    ʮਓؒɹʯͷೝࣝ݁ՌͳͷͰ

    ࠾༻⭕

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  27. ʮϘοτʯͷճ౴͸࠾༻͠ͳ͍
    27
    overfooRs morpipg
    reCAPTCHA
    ਖ਼ղط஌ ਖ਼ղະ஌
    L. von Ahn et al.: reCAPTCHA: Human-Based Character Recognition via Web Security Measures. Science, Vol. 321, Issue 589, 2008.
    ෆਖ਼ղͳͷͰ

    ʮϘοτɹʯͱ൑ఆ
    ʮϘοτɹʯͷճ౴ͳͷͰ

    ෆ࠾༻❌

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  28. ˔ ʮਓؒʯͳΒ͹ਖ਼ղະ஌ͷ໰୊ʹ΋ਖ਼͘͠౴͑ΔՄೳੑ͕ߴ͍
    ˔ Ұਓ͚ͩʹ໰͍߹ΘͤΔͱɼͦͷਓ͕ؒҧ͑ΔՄೳੑ͕͋Δ
    ˔ ಉ͡จࣈྻΛෳ਺ਓʹ໰͍߹ΘͤɼҰఆ਺ͷճ౴͕Ұகͨ͠ͱ͖͚ͩ

    ࠷ऴతͳೝࣝ݁Ռͱͯ͠࠾༻͢Δʢฒྻ໰߹ͤʣ
    ඼࣭อূͷͨΊʹಉ͡จࣈྻΛෳ਺ਓʹ໰͍߹ΘͤΔ
    28
    ਓͷճ౴͕Ұகͨ͠ͷͰ

    morning

    Λೝࣝ݁Ռͱͯ͠࠾༻
    morning
    morning morping morning

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  29. &41ήʔϜɿը૾΁ͷϥϕϧ෇͚ΛήʔϜԽ
    29
    ਓ޻஌ೳͷֶशʹ༻͍ΔͨΊɼը૾ͷλάΛਓ͔ؒΒूΊΔ
    ˔ ૬खϓϨΠϠʔͱಉ͡λάΛ౴
    ͑ͨΒϙΠϯτ͕΋Β͑Δ

    ʮήʔϜʯʹ͢Δ͜ͱͰ

    ࢀՃΛଅ͢
    ˔ σλϥϝͳճ౴ͷ๷ࢭʹ΋

    ໾ཱͭ
    L. von Ahn et al.: Designing games with a Purpose. Communication of the ACM, Vol.51, Issue 8, 2008

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  30. ˔ λϯύΫ࣭ͷߏ଄༧ଌ͸ͦͷػೳΛ஌Δͷʹॏཁ͕ͩਓ޻஌ೳʹ͸ࠔ೉
    ˔ ήʔϜʹ͢Δ͜ͱͰେྔͷਓؒʹղ͔ͤͨͱ͜Ζ

    ೥Ҏ্ະղܾͩͬͨ໰୊ΛϓϨΠϠʔୡ͕೔Ҏ಺ʹղ͍ͨ
    'PME*UλϯύΫ࣭ͷߏ଄༧ଌΛήʔϜԽ͠ਓؒʹղ͔ͤΔ
    30
    ཱମߏ଄
    ഑ྻ
    https://fold.it/


    S. Cooper et al.: Predicting Protein Structures with a Multiplayer Online Game. Nature, Vol. 466, No. 7307, 2010.
    ϓϨΠϠʔ͸ߴείΞΛ

    ૂ͍ߏ଄ΛมԽͤ͞Δ

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  31. 'PME*UλϯύΫ࣭ͷߏ଄༧ଌΛήʔϜԽ͠ਓؒʹղ͔ͤΔ
    31
    https://www.youtube.com/watch?v=DvYFjo3vC-k

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  32. ˔ ໺ௗͷ؍࡯ه࿥Λऩू͢ΔͷʹਓؒΛ׆༻
    ˔ ࢀՃऀ͸ࣗൃతʹ໺ௗΛ؍࡯͠छྨɾ਺ɾࣸਅɾԻ੠౳Λه࿥͢Δ
    ˔ ࢀՃऀʹ޿ൣғΛΧόʔͤ͞ΔͨΊʹ؍࡯৔ॴΛਪન͢Δ
    F#JSE໺ௗѪ޷ՈΛར༻ͯ͠໺ௗͷσʔλΛूΊΔ
    32
    https://ebird.org/home

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  33. ଟ਺ܾ͸શһʹಉ͡ॏΈΛ༩͑Δ͕ɼਖ਼͍͠౴͑ΛಘΔͨΊʹ͸

    ৴པੑͷߴ͍ਓʹߴ͍ॏΈΛ༩͍͑ͨ
    ฒྻ໰߹ͤʹΑΔ඼࣭อূ
    33
    ࣸਅʹௗ͕ࣸͬͯ·͔͢ʁ
    YES NO
    YES YES YES
    ଟ਺ܾͷ݁Ռ͸YES
    ࣸਅʹௗ͕ࣸͬͯ·͔͢ʁ
    YES YES NO
    ଟ਺ܾͷ݁Ռ͸YES
    YES NO

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  34. ੜెͷճ౴͚͔ͩΒࢼݧͷਖ਼ղΛ༧ଌ͢ΔΑ͏ͳ΋ͷ
    ճ౴͔Β֤ࣗͷ৴པੑΛਪఆ֤͠໰୊ͷਖ਼ղΛ༧ଌ͢Δ
    34
    ਖ਼ղ
    ໰୊
    NO YES YES YES
    YES YES NO YES
    YES YES YES NO
    ?
    ?
    ?
    “Is a bird in

    the picture?"
    https://commons.wikimedia.org/wiki/File:American_
    fl
    amingo_(Phoenicopterus_ruber).JPG

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  35. ti
    ਖ਼ղ
    YES
    ti
    =
    NO
    ti
    =
    yij
    βj
    ճ౴
    ճ౴Ϟσϧʢ໰୊ ʹର͢Δճ౴ऀ ͷճ౴ʣ
    i j
    αj
    Pr[yij
    ∣ ti
    = 1] = αyij
    j
    (1 − αj
    )(1−yij
    )
    Pr[yij
    ∣ ti
    = 0] = β(1−yij
    )
    j
    (1 − βj
    )yij
    ճ౴ϞσϧΛߏஙͯ͠ਖ਼ղ༧ଌʹ༻͍Δ
    35
    ɿճ౴ऀ ͕ਖ਼ղ͕YESͷ໰୊ʹYESͱ౴͑Δ֬཰
    αj j
    ɿճ౴ऀ ͕ਖ਼ղ͕NOͷ໰୊ʹNOͱ౴͑Δ֬཰
    βj j
    ճ౴ऀͷ৴པੑύϥϝʔλʢࠞಉߦྻʣ
    ճ౴
    YES NO


    ղ
    YES
    NO
    αj
    βj
    1 − αj
    1 − βj
    ࠞಉߦྻ
    A. P. Dawid and A. M. Skene: Maximum likelihood estimation of observer error-rates using the EM algorithm. Journal of the Royal Statistical Society,
    Series C (Applied Statistics), 1979.

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  36. ˔ ଞਓͷ౴͑Λ࢖͍ղ͔ͤΔ͜ͱͰਖ਼͍͠౴͑ΛҾ͖ग़͢
    ˙ 4PZMFOUਓؒΛ࢖ͬͨจষߍਖ਼γεςϜ
    'JOE໰୊ͷ͋ΔՕॴͷݕग़
    'JYߍਖ਼ͷ࣮ࢪ
    7FSJGZߍਖ਼͕ਖ਼͍͔֬͠ೝ
    ௚ྻ໰߹ͤʹΑΔ඼࣭อূ
    36
    When the crowd is finished, Soylent calls out the edited
    sections with a purple dashed underline. If the user clicks
    on the error, a drop-down menu explains the problem and
    offers a list of alternatives. By clicking on the desired alter-
    native, the user replaces the incorrect text with an option of
    Figure 2. Crowdproof is a human-augmented proofreader.
    The drop-down explains the problem (blue title) and suggests
    fixes (gold selection).
    M. S. Bernstein et al.: Soylent: a Word Processor with a Crowd Inside. Communications of the ACM, Vol. 58, Issue 8, 2015.
    Find Fix Verify

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  37. ˔ %"31"3FE#BMMPPO$IBMMFOHF੺͍෩ધΛ୳͢ίϯςετ
    ˙ શถՕॴʹ੺͍෩ધ🎈Λઃஔ
    ˙ ࠷ॳʹશͯͷ੺͍෩ધΛݟ͚ͭͨνʔϜʹ৆͕ۚࢧ෷ΘΕΔ
    ˙ ༏উνʔϜ͸ʮ෩ધΛݟ͚ͭͦ͏ͳਓʯΛଞऀ͔ΒͷਪનͰൃݟ
    ਪનʹΑΔ඼࣭อূ
    37
    contributed articles
    platform for viral collaboration that
    used recursive incentives to align the
    public’s interest with the goal of win-
    ning the Challenge. This approach was
    inspired by the work of Peter S. Dodds
    et al.5 that found that success in us-
    ing social networks to tackle widely
    distributed search problems depends
    on individual incentives. The work of
    Mason and Watts7 also informed the
    use of financial incentives to motivate
    crowdsourcing productivity.
    The MIT team’s winning strategy
    was to use the prize money as a finan-
    cial incentive structure rewarding not
    only the people who correctly located
    balloons but also those connecting
    the finder to the MIT team. Should the
    team win, they would allocate $4,000
    in prize money to each balloon. They
    promised $2,000 per balloon to the
    Figure 1. Locations in the DARPA Red Balloon Challenge.
    Figure 2. Example recursive incentive-structure process for the MIT team.
    J. C. Tang et al.: Re
    fl
    ecting on the DARPA Red Balloon Challenge. Communications of the ACM, Vol. 54, Issue 11, 2011.

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  38. ඃਪનऀ͕෩ધΛൃݟͨ͠Βਪનऀʹ΋ใु͕෼഑͞ΕΔΑ͏ʹઃܭ
    ਪનʹΑΔ඼࣭อূ
    38
    ਪન
    ਪન
    ਪન
    ෩ધΛൃݟʂ
    ͷใु
    ͷใु
    ෩ધΛൃݟʂ
    ͷใु
    ͷใु ͷใु

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  39. ˔ ώϡʔϚϯίϯϐϡςʔγϣϯ͸ɼਓ޻஌ೳͱਓؒͷڠಇʹΑΓ

    ͜ͱͰͲͪΒ͔Ұํ͚ͩͰ͸ղܾ͕೉͍͠໰୊Λղܾ͢Δ
    ˔ ώϡʔϚϯίϯϐϡςʔγϣϯͷ՝୊͸ಈػ͚ͮͱ඼࣭อূ
    ˙ ಈػ͚ͮ

    ήʔϜԽ΍ɼΞΫηε੍ޚɾ޷ح৺ɾۚમͳͲΛใुʹ͢Δ͜ͱͰ
    ਓؒͷࢀՃΛଅ͢
    ˙ ඼࣭อূ

    ௚ྻ໰߹ͤɾฒྻ໰߹ͤʹΑΓෳ਺ਓΛ૊Έ߹ΘͤͨΓɼ

    ਪનʹΑΓ৴པͰ͖ΔਓΛݟ͚ͭͯɼਖ਼͍͠ճ౴ͷ֫ಘΛ໨ࢦ͢
    ·ͱΊ
    39

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