Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
SECIモデルを誤解しよう w/ @m_seki
Search
Yasunobu Kawaguchi
PRO
February 08, 2012
Programming
6
1.2k
SECIモデルを誤解しよう w/ @m_seki
@m_seki @kawaguti in DevSumi 2012 OpenJam
Yasunobu Kawaguchi
PRO
February 08, 2012
Tweet
Share
More Decks by Yasunobu Kawaguchi
See All by Yasunobu Kawaguchi
Git in Team
kawaguti
PRO
4
620
from Sakichi Toyoda to Agile
kawaguti
PRO
2
160
Agile PBL at New Grads Trainings
kawaguti
PRO
1
1.4k
Last 2 Weeks on PBL
kawaguti
PRO
1
81
Bridging gaps between skills and ideas
kawaguti
PRO
1
88
Definition of Done
kawaguti
PRO
6
650
Nonaka Sensei
kawaguti
PRO
5
1.5k
Ninno LT
kawaguti
PRO
1
230
大人の学び - マイクの持ち方について
kawaguti
PRO
6
1.1k
Other Decks in Programming
See All in Programming
LLM Observabilityによる 対話型音声AIアプリケーションの安定運用
gekko0114
2
380
[AI Engineering Summit Tokyo 2025] LLMは計画業務のゲームチェンジャーか? 最適化業務における活⽤の可能性と限界
terryu16
2
390
高速開発のためのコード整理術
sutetotanuki
1
230
AIで開発はどれくらい加速したのか?AIエージェントによるコード生成を、現場の評価と研究開発の評価の両面からdeep diveしてみる
daisuketakeda
1
830
AI Agent Tool のためのバックエンドアーキテクチャを考える #encraft
izumin5210
6
1.7k
TerraformとStrands AgentsでAmazon Bedrock AgentCoreのSSO認証付きエージェントを量産しよう!
neruneruo
4
2.5k
生成AI時代を勝ち抜くエンジニア組織マネジメント
coconala_engineer
0
40k
SQL Server 2025 LT
odashinsuke
0
190
rack-attack gemによるリクエスト制限の失敗と学び
pndcat
0
230
Kotlin Multiplatform Meetup - Compose Multiplatform 외부 의존성 아키텍처 설계부터 운영까지
wisemuji
0
180
CSC307 Lecture 05
javiergs
PRO
0
480
なぜSQLはAIぽく見えるのか/why does SQL look AI like
florets1
0
320
Featured
See All Featured
Gemini Prompt Engineering: Practical Techniques for Tangible AI Outcomes
mfonobong
2
260
HU Berlin: Industrial-Strength Natural Language Processing with spaCy and Prodigy
inesmontani
PRO
0
140
Thoughts on Productivity
jonyablonski
74
5k
Context Engineering - Making Every Token Count
addyosmani
9
620
I Don’t Have Time: Getting Over the Fear to Launch Your Podcast
jcasabona
34
2.6k
Technical Leadership for Architectural Decision Making
baasie
1
220
How GitHub (no longer) Works
holman
316
140k
Build The Right Thing And Hit Your Dates
maggiecrowley
38
3k
Mobile First: as difficult as doing things right
swwweet
225
10k
Groundhog Day: Seeking Process in Gaming for Health
codingconduct
0
76
What's in a price? How to price your products and services
michaelherold
247
13k
Measuring & Analyzing Core Web Vitals
bluesmoon
9
740
Transcript
දग़ԽޡղΛظͯ͠ΔΑ Ծઆ @kawagutiͱ@m_sekiʹΑΔSECIϞσϧͷޡղʹΑΔ৽ͨͳ ޡղͷ࣮ྫ
ͦ͏͍͑Ұࡢ͘Β͍ޡ ղͨ͠ SECIϞσϧͷεύΠϥϧݸਓؒͰ͓͖Δ͔Β৫͔Ͳ ͏͔Ͳ͏Ͱ͍͍Μ͡ΌͶʁ
The dRuby Book amazonͰ༧now
ࣝͷ ͕ࣝΘΔͱͳʹ͔ 100%ͷͬͯͳΜͩΖ͏ ͦΜͳͷ͋Μͷ͔ʁ ϑΟʔυόοΫʹΑΔ֬ೝͱΤϥʔగਖ਼
ޡղͰ͖Δ͋͠Θͤ ޡղ͓͔ͨ͛͠Ͱ͍Ζ͍ΖੜΈग़ͤͨ શͳΫϩʔϯ͕Ίͳ͍͍ͯ͘͡ΌΜ ͱ͍͏ΑΓΉ͠ΖͦͷΤϥʔ͕ͦ͜৽͠ ͍ΛੜΉΜ͡Όͳ͍͔
ͱ͍͏ͱ લ͖Ͱؾָ͕ͩ
SECIϞσϧΛޡղ͢Δͧ http://www.jaist.ac.jp/ks/labs/umemoto/ai_km.html [Nonaka 98] Nonaka, I. and N. Konno (1998).
"The Concept of 'ba': Building a Foundation for Knowledge Creation," California Management Review , 40-3, pp.40-54, 1998.
νʔϜͰಈ͍ͯΔͱ͖ ΈΜͳͷ݂ʹͳ͍ͬͯΔ ҉ ໘Խ ڞಉԽ ใೱີʹަ͞ΕͯͯΤϥʔగਖ਼සൟ
νʔϜͷ֎ݴ͍;Β͢ ظؒͰશͯΛ͑ΒΕͳ͍ ίϯηϓτʹͯ͠֎ʹग़͢ ͋Δࢹ/ϞσϧͰΓऔͬͯތு͢Δ දग़Խ
୭͔͕ड৴ ड৴ଆͷͬͯΔίϯηϓτͱࠞͥͯड͚Δ ݁߹Խ Τϥʔగਖ਼ͳ͍͠100%ΘΔ͜ͱظ ͯ͠ͳ͍
දग़Խͱ݁߹Խ ՖคΛ·͘ डค͢Δ ੵۃతʹΤϥʔΛ༠͏͜ͱͰࣅ͍ͯΔΑ ͏Ͱҧ͏ͷΛੜΈग़͢ ΑΓΑ͘ͳΔͱݶΒͳ͍ ૠ͠ժΈ͍ͨͳΫϩʔϯ͡Όͳ͍
Τϥʔͷ࣮ྫ TDDΛฉ͍ͯͨΒςετʹۦಈ͞Εͨ։ൃ Λ͢ΔΑ͏ʹͳͬͯͨΘʔ ʮςετʹۦಈ͞Εͨ։ൃʯͱฉ͍ͯखಈ ςετͷྖҬʹͦΕΛ࣋ͪࠐΜ͡Όͬ ͨΒ͘͢͝͏·͍ͬͨ͘Θʔ
Τϥʔͷ࣮ྫ ετʔϦʔΧʔυɺ͔ΜΜ BTSͷνέοτͱ૬ࣅʹݟ͑ͪΌͬͨͷͰ צҧ͍ͯ͠νέοτͰϓϩδΣΫτΛඍ ͯ͠Ϛωδϝϯτͯͨ͠Θʔ ͦͷޙΑ͘ࣅͨίϯηϓτͷʮνέοτ ۦಈʯΛฉ͍ͯࣅͯΔͱࢥͬͨΒ͕ͪͬ ͯͨΘʔ
Τϥʔͷ࣮ྫ !N@TFLJ !LBXBHVUJ +PFM4QPMTLZͷཪ൪ σϒαϛʮͷΞδϟΠϧʯ ʮ91ͷ৽͍͠ͱ͜ΖܭըήʔϜ͚ͩͩͱࢥ͏ʯ ʮ8JLJͰςετέʔεΛཧ͍ͯͯ͠ɺࣗಈͰಈ͘ʯ ʮຖճΒͤͳͯ͘Α͘ͳͬͨςετΛ֎͢ʯ ʮςετΛཧ͢ΔਓࡢͷσϒαϛͰग़ձͬͨʯ 2008ͷؔ͞ΜͷσϒαϛߨԋͰ৮ൃ͞Εͯɺ
͔Θ͙͕ͪΞδϟΠϧͷϓϥΫςΟεΛ ࢝ΊΔ͖͔͚ͬʹʂ
Τϥʔͷ࣮ྫ σϒαϛॳࢀՃɻ࠷ॳͷηογϣϯͷিܸ ܭըήʔϜΈΜͳͰܭը͢Δͷ͔ɺ͍͢͝ΞΠσΞ 91ϖΞϓϩͱ͔ɺνʔϜ͕͍͖ͭͯͯ͘Εͳ͍ͱ Ͱ͖ͳ͍͚ͲɺςετࣗಈԽͷ෦ɺࣗͷٕज़͕ ͋Εɺ݁ߏΈΜͳָ͕ʹͳΔ෦ɻ ͢Εࠓͷঢ়گͰͰ͖Δ͔ʂ ͍ͬͯ͏͔ɺલ͘Β͍͔ΒͬͯΔਓ͕͍Δͷʹ ͳʹͬͯΜͩɺԶɻ ड͚औͬͨσʔλ
ʮܭըήʔϜʯʮWikiͰͷςετࣗಈԽཧʯ ͬͨͷ σϓϩΠࣗಈԽɺ࣍ʹɺεΫϥϜ (શવҧ)
SECIϞσϧΛޡղ͠ޡղ͠Α ͏ ͩΕ͔ͷίϯηϓτʹܹ͞ΕͯผͷԿ͔Λ ࢝ΊΔྫΛࣔͨ͠ ͱ͍͏ͱ͔͍͍͕ͬ͜ݪཧओٛతʹͨ ͩͷצҧ͍͡ΌΜ צҧ͍ͯ͠Δ͔Ͳ͏͔ΑΓࣗͨͪͷΠ ϯελϯε͕͏·͍͔Ͳ͏͔Λָ͠͏
ͯ݁͞߹Խͷ࣌ؒͰ͢ ࠓͷΛޡղ͠Α͏