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デー活#2「失敗した話」
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Takehiro Kohashi
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デー活#2「失敗した話」
1.
#2 失敗した話 デー活 2019/3/20
2.
成功の裏に多数の失敗あり? 2 意識 無意識 重大事故 ヒヤリ 事故
3.
意識 無意識 重大事故 ヒヤリ 事故 失敗から学ぼう 3 成功 失敗 https://twitter.com/nami1to 好き
4.
なぜ失敗するのか? 4 https://tech.nikkeibp.co.jp/it/atclact/active/17/0529 00291/053000001/ https://www.sbbit.jp/article/cont1/35926 https://www.itmedia.co.jp/news/articles/1811/27/ne ws002.html https://president.jp/articles/-/24489
5.
ユーザー側の意識が悪い …のか? 5 https://tech.nikkeibp.co.jp/it/atclact/active/17/0529 00291/053000001/ https://www.sbbit.jp/article/cont1/35926 https://www.itmedia.co.jp/news/articles/1811/27/ne ws002.html https://president.jp/articles/-/24489 1.当事者意識が薄く、外注へ丸投げ 2.適切な目的を設定できていない 3.目的と行動がずれている 1.見切り発車 2.深層学習信奉 3.構築を丸投げ 4.作って満足
6.
カッコつけずに、今までの失敗を振り返る 6 ■やりたいこと(解決したい課題)が不明確 ・(事業側)「AIで何かやれと社長に言われたから(何をやるかは考えてね)」 ・(IT側) 「ウチAI出来ますよ!(何をやるかは考えてね)」 ⇒ゴールが無いまま走り出す →目的がブレブレになり失敗 ※システムを1から自前で作ることが目的化 ※「既にある資産を使うこと」が目的化 ※難しい技術を使う事が目的化 こういう事ありませんか?
7.
カッコつけずに、今までの失敗を振り返る 7 ■判断基準が不明確 「何がどこまで出来ればOK」なのか誰も考えておらず、 ・(IT側) 「精度90%出ました!結構良くないですか?」 ・(事業側)「え、100%じゃないの?」 ■AIにやらせたい事が不明確 「AIで何が出来る/出来ない?」「やろうとしている事はAIで出来る?」 ・(事業側)「何か知らんけどAIって人間より凄いこと出来るんでしょ?」 ・(IT側) 「それは事業側の範疇なので知りません」
8.
カッコつけずに、今までの失敗を振り返る 8 ■「誰が使うのか」「使い続ける」を考えてない ・そんな難しいスキルをユーザーに求めて大丈夫? ・実際の環境では頻繁にモデル更新が必要になるが、誰がメンテするの? ■データが無い/質が悪い、モデル選択が不適切 ・(事業側)「ウチはデータは持ってるから」 ↑本当に必要なデータを必要な精度・粒度・規模で持ってるかは別 ・(IT側) 「とりあえず決定木を使ってみました!」 ↑過去に使った経験があるから使っただけ
9.
カッコつけずに、今までの失敗を振り返る 9 ■コスト感、スケジュール感が合わない ・そのコストインパクトのために一体いくら・何年かけるの? ※AI導入による期待効果に対して、人件費、システム開発費、 設備費、保守運用費などが見合わない 何が原因で失敗するのか? 成功確率を上げるには何に気を付ければいいか? 客観的・体系的なまとめがあると皆嬉しい?
10.
失敗PJフィッシュボーン -10- フィッシュボーン(特性要因図) • 問題の原因特定・解決を支援するためのフレームワーク (もとは製造業で広く使われてきた) •
解決したいテーマ …背骨 • それに対する大要因 …大骨 • 大骨を構成する小要因 …小骨(・孫骨) • ベースとして、下記を大骨に設定して書き出すことが多い • 4M:Man(人)/Machine(設備)/Material(材料)/Method(方法) • 5M1E:上記+Measurement(計測)/Environment(環境)
11.
4Mで考えてみる -11- PJ失敗 Method (方法)Material(材料) Man(人) Machine(設備) 知識・スキル 人手 目的 責任 センサー ツール・インフラ データ モノ (データ発生源) PJ計画 分析手法 AI適用先
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