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Dic ai 1704_人工知能の事例_橘健司
1.
人工知能の最新事例 橘 健司
2.
人工知能によるレコメンドの事例 メガネを販売するJINSが自分に似合うメガネを人工知能で判別してくれるサービス 「JINS BRAIN(ジンズ・ブレイン)」を開始 →いつ・どこにいても、パソコンやスマートフォンから 手軽にJINSスタッフのAIによるメガネのレコメンドサービスを受けることができる。
3.
”実際にJINSスタッフ約3,000人がのべ60,000枚に及ぶ画像データを評価することでAIを開発。” →アルゴリズムを確立するまでには未だ膨大な元データが必要となる。 →少ないデータからアルゴリズムを確立できることが理想。 ”いつどこからでもパソコンやスマートフォンからメガネのレコメンドサービスを受けることができる。” →通販などだとファッション系の商品は買いづらい点はあるかもしれないが、 このサービスによりリモートでもある程度自分に似合う/似合わないを客観的に判断してもらえる。 →通販のようなリモートでも商品を買う動機になる? ”将来的にはそれぞれの趣味嗜好に応じたAIの展開も可能だという。” →一旦サービスをローンチすればカスタマーからのデータ収集が可能になり、 より精度の高い予測や幅を広げた予測が可能になる。 人工知能によるレコメンドの事例(考察)
4.
簡単に言うとコールセンターにおいて、質問者からの質問を一部AIによる自動応答によって まかない、オペレーターも負荷を削減する仕組み。 ・過去にあった質問はデータベースにある情報を元にAIが自動応答する。 ・新規の質問に関してはオペレータの回答をデータベースにフィードバックすることで学習する。 ”オペレーター不足、品質の向上および均一化、人件費の高騰といった課題の解決を支援し、 コールセンターの生産性向上、マーケティング活動の貢献、顧客満足度の更なる向上を促す、 次世代型のコールセンターソリューションを提案する。” 人口知能のコールセンターへの応用事例
5.
”コールセンターはオペレーター不足という問題を抱えている” →AIが人が仕事を奪うと言われているが、人がやりたくない仕事をAIに任せるのは良いのでは。 より人がやりたい仕事をする方向になる? ”ホームページの「よくある質問集」などを見ても探しきれず、コールセンターに電話してしまう。” →膨大な情報を処理することは、コンピュータの方が長けている。 ”コールセンターにおける課題は、従前から変わらず“待ち時間が長い”“電話がつながらない”といった接続品質だ。” → コールセンターにくる質問の一部をAIによる自動返答に置き換えることによって待ち時間が減り、 全体的な顧客満足度につながる。 人口知能のコールセンターへの応用事例(考察)
6.
GoogleのAIシステムGoogle Brainが開発した、インターネット上にあるモザイクがかかった画像や 「ぼかし」が入った画像がより鮮明になる新しい技術。 “人にはほとんど認識できない低解像度の不鮮明な画像を、解像度の高いはっきりした画像に変換できるほか、 画像がかけている部分を予測して再生することもできるという。” “解像度が上げることができれば、たとえば街や店舗に備え付けられた防犯カメラやドライブレコーダーなどの精度が 上がるため、防犯に応用できる可能性がある。” ”おそらくGoogleの画像検索のデータが判断の元になっている” Googleによる画像補完の事例
7.
”解像度が上げることができれば、たとえば街や店舗に備え付けられた防犯カメラやドライブレコーダーなどの精度が 上がるため、防犯に応用できる可能性がある。” →防犯カメラなどは高い解像度が必要なくなることで、保存容量などの問題が緩和される。 かつ、カメラに映った人が特定の人(指名手配犯など)かを判断もできるようになる? ”個人が特定できないように伏せていた情報が、興味半分で勝手に暴かれてSNSなどでさらされてしまう可能性” →このアルゴリズムが一般化すると、伏せるべき情報が暴かれ公開される可能性がある? 公開範囲や用途を限定する必要があるのでは。 Googleによる画像補完の事例(考察)
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