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2016/11/6
原田 慧
KEI HARADA
 いわゆる「データサイエンティスト」
◦ 某データ分析企業勤務
◦ 佐野くんの先輩
◦ @hoxosh (タコの絵のひと)
 数理学博士から就職
◦ 何でも不器用にこなし、特に数学が得意
◦ データ分析の本をちょっと翻訳した
◦ KDDCUP2015というデータ分析コンペで世界2位!
 ITLeadersというところに連載した
http://it.impressbm.co.jp/articles/-/12969
◦ 人狼知能ミニ大会というもので優勝した
http://aiwolf.org/
 大義名分は「Feynman経路積分の数学的な定式化」
◦ すごく振動する関数(の関数)を、AからBに至るすべての
pathについて平行移動不変な積分をするとシュレディンガー
方程式の基本解ができる
 どこが難しいか(面白いか)
◦ 1.振動するものに数学はとても弱い
◦ 2.Pathの空間は無限次元空間で、いろんなものがコンパク
トでなくて詰む、例えばLebesgue測度がない
絶対可積分は甘え コンパクト近傍は甘え
◦ 1.振動するものに数学はとても弱い、だけちょっと説明
◦ 例題1: S1 = -1+1-1+1-1+1-1+ ・・・ = ?
◦ 例題2: S2 = -1+2-3+4-5+6-7+ ・・・ = ?
◦ 学生のときに閃いたことは、(0.25, 0.50, 0.25)と合成積を
とると、S1 = -0.5, S2 = -0.25
◦ S3 = 1+2+3+4+5+6+7+ ・・・ とおくと、
S2+S3 = 4+8+12+16+ ・・・ = 4*S3
よって、 S3 = -1/12
ゼータの計算ができた!
 「データサイエンティスト」はセクシーらしいね
◦ あと「人工知能」すごいらしいね
 数学はデータ分析の役に立つ
◦ プログラミングもできるとさらに強い
 データ分析ができるとこういう楽しい問題がある
 結論:数学の人がデータ分析やるとたぶん楽しい
◦ だからみなさんもやってみてください
 これは違う、自信を持って言える
◦ やってることはかなり地味
 経営層から見たらもしかしたらセクシーかもしれないけど
◦ ビジネスを数字で、客観的・合理的にみて、改善するというの
は、まあ本来当たり前のこと
◦ データサイエンティストはそのとき使う武器がデータ・データ
分析であるだけ
 集計されたExcelではなくて、その元のデータを自由にさわる
 世の中の需要はかなりあると思う
◦ ちょっとデモ
 これはわからない
◦ 個人的には、もしすごい人工知能ができたら、ものすごく無関心・
消極的に滅ぼされるかもと思っている
◦ いま新聞をにぎわせているAIはそういうものではない
 どうやら人は自分にはよくわからないプログラムを「AI」と呼びたくなるら
しい
 強いAIと弱いAI
◦ 明確な定義はないけど、特定のことに強いのが弱いAI
 データ分析の結果できるものは弱いAI
◦ 知性・自我・意識とかをもっているようにしか見えないのが強いAI
◦ 作る立場からすると、さわっているうちにどんどん弱いAIに見えてく
るというジレンマ・・・
 数学は多少は必要
◦ それ以上に、数学者が持って
いる素養がデータ分析にとて
もマッチする
◦ 特にHacker Mindsetと
Scientific Method
 これらはサラリーマンが身に付
けるのはとても大変
 プログラムができて、コミュ
ニケーションがちゃんと取
れて、数学ができると強い
 マリオをクリアする人工知能(?)
◦ もともと親会社にいたtomomoto_factoryさんのネタ
 DeepMindという会社がAtariでやったことで有名
 AlphaGoの会社
◦ 画像とマリオの残機を入力して、ゼロからスタートして何度も
何度もプレイするうちに強化学習によってだんだん賢くなるも
のを作りたい
 目標は1-4クリア
◦ ゼロの状態のデモ
 人狼をする人工知能
◦ 人狼ゲームは人狼陣営(嘘つき)と人間陣営に分かれて、人
狼は村人を騙す、村人は嘘つきを探すゲーム
 人狼ゲームの特徴
◦ チェスや将棋、囲碁のような完全情報ゲームではない
◦ 対戦相手とのコミュニケーションが必要
 例えば合意形成
◦ 「騙される」って実はすごい知性
 行動をもとに推定するだけなら意外と簡単
◦ デモ

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