AI 邁向商轉時代,決策者該如何導入企業?在 AI 商轉的時代,各企業都想導入 AI,但是懂 AI 技術和做決策的人往往是不同的人,不只 AI 的執行者需要深入研究,決策者也必須對 AI 有正確的認識,才能相互溝通並做出正確的決策。本書由日本著名的產業研究機構「野村總合研究所 (NRI)」的專家群所撰寫,不僅帶您綜觀 AI 的核心技術也具體說明未來的趨勢,並列舉 30 餘家大企業如何活用 AI,希
人性化的AI智慧 VS 機器化的人類,「攻殼機動隊」終有實現的一天!人工智慧已經發展出相當耀眼的成就,未來,更會出現「超AI」。一旦超AI異能力凌駕人類,人腦將不得不與電腦結合,化身為賽伯格。哲學教授髙橋 透在《AI世代生存哲學大思考:人人都必須了解的「新AI學」》一書裡,從人類生態、經濟發展、科學哲學等角度思考AI世代的未來,並藉由AI思考人類存在的意義,以及今後人類的生存之道。髙橋 透在《AI
【AI 權威領進門,破除你對 AI 的疑惑和迷思!】本書作者野村直之先生,是日本產業界的 AI 導入教父,曾在美國麻省理工學院 AI 研究所與「AI 之父」馬文.閔斯基 (Marvin Minsky) 及語言學家諾姆.杭士基 (Noam Chomsky) 密切交流。他創立的 Metadata 公司已協助許多企業活用大數據分析、社群媒體與 AI 技術來解決問題。野村先生將以自己為各大企業導入 AI
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Ai改變教育,當答案一鍵生成,教育和學習的重心如何改變?2025Ai時代教育教養趨勢全解讀:從社會情緒學習(SEL)、深度閱讀到自由玩耍⋯⋯一本為孩子打造未來幸福與競爭力的實踐指南!★獨家萬人調查、全球專家最新觀點!★爲家長與教師提供具體策略,家長輕鬆吸收、馬上上手!未來已來:AI時代教育與教養大趨勢不改變、不學習是最大危機:集體「無照駕駛」的時代我們正處於一個深刻矛盾的時代。一方面,生成式AI正以前所未有的速度賦予人類社會巨大的潛力;但另一方面,這股看似無所不能的科技浪潮下,卻有一股令人不安的暗流正在湧動。這是一個集體「無照駕駛」的時代。我們的孩子,如同數位時代的原住民,正以驚人的速度擁抱AI,而身為大人的我們卻仍在黑暗中摸索,AI帶來的衝擊,正迫使我們思考更根本的問題:「我們到底應該教什麼、測驗什麼?」世界經濟論壇(WEF)《二〇二五未來工作報告》整合了全球千家企業雇主的心聲,報告列出的「未來十大核心能力」中,高達八項屬於傳統測驗很難評量的「非認知能力」,例如韌性、自我覺察力、終身學習力等等。這些「軟技能(Soft Skills)」極難用傳統紙筆測驗衡量,卻是AI無法取代的關鍵能力。報告更預測,到二〇三〇年,全球將有近四成的職場核心能力將再次變革。這傳遞了一個清晰的訊息:在未來的世界裡,不改變、不學習,就意味著直接被淘汰。 重新學習(Reskill)、技能升級(Upskill)將成為生活的日常。面對巨變,教育的首要任務,是為每個孩子配備一張AI時代的「駕照」。這不僅是「會用」AI,更是「懂用」AI,將AI視為強化自身能力的「副駕」,而非全權託付的「代駕」。如果說AI素養是駕馭未來的「駕照」,那麼培養無法被取代的非認知能力,就是確保我們駛向正確方向的「內在導航」。在知識快速貶值的時代,真正決定一個人未來成就的,將是其內在的心理素質與人文素養。面對如此巨大的變革,沒有人是局外人。單一家庭或一所學校的努力,不足以照亮整片天空。我們需要的,是集體的力量與共同的對話,手牽著手,一起畫出屬於台灣新時代的教育地圖。這本書寫給——◼關心未來教育的父母想為孩子準備 AI 時代「駕照」,不只懂得使用工具,更能培養抗淘汰的非認知能力的家長。◼教育工作者與學校決策者希望在教學與課程中融合 AI 素養與軟實力培養,並重新設計評量標準的老師、校長與教育行政人員。◼青少年與大學生的導師、輔導
內容介紹: 【世界發生劇變!銷售 AI 化勢在必行!】AI 議題已經講了好多年,然而絕大多數的企業仍然沿用老路子做生意,亂槍打鳥式的銷售方式也沒甚麼進步。然而時代不同了,既然有 AI 這個好工具為何不用?因為不懂不會所以不知該如何開始?眼睜睜看著 Google、Facebook 靠著 AI 化的銷售技術大賺特賺!其實,AI 不是大企業才能作,中小企業也可以,但不是全面做,而是挑選適合的做。最容易做到且很快就能換成實績的就是行銷、業務的銷售工作,只要利用自身累積的銷售與客戶等資料就行,讓 AI 自動找出其中隱藏的銷售密碼,並直接運用在工作中驗證,這就是本書的主軸:銷售 AI 化,讓機器學習來幫忙。【AI 不是打高空,要落實在工作中】許多工程師學了 AI 技術,卻不知道如何讓技術落地!其實開發以銷售為目的的 AI 並不需要高深的技術,只要用機器學習就能辦到。書中範例使用的都是真實企業產生的工作資料,例如要預測潛在客戶時,可讓 AI 從客戶職業、年齡與過去的銷售實績等資料自動學習,找出資料間的關係建出模型並做出預測,我們就可以對商品或客戶擬定策略去執行計畫。也可以依據過去幾年的每日銷量,考慮節假日的影響,利用 Facebook 提供的時間序列套件去預測未來一段時間的銷量等等。重要的商用實作範例包括:●銷售成交預測●銷量或來客數預測●季節週期性變化預測●推薦商品提案●根據客群制定銷售策略本書由世界 500 大企業 Accenture (埃森哲) 公司的 AI 集團資深總監親自執筆,規劃出開發 AI 專案的標準流程,從選擇適合引入工作中的 AI 開始,一路到訓練資料的取得、資料加工、選擇演算法及建立 AI 模型之後的評估與調整等 9 大步驟。即使沒有開發過 AI 專案經驗的人也不用擔心,只要跟著動手做,就能看到成果。書中的案例會實作監督式學習與非監督式學習中「分類」、「迴歸」、「時間序列」、「關聯分析」、「分群」、「降維」等各種演算法,讓讀者依照問題的類型選擇適用的處理模式。而且每個專案都不馬虎,從頭做到尾一遍一遍演練 SOP,將流程深深印入腦海,熟悉每個步驟之後才能順利應用到自己的專案。【邁向資料科學家之路】書中提供的所有 Python 程式都是可以運用在實務工作上的原型,每個人都可以利用這些原型建出自己想要的機器學習模型。了解如何從實務觀點建立 AI,藉由本
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