人工智能項目爲什麼大部分都失敗了，可能是這5個原因

{"type":"doc","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"大多數企業都在增加人工智能方面的投資，因爲疫情爆發。一些組織正在加速採用人工智能，以確保他們的業務不受當前疫情的影響。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"但組織也需要認識到採用人工智能的挑戰。構建人工智能系統並非一項簡單的工作，每一個階段都伴隨着挑戰。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"即便你建立了一個人工智能項目，在部署時也會有很高的失敗率，這可以歸結爲很多原因。本文將介紹人工智能項目失敗的五個主要原因，並提到了成功實施人工智能項目的解決方案。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"一、戰略方針不當"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"戰略方針有兩個方面：一是好高騖遠，二是缺乏商業方法。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"當談到採用人工智能項目時，大多數組織通常都是從大規模的問題開始。主要原因之一是人們對人工智能的錯誤認識。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"當前，人工智能被過分炒作，但並沒有得到足夠的應用。大部分人認爲人工智能是一種先進的技術，不亞於魔法。雖然人工智能足以成爲一項這樣的技術，但它還處於非常初級的階段。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"而且，當一個組織採用人工智能，是對時間、金錢、資源和人力的巨大投資。因爲公司投入瞭如此巨大的資金，他們也期待更高的回報。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"但是，正如前面所提到的，人工智能還是太狹隘，不可能一蹴而就地推動這樣的回報。那就意味着你得不到正的投資回報率了嗎？完全不是這樣。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"人工智能的採用是一個循序漸進的過程。你構建的每個人工智能項目都是讓人工智能成爲你業務核心的一步。所以從一些小的項目開始，比如測量你的產品需求，預測信用評分，個性化營銷等等。當你構建更多的項目時，你的人工智能會更好地理解你的需求（有了所有的數據），你就會看到更好的投資回報率。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"接下來是問題的第二個方面：當公司決定構建一個人工智能項目的時候，他們總是從技術的角度來看問題陳述。這一做法讓他們不能衡量其真正的商業成功。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"公司必須從商業角度來考慮問題。自問一下問題："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"bulletedlist","content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"你想解決什麼商業問題？"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"界定成功的衡量標準是什麼？"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"回答完這些問題後，再決定用哪種技術來解決問題。請記住，人工智能包含了很多技術，比如機器學習、神經網絡、深度學習、計算機視覺等。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"要知道哪些技術最適合當前的問題，然後開始構建人工智能解決方案。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"二、缺乏優秀人才"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"很多人都忘了人工智能是人類創造的工具。數據當然是一個關鍵因素，但是人類是用數據開發人工智能的人。而目前，缺乏能夠構建高效人工智能系統的天才專業人員。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"LinkedIn 在其《2020 年新興工作》（Emerging Jobs 2020）報告中，將人工智能專家放在了首位。但是，供應似乎還不能滿足需求。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"從質量和經驗兩個方面考慮，短缺將進一步減少。要精通或成爲人工智能專家需要很多年的時間。要成爲人工智能專家，人們必須具備各種各樣的基本技能，例如統計學、數學和編程。而且，因爲人工智能是一個不斷髮展的領域，人工智能從業者必須不斷更新自身。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"據 Gartner 的數據，56% 的被調查組織認爲缺乏技能是沒有成功發展人工智能項目的主要原因。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"組織可以從兩個方面解決這個問題。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"首先，他們需要從員工隊伍中發現人才，並對他們進行技能提升。他們可以逐漸地將這一過程擴展到組織的其他部分。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"其次，組織需要與大學合作，架設學術與產業之間的橋樑。如果清楚地瞭解所需的技能並擁有合適的資源，大學可以訓練學生掌握行業所需的技能。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"儘管教學改革將推動人工智能的發展，但這仍是一種長期的方法。那麼現在呢？第三個吸引人的方法就是專業的人工智能公司，有合適的人才來構建人工智能模型，以及提供人工智能即服務。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"三、數據質量與數量"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在討論了創造人工智能的人的問題之後，現在讓我們來談談使這項技術成爲可能的要素：數據。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在 1950 年第一次提出了人工智能這個概念。但是在那時，研究人員還沒有足夠的數據來實現這一技術。但是，近十年來，情況發生了巨大的變化。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"當技術和小工具成爲人類的親密夥伴時，大多數小工具和軟件都具備了收集海量數據的能力。由於這些數據的收集越來越多，人工智能開始引起注意。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"但是，新的問題隨之而來：數據的質量。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"在構建有效的人工智能系統方面，數據是兩個最關鍵的需求之一。儘管企業已經開始收集大量數據，但是不需要的數據、非結構化數據等問題仍然存在。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"收集數據的方式一般有很多種：結構化、非結構化、半結構化。這通常是無組織的，包含了各種各樣的參數，這些參數可能對於你的人工智能項目尤其重要，也許沒有。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"舉例來說，如果你要構建一個推薦系統，你就不需要手機收集不必要的數據，比如郵件 ID、客戶圖片、電話號碼等等。這些數據並不能幫助你解決客戶偏好問題。甚至更糟的是，在存在大量不必要的數據時，你可能會遇到過擬合的問題。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"對於上面的例子，如果你有網頁瀏覽歷史、以前的購買行爲、興趣、位置等數據，那麼你的人工智能系統會給你更好的結果。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"要解決數據問題，請考慮在啓動人工智能項目前，讓所有的利益相關方都參與進來，包括業務主管、數據分析師、數據科學家、機器學習工程師、IT 分析師和 DevOps 工程師。這樣你就能清楚地理解建立人工智能模型所需的數據、數量和形式。當你瞭解它之後，你可以根據需要清理並轉換數據。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"此外，當你準備好數據時，要確定你用來測試數據的一部分，以確保你構建的人工智能符合你的意圖。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"四、員工缺乏人工智能意識"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"很多人認爲人工智能將取代他們的工作。不過，情況顯然並非如此。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"當企業採用人工智能時，他們也必須同時對其員工進行教育，使其瞭解人工智能是如何“增強”的。若要你的組織在企業範圍內採用人工智能，這種被稱爲“數據素養”的教育是至關重要的。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"需要優先考慮數據素養的原因有兩個："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"numberedlist","attrs":{"start":1,"normalizeStart":1},"content":[{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":1,"align":null,"origin":null},"content":[{"type":"text","text":"確定你的員工（特別是非技術人員）理解人工智能的作用及其幫助他們的能力。"}]}]},{"type":"listitem","attrs":{"listStyle":null},"content":[{"type":"paragraph","attrs":{"indent":0,"number":2,"align":null,"origin":null},"content":[{"type":"text","text":"爲保證教育的成功，他們不會盲目依賴人工智能的決策。"}]}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"曾有一種情景是，即使公司將人工智能部署到日常運營中，員工還是拒絕了。它顯示了員工對這項技術的信任問題。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"同樣，你也不想讓你的員工盲目地接受所有由人工智能作出的決策。你需要確保這些決策是合理和有意義的。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"出於這些原因，並且你的組織開始採用人工智能的方法，你也必須開始培養你的員工的技術知識。促進人工智能的普及，把它當作一項任務而非工作。要讓你的員工理解人工智能的唯一目的就是釋放人類的時間，使他們能夠專注於複雜的問題。有一點很重要，人工智能不只是人工智能，而是增強智能的認知。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"五、 部署後的管理和監測"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"只要你買了一輛車，它具有你所需要的所有必需功能。這款車輛行駛平穩，幫助你在幾分鐘內到達工作地點，甚至根據你的喜好定製環境。那是不是說它不需要你的關注和維護呢？絕對不是。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"類似的，爲了簡單起見，構建和部署人工智能就像擁有一輛汽車。在部署之後，還需要維護人工智能。不過，維護人工智能是一項遠比汽車維護更大的工程。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"人工智能系統會根據提供給它們的數據來作出無數的決策。假如人們不能理解一個人工智能是如何做出某一決策的，那麼這個人工智能系統就被稱爲“黑盒人工智能”。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"確定你的人工智能不會變成黑盒是至關重要的，尤其是在做決策的時候，例如處理貸款、建議醫療方法、接受大學申請等等。很多政府都意識到了黑盒問題，並且考慮規範這些技術。即便沒有法律的約束，確保人工智能的公平與公正也成爲了開發者的道德責任。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"另一個挑戰是數據和業務場景的動態特性。數據不太可能在人工智能項目的整個生命週期中保持靜態。當數據改變時，人工智能也需要重新校準以確保不會偏離其性能。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"對人工智能系統進行重新校準的過程與構建一個全新的模型相似。正如其他人工智能項目一樣，這需要時間和資源。爲了達到這個目的，大多數公司都在很長一段時間內延伸自己的模型，而沒有“維護”或適應模型中的業務變更。但你不能等到模型開始“漂移”而引起不必要的影響。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"要解決這些問題，組織必須持續監控他們的人工智能系統。當數據和業務場景發生變化時，人工智能需要定期更新。要減少這一過程的難度，你可以使用人工智能觀察工具，幫助你監控模型和報告不必要的“漂移”。"}]},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","text":"企業和組織應逐步採用人工智能"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"人工智能是一項強大的技術，它正在改變我們今天的商業方式。但是，正如一些美好的事物一樣，它也需要時間和精力去發掘併發揮它的最好能力。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"身爲人工智能的推動者，我們建議組織"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"作爲人工智能的推動者，我們建議組織逐步採用人工智能。人工智能投資的回報並不是線性的——當你開始在你的組織中使用它的時候，這就會變得複雜。當你擁有了特定的人工智能用例後，你就可以在企業範圍內擴展人工智能系統的採用。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"作者簡介："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Jaimin Dave，市場營銷專家，與 Attri 合作。Attri 是業界第一個可互操作的端到端企業人工智能與機器學習平臺，幫助企業構建、部署和監控模型。喜歡在 LinkedIn 上與大家分享有關人工智能、機器學習、數據科學、MLOps 等方面的最新技術文章。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"原文鏈接："}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"https:\/\/www.datasciencecentral.com\/profiles\/blogs\/the-top-5-reasons-why-most-ai-projects-fail"}]}]}