AI芯視界

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在這段視頻摘錄中， 埃森哲的一位高管討論了讓開發人工智能 (AI) 的專家團隊和需要適應和使用人工智能的其他員工隊伍做好準備的重要性。 他強調組織需要投資於學習平台和人才發展，以不斷對員工進行再培訓並保持他們的技能與時俱進。 只有 3% 的組織計劃增加 AI 培訓支出，這還不夠。 使用人工智能本身來幫助準備勞動力.也是一個潛在的創新領域。


Q:埃森哲如何投資人工智能人才培養
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埃森哲每年投資約 10 億美元用於培訓和再培訓其員工，開發人才平台，不斷對人員進行再培訓，以確保他們的技能是相關的，並且隨著 AI 技術的進步，他們能夠為組織做出富有成效的貢獻。


Q:需要為 AI 開發的兩大類技能是什麼
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需要為 AI 開發的兩大類技能是：
1）做人工智能所需的技能，包括構建未來技術所需的機器學習、數據科學、STEM 技能和編碼技能。
2）使用人工智能所需的技能，這意味著在組織的不同級別的員工隊伍中適應和實施人工智能。 這包括培訓人們如何使用新平台、分析人工智能提供的數據以及與機器協作以提高工作效率。


Q:如何使用 AI 來幫助勞動力做好使用準備？
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人工智能可用於通過分析工作趨勢、評估個人技能和建議潛在的新職業道路來幫助勞動力做好準備。 例如，公司可以使用 AI 來分析現有員工的技能和經驗，並將其與外部職位發布和市場趨勢進行比較。 然後，人工智能可以洞悉哪些技能在未來可能會過時，並建議員工可以進行自我培訓的相鄰工作類型。 這可以幫助勞動力適應不斷變化的就業市場並保持相關性。 此外，人工智能可用於提供個性化培訓和教育、跟踪進度並向員工提供反饋。 總體而言，人工智能可以提供有價值的見解和建議，幫助員工和組織為未來的工作做好準備。


Q:AI如何面試新進員工
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AI可以作為輔助工具來面試新進員工。 1可以利用自然語言處理技術在招聘廣告和簡歷中尋找匹配度高的關鍵字和經驗，挑選出符合職位要求的候選人。
2.可以編寫面試問
題，利用AI人工智能語音識別和語義分析技術，對應答者的表現進行實時評估，例如評估其表達能力、邏輯思維、人際溝通能力等。 3.可以利用 AI 中的
情感分析技術，對面試者的語音和表情進行分析，了解其情緒變化和態度，幫助面試官更全面地了解面試者的個人素質和能力。 但需要注意，AI仍然難以完全取代面試官的角色。 例如，AI不太可能像人類面試官那樣能夠識別身體語言、了解生活背景，或者運用經驗和感性判斷力去參與復雜的人際互動。 因此，面試者依然需要與真人面試官進行面試交互，而AI技術可以在這個過程中作為輔助手段，幫助面試官更快速、更準確的識別出優秀的候選人


Q:AI如何考核人事
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AI可以通過分析員工的工作表現、數據記錄和反饋，來對人事進行考核。 舉例來說， 可以利用AI來分析員工的銷售成績、客服反饋、項目執行情況等數據，然後結合員工的職責和任務要求進行評估。 此外， 也可以應用自然語言處理技術，對員工的匯報、郵件等文檔內容進行語義分析，判斷員工的邏輯思維能力、溝通能力、創新能力等方面的表現，從而幫助管理者更加全面地了解員工的表現。 然而， 需要注意的是，人事是一個複雜而敏感的領域，不應完全依賴於AI來考核員工。 AI僅作為輔助手段，需考慮保護員工的隱私權，避免過於依賴技術導致人性化因素的忽略。


0:05 As you think about your talent and your workforce for the age of AI and where we’re moving 0:11 to, there’s a few things that are very important to think about. 0:14 I think about two broad categories of skills that you need to prepare for. 0:20 One is what I’d call the Talent or the skills – the people that “do” AI. 0:24 Who’s going to develop the AI? 0:26 Those are the machine learning experts, the data scientists, the people with the STEM 0:30 skills and coding skills that are going to build the technology of the future. 0:33 And that is an important area that every organization needs to be preparing for in developing and 0:39 building those types of skills. 0:41 And that’s one important thing. 0:43 The bigger set of skills that I think every organization needs to think about are the 0:47 people who use AI. 0:48 Not the ones who do and build it, but the people who use AI. 0:51 And that’s going to be basically everybody in the workforce, or almost everybody in the 0:55 workforce, in your workforces or organization. 0:57 And I think that’s an area where organizations haven’t spent enough time. 1:00 Everybody knows they have to develop and hire the AI experts and the coding experts; I think 1:05 how the rest of your organization is going to adapt and use AI is the big question that 1:09 we’re really trying to address in Human + Machine. 1:12 There’s a few things that I’d say that are really important there. 1:14 One is you need to think about the learning platforms that you’re developing for your 1:20 organization. 1:21 One thing that we found in the survey in the research work we did is two-thirds of organizations, 1:26 roughly, believe that their workforce isn’t ready for AI, broadly for using AI – a big 1:32 number. 1:33 Only three percent of organizations plan to increase their training spending to account 1:38 for that, which isn’t appropriate. 1:40 That means that generally, people think it’s somebody else’s problem to prepare the workforce. 1:45 And we believe that that’s not the right answer. 1:47 At Accenture, we’re investing about a billion dollars a year in training and retraining 1:51 our workforce, in developing talent platforms that continually retrain people. 1:55 And we think that that’s the approach you need to take because we’re in an age of 1:58 continuous innovation. 2:00 The roles of your workforce are going to continue to change, and you can’t flush and replace 2:04 the workforce, and that’s not the right way to view it. 2:06 How do you look at your employees as an investable resource, where you’re investing in the 2:10 talent and developing the right learning platforms that they can learn how to use AI in the initial 2:15 applications you’re rolling out now, and continue to learn so that their skills are 2:19 relevant and they’re productive contributors to your organization as you continue to progress? 2:23 Another area that we really overlooked and where there’s huge potential is using AI 2:27 itself to help prepare the workforce. 2:30 And I think there’s huge opportunities for innovation here. 2:33 We’re starting to see some real interesting possibilities coming. 2:37 One experiment we’ve done as an organization, and this is still in the research and development 2:41 stage, we’ve looked at all of our employees in our Accenture organization (and we have 2:46 over 430,000 people, so it’s a large workforce). 2:49 We’ve developed a machine learning model using artificial intelligence that can take 2:53 the resumes and experience of any one of our employees – and this is something our employees 2:58 can use to understand how their job will be impacted by AI. 3:02 So it might say that as you feed in all of your information, it’ll compare it to external 3:07 job postings and trends in the marketplace. 3:09 And it might say “Well, your skills are at risk in about one to three years.” 3:13 And it doesn’t stop there, but it says “and based on what you know here’s the adjacent 3:18 types of jobs that you should start looking to train yourself for.” 3:24 And again this is at the research and development stage, so I wouldn’t say it’s a product 3:27 out there yet but it shows the kind of innovation and creativity and way that we can use technology 3:32 itself to help prepare workforces for the changes that are coming down the road. 3:37 There’s a lot of voices out there that are very well-regarded voices – Elon Musk, the 3:41 late Stephen Hawking, who talked very eloquently about the perils and dangers that we face 3:46 with AI. 3:47 I do think we need to consider those and we need to think about the longer term implications 3:51 of AI, like we do of any technology. 3:54 Every technology that’s ever existed from, the first stone wedge that the cavemen carved 4:00 or the first fire that was lit, was used for good, and it could be used for bad as well. 4:05 AI is no different, and the thing that I think concerns people sometimes is the pace of AI 4:10 and the ability of AI to make decisions that are not in the interest of us as human beings. 4:16 The reality is that the risk of that happening is far away. 4:21 We should be thinking about it. 4:22 There are organizations that are set up to think about those implications, and we support 4:25 and are involved in some of those organizations. 4:28 But that’s for the distant future, and not something that we need to think about in our 4:31 generation right now in terms of real, serious consequences. 4:36 The opportunity for us now is to think about how do we apply this to live more effectively 4:42 on the planet, to better use our resources and to operate businesses and educational 4:47 institutions and our governments more effectively? 4:48 There’s massive opportunity for this, so rather than be consumed and stuck by what 4:54 might happen with the technology in the future we need to think about that and prepare for 4:57 it, but let’s apply the technology in a responsible way, which is what we talk about 5:01 in Human + Machine: to solve these problems today.