跟讀練習: Don’t Start with AI, Start with the Problem - 通過影片學習英語口語
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-To build good AI products, we should start with three major steps, identifying problems that users experience, identifying how we might go about solving those problems, then identifying the technology that can help.
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The challenge is that these days, we so often start with selecting the technology and find a problem for it to solve later.
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With AI, this bad reverse order is even worse.
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More often than not, with AI, people are pressured to use AI for something, anything.
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In UX, we call this problem solutioneering.
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It's so common that we included it as our UX slogan number 18.
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Instead, we should be collecting issues that do exist, problems that users do experience.
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A good AI strategy begins with identifying unmet user needs, business challenges, or market opportunities, and then considering where AI might be a viable solution along with other alternatives.
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Let's talk about an example.
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Imagine a team at a FinTech company.
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The wrong way, the solutioneering way, starts with some sort of pressure from above.
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"We need to use AI to look innovative." The team decides to add a generic AI chatbot to their main credit card page.
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After months of work, they launch the chatbot, but users don't use it.
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Why? Because maybe the page already has a clear comparison table and customers can find what they need faster without talking to a bot.
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In this situation, the AI chatbot is a classic solution that solves no real user problem.
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A solution looking for a problem.
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Let's flip that.
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Let's take a look at an example of how we should approach building useful AI products.
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Step one, identify the user problem.
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Let's imagine that the team analyzes user data and sees that plenty of people abandon their online mortgage application halfway through.
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Through a few customer interviews, they quickly learn why.
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Customers are confused by financial jargon like APR or interest rate and feel anxious they're making the wrong choice.
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That's the problem.
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Step two, identify how to solve it.
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The goal is to reduce confusion and anxiety.
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They could create a glossary page, make explainer videos, or offer a live chat with a loan officer.
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Step three, identify the technology.
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This is where AI could actually help.
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A well-trained AI assistant could offer instant, in-context explanations for confusing terms 24-7.
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Success can be defined and would be easy to measure.
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A decrease in the number of abandoned mortgage applications.
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See the difference?
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We didn't start with AI.
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We started with a real user problem and found that AI was a fitting tool that might solve it.
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AI is just a tool that can solve problems.
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It shouldn't be assumed to be part of the solution unless you've started with the problems first and understood them clearly.
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Not only should we have identified legitimate problems, but ideally, we've had a discussion on how we'd recognize success by doing it.
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As much as we want to start with AI, we want to create good experiences.
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Start by identifying problems.
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Then, just maybe, AI could help solve those problems.
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-Thanks for watching.
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If you want to see more of our UX videos, take a look at these over here and consider subscribing to our channel.
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On our website nngroup.com, you can access our free library of over 2,000 articles.
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You can also register for one of our UX courses that offer live hands-on UX training.
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背景與語境
在當今技術快速發展的時代，人工智慧（AI）漸漸成為各行各業的熱門話題。許多人在構建AI產品時，往往不自覺地遵循錯誤的順序：首先選擇技術，然後尋找解決問題的方法。這樣的方法不僅無效，甚至可能導致資源的浪費。本影片強調了確立使用者問題的重要性，只有在瞭解真實的使用者需求和痛點之後，才能有效地運用AI技術。在這個過程中，正確的思維方式將有助於創造更好的使用者體驗。
日常交流的五大短語
- 我們需要使用AI來展現創新。
- 許多使用者在申請線上抵押貸款時中途放棄。
- 他們對金融術語感到困惑。
- 我們應該確定使用者的問題。
- 成功的標準應該易於衡量。
逐步跟讀指南
為了提高您的英語能力，尤其是在發音方面，跟隨影片中的對話是非常有效的練習方法。以下是您可以採取的步驟，幫助您更好地理解和模仿：
- 選擇內容：首先，觀看相關主題的YouTube影片，如本影片，瞭解背景和語境。
- 集中注意：在開始跟讀前，確保您理解影片中的關鍵內容和短語。
- 分段練習：將影片分成小段進行跟讀，這樣可以更好地掌握每一句的發音和語調。
- 錄音對比：用手機錄下自己的聲音，與原影片進行對比，注意發音和語調的差異。
- 重複練習：定期反覆進行跟讀練習，逐漸提高自己的流利度和自信心。
通過這種方法，您可以在提高英語發音的同時，深入理解問題導向的思維方式。利用看YouTube學英語、shadow speech等資源，可以加速您的雅思口語練習，進而提升您的整體英語水平。
影片中的語法
說話人最常用的結構，並附上影片中的原話：
| 結構 | 影片中的用法 |
|---|---|
| 被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的 | are pressured · can be defined · be assumed |
| 現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事 | you've started · have identified |
什麼是跟讀法？
跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。