“設計師”插件 (The ‘Designer’ plugin)

A couple days ago, a tweet shared by Jordan Singer turned the heads of thousands of designers. With the capabilities of GPT-3 (from OpenAI), he shared a sample of what he was able to create: a Figma plugin (called ‘Designer’) that has the ability to generate a functional prototype from raw text. If you haven’t seen it yet, you can view the video of his demo above.

幾天前， 喬丹·辛格(Jordan Singer)分享的一條推文吸引了成千上萬名設計師的目光。 借助GPT-3(來自OpenAI)的功能，他分享了自己能夠創建的示例：Figma插件(稱為“ Designer”)，它能夠從原始文本生成功能性原型。 如果您還沒有看過，可以在上方觀看他的演示視頻。

As you can see, what’s written in the described raw text accounts for all the desired functionality and visual/graphical characteristics. Singer keys in:

如您所見，所描述的原始文本中的內容說明了所有所需的功能和視覺/圖形特性。 歌手輸入：

“An app that has a navigation bar with a camera icon, “Photos” title, and a message icon, a feed of photos with each photo having a user icon, a photo, a heart icon, and a chat bubble icon”

“具有導航欄的應用程序，其中帶有帶有照相機圖標，“照片”標題和消息圖標的導航欄，每張照片具有用戶圖標，照片，心形圖標和聊天氣泡圖標的照片供稿”

Clicks Design — done.

單擊設計-完成。

The plugin works its magic and then voila — Everything written in appears as requested. He wanted a photo? He got it. He wanted a ‘heart’ icon? No problem. A feed of photos? Done. AI in this example, was not only able to correctly identify the UI elements to be used in the design, but to also intelligently discern the placement of it on the layout as well. So, without a single line of code (in the conventional sense), we have here a very early look into what’s possible in the years to come, as the technology continues to evolve for UX Design.

該插件發揮了神奇的作用，然后瞧瞧-寫入的所有內容均按要求顯示。 他想要一張照片嗎？ 他明白了。 他想要一個“心臟”圖標嗎？ 沒問題。 提要照片嗎？ 做完了 在此示例中，AI不僅能夠正確識別要在設計中使用的UI元素，而且還可以智能地識別其在布局上的位置。 因此，在沒有一行代碼的情況下(按照常規意義)，隨著UX設計技術的不斷發展，我們將在很短的時間內對未來幾年的可能性進行研究。

The responses and retweets were fast and furious. After wading through the comment section on that tweet, it was clear that the collective sentiment in the community was divided. You would read everything from:

回復和轉推既快速又憤怒。 在瀏覽了有關該推文的評論部分之后，很明顯社區中的集體情感是分歧的。 您將從以下內容中讀取所有內容：

“We’re going to get automated — it’s just a matter of time.”

“我們將實現自動化-這只是時間問題。”

to

至

“Graphic designers are probably still significantly safer than UX “designers”

“圖形設計師可能仍然比UX“設計師”安全得多。

to

至

“If all you’re able to create through this is an app that looks like a knockoff of Instagram, we have nothing to freak out about.”

“如果您能通過此程序創建的所有應用程序看起來像是Instagram仿制品，那么我們就沒什么好擔心的。”

And as we speak, the discussions continue. But, regardless of where you stand, or how you view this, it’s first worth noting that this isn’t anything new in particular.

在我們發言時，討論仍在繼續。 但是，無論您身在何處或如何看待它，首先要注意的是， 這并不是什么新鮮事物。

之前我們已經看到過更多極端的技術示例 (We’ve seen even more extreme examples of technology like this before)

生成設計 (Generative design)

Although this new plugin has surprised, scared and even inspired some of us, the above demonstration is not the first of its kind. In fact, we’ve already seen even more extreme, sophisticated outputs of AI-powered, computational design in related fields, like architecture.

盡管這個新插件使我們中的某些人感到驚訝，害怕甚至受到啟發，但上述演示并不是同類中的第一個。 實際上，我們已經在架構等相關領域中看到了AI驅動的計算設計的更極端，更復雜的輸出。

If we take a moment to consider Generative design, this process allows designers and engineers to input constraints into an AI system that then automatically generates all structural permutations.

如果我們花一點時間考慮生成設計 ，則此過程允許設計人員和工程師將約束輸入到AI系統中，然后自動生成所有結構排列。

Teams only need to select what they believe to be is the perfect solution from all its variants — based on the performance data. After having selected a design, the system produces outputs from the results of these tests. Then with these results, the system automatically creates updates towards subsequent iterations. Without being limited to what the human mind is capable of conceiving at any given time, the system can do it all for you.

團隊只需根據性能數據從所有變體中選擇他們認為是最佳的解決方案。 選擇設計后，系統將根據這些測試的結果產生輸出。 然后，根據這些結果，系統會自動創建針對后續迭代的更新。 不受限于任何給定時間的人類思維能力，系統可以為您完成所有工作。

Now that may have completely knocked the wind out of your sails, or you may feel totally inspired. Regardless of how approach it, a conclusion as we come to see, is that these new technologies render design to becoming less and less a specialty skill in this particular sense. The barriers that prevent the creation of great design is diminishing; and it’s terrifying to think that, considering so much of our identity is tied to our craft and our abilities to produce. Naturally, questions like the following emerge:

現在，這可能完全消除了風，或者您可能會感到完全受啟發。 無論采用何種方法，我們都會得出一個結論，即這些新技術使設計在這種特定意義上的專業技能越來越少。 阻礙創造出色設計的障礙正在減少； 想到如此多的身份與我們的Craft.io和生產能力息息相關，這真是令人恐懼。 當然，會出現類似以下的問題：

“Will designers have a job in the next few years?”

“設計師將在未來幾年找到工作嗎？”

“Will we be made irrelevant?”

“我們會變得無關緊要嗎？”

“Do I need to learn to understand how AI works?”

“我需要學習了解AI的工作原理嗎？”

“If engineers hold user interviews and conduct usability tests, are we all doomed?”

“如果工程師進行用戶訪談并進行可用性測試，我們注定要失敗嗎？”

The answer to the above is not so black and white. When we think about what technology effectively allows for, there’s no denying that a lot of what constitutes the domain of design can be deferred to a machine.

上面的答案不是那么黑白。 當我們考慮技術有效地支持什么時，不可否認的是，構成設計領域的許多內容都可以歸結到機器上。

“那么我們對人工智能和自動化的態度應該是什么？ 作為設計師，我們如何保持價值和身份不變？” (“So what should our posture be towards AI and automation? How do we keep our value and identities as designers intact?”)

The AEC team at Autodesk who are developing the AI-powered generative design technology, adopted a very forward-thinking mindset that I found incredibly profound. Meyer says,

正在開發以AI為動力的生成設計技術的Autodesk的AEC團隊采用了一種非常具有前瞻性的思維方式，我發現這是極其深刻的。 邁耶說，

“You’re not mastering the tool any longer, you’re mastering the problem — and letting the computer do all the work.” — Caleb Meyer, Project Industrial Designer

“您不再需要掌握該工具，而是可以解決問題，讓計算機完成所有工作。” —項目工業設計師Caleb Meyer

By shifting its focus away from individual capabilities and towards technological ones, they’re able to channel their efforts towards things that mattered more to them than their own abilities — the problem. If you put your focus and energy towards creating the best possible experiences, you will never be irrelevant. We can recognize AI and its abilities to automate, as a partnership towards our work as designers.

通過將重點從個人能力轉移到技術能力，他們能夠將自己的精力轉移到對他們而言比自身能力更重要的事情上-問題。 如果您將精力和精力放在創造最佳體驗上 ，那么您將永遠沒有關系。 作為設計師的合作伙伴，我們可以認識到AI及其自動化能力。

反思一下我們如何接受自動化，以幫助我們專注于最重要的事情-創造性地解決問題 (Reflect on how we’ve already embraced automation to help us focus on what matters most — creative problem solving)

Let’s reflect on the general impact of automation in UX Design, for example. The creative industry welcomed Zeplin.io back in 2015 to automatically spec our high-fidelity designs for us. Gone were the days when the width, height, colours, drop shadows needed to be manually defined — a platform did it all for us. It removed a layer of painstaking labour to our workflow that freed up our time to work on other creative pursuits.

例如，讓我們來思考一下自動化在UX Design中的一般影響。 創意產業早在2015年就歡迎Zeplin.io為我們自動指定我們的高保真設計。 需要手動定義寬度，高度，顏色，陰影的日子已經一去不復返了，一個平臺為我們完成了所有工作。 它為我們的工作流程省去了費力的工作，從而騰出了時間來從事其他創造性工作。

In that same vein, the emergence of design systems serving as the overarching framework for consistent, usable experiences effectively did the same for us when Google’s Material Design was first introduced back in 2014. UI components with pre-written code snippets could be re-used, instead of creating from scratch — the design system standardized it all for us. It helped ensure our designs were consistent, usable and visually on brand.

同樣，當2014年首次推出Google的Material Design時，出現了用作一致，可用體驗的總體框架的設計系統，實際上對我們做了同樣的事情。可以重復使用帶有預編寫代碼段的UI組件，而不是從頭開始創建-設計系統為我們實現了所有標準化。 它有助于確保我們的設計是一致的，可用的并且在品牌上具有視覺效果。

So, as we now consider the automation of the craft itself, AI will expedite our workflow in a way where some of that grunt work will no longer bury us in tedium. This will allow for more creative exploration and imaginative thinking — freeing us to discover new design paradigms. In the case of AI, it’s a matter of harnessing it. Do as what AEC is doing at Autodesk and make it about solving the problem and leave the rest to the machines.

因此，當我們現在考慮Craft.io本身的自動化時，人工智能將以某種方式加速我們的工作流程，使某些繁瑣的工作不再將我們埋在乏味中。 這將允許進行更多的創造性探索和富于想象力的思考-使我們能夠發現新的設計范例。 就AI而言，這是利用它的問題。 按照AEC在Autodesk中所做的事情做，并解決問題，然后將其余問題留給機器處理。

Upon further reflection of the ‘Designer’ plugin demo, we as the audience were enamoured by the wrong thing. We were enamoured by the tool, instead of the designer himself (in this case, Jordan). What this does, is pigeon hole our discipline to a very specific area in the craft alone, rather than the individual who discerns how to harness the technology behind the keyboard. At the end of the day, the means to value-adding experiences will always get quicker, faster and more efficient. But the empathic human, understanding the needs faced by everyday people, curating that experience in the first place, will always be relevant and depended upon to solve real-world challenges.

在進一步思考“ Designer”插件演示后，我們作為觀眾迷戀了錯誤的事物。 我們迷上了該工具，而不是設計師本人(在本例中為Jordan)。 這樣做的結果是，僅在手Craft.io領域的某個特定領域就放飛了我們的學科，而不是識別如何利用鍵盤背后的技術的個人。 歸根結底，增值體驗的手段將永遠變得越來越快，越來越快，效率越來越高。 但是，善解人意的人，要理解日常人們所面臨的需求，并從頭開始培養這種經驗，將始終具有相關性，并依賴于解決現實世界中的挑戰。