You're either you're right or you're wrong.

要麼是對的，要麼是錯的。

And I actually think that teaches you some of the wrong lessons.

我其實覺得這教了大家一些錯誤的觀念。

I remember really vividly from my early violin lessons where you can get all the notes right, but that actually isn't what matters.

我非常清楚記得我以前上小提琴課時，必需正確地記下所有的音符，但這其實並不重要。

What matters is that you could weave through the notes, the emotion and the story that the original composer is trying to convey.

重要的是你能通過音符、情感和故事編織出原作曲家所要表達的內容。

And I think that was a really powerful lesson 'cause I think one thing that many of us learned over time is that a lot of times it's not about something being clinically correct or clinically right or exactly right,

我覺得這是非常有影響力的課題，因為我覺得隨著時間的推移，我們許多人學到的一件事是，很多時候，重要的不是一件事是否在臨床上是正確的或者完全正確，

it's about how they kind of make people feel.

重要的是它對人帶來什麼樣的感受。

And I think that definitely is true in technology and it's definitely true in everything that we try to build.

我認為這套用在科技上絕對是正確的，在我們嘗試構建的所有事物中也是如此。

My name is Alexander Way.

我是 Alexander Way。

I'm the CEO and founder of Scale AI.

我是 Scale AI 的執行長和創辦人。

Scale AI is the data infrastructure for AI to power the most ambitious AI projects in the world.

Scale AI 是人工智能的數據基礎設施，驅動世界上最雄心勃勃的人工智能計畫。

Every organization wants to implement AI but oftentimes the biggest bottleneck in their way is being able to create really high-quality data and datasets to power that AI.

每個組織都希望實施 AI，但通常他們遇到的最大瓶頸是能夠創建高品質的數據和數據集來驅動 AI。

At Scale, we sort of view data as the core problem of building great AI whereas a lot of other companies view it as an afterthought and that really prevents AI from having sort of the magnitude of outcomes that it's able to have.

在 Scale，我們將數據視為構建偉大 AI 的核心問題，這是許多其他公司在事後才會思考的，這阻擋了 AI 產生它能夠產生的那種規模的結果。

We've raised over $600 million dollars to date,

迄今為止，我們已經籌集了超過六億美元，

and we work with everywhere from the largest automakers in the world like Toyota and General Motors to the United States Department of Defense to some of the largest enterprises in the world like Microsoft Square and PayPal,

我們與世界各地合作，從世界上最大的汽車製造商，如豐田和通用汽車，到美國國防部，再到世界上一些最大的企業，如 Microsoft Square 和 PayPal，

and some of the leading AI research organizations like OpenAI.

以及一些領頭的 AI 研究組織，如 OpenAI。

When you learn how to program for the first time, it's kind of shocking, but you actually are generally sort of telling the computer to do very simple things.

當你第一次學習設計程式時，會有點震驚，但其實你就是在告訴電腦做非常簡單的事情。

The art of programming traditionally is the art of sort of giving computers very black-and-white instructions, very simple instructions that anybody could follow.

傳統的程式設計是提供電腦非常黑白分明的指令的，非常簡單的指令，任何人都可以遵循。

And one of the beauties of AI is that you actually have the ability to program computers with judgment and with reasoning and with sort of nuanced understanding of the world.

AI 的優點之一是，你有能力通過判斷和推理以及對世界的微妙理解來對電腦進行程式設計。

And so you can have an AI system, look at an image and tell you what's in the image or listen to an audio snippet and understand what's being said.

所以你可以讓 AI 系統看著一張圖片並告訴你圖片中的內容，或者聽一段音頻片段並理解所說的內容。

And it is sort of this incredible enabler for what computers can do or the power of computing.

它是電腦可以做到的事情或是電腦學的能力的驅動者。

And in general, I think we've already seen sort of over the past many decades what the power of computers and computing and mobile phones and all that stuff has been on humanity.

總的來說，我認為在過去的幾十年裡，我們已經看到了電腦、電腦學和手機以及這些東西對人類的影響。

And I think AI and machine learning has a huge opportunity to do the same.

我認為 AI 和機器學習有很大的機會達到一樣的效果。

Both my parents are physicists and I grew up in the small town in New Mexico called Los Alamos, New Mexico,

我的父母都是物理學家，我在新墨西哥州洛斯阿拉莫斯的小鎮長大，

where there's a national lab and a lot of the people I grew up with had parents who were scientists of some sort, it was a sort of very special place.

那裡有一個國家實驗室，很多和我一起長大的人的父母都是某種科學家，那是一個有點特別的地方。

And my mom, from a very young age, taught me about math and physics and science.

我媽媽從小就教我數學、物理和科學知識。

And, you know, she taught me with such wonders.

她用很神奇的方式教我。

I was really impatient as a kid.

我在小的時候真的很沒有耐心。

I think I always wanted to be learning more or I always want to be doing more, always wanted to sort of be accomplishing more.

我一直想學得更多，或者我總是想做更多，總是想完成更多。

And so, I actually, I left high school after my junior year of high school and then moved out to Silicon Valley to work as a software engineer.

所以我其實在高二後就不讀了，然後搬到矽谷擔任軟體工程師。

I learned so much about building products, about what it meant to be metrics, focus and data focus and what it meant to build great software.

我學到了很多關於構建產品的知識，關於度量、焦點和數據焦點的意義以及建造優秀的軟體的意義。

And then, that's when I was inspired by AI, I sort of saw it in my daily work.

然後，那是我受到 AI 啟發的時候，我在日常的工作中看到它。

I was like, AI is really cool and I went back to MIT and then for about a year of MIT, I dropped out to start Scale.

我當時想，AI 真的很酷，所以我回到了麻省理工學院，然後在麻省理工學院待了大約一年，我退學創辦了 Scale。

We have over 500 people now, so it's pretty insane to watch.

我們現在有超過 500 名員工，很誇張。

You know, what we originally started as a, you know, a few people in a, in the basement of our investor to what has sort of become.

畢竟當初我們是在投資人的地下室裡的幾個人開始做起的。

Where we started was in autonomous vehicles and self-driving.

我們的剛開始是做自動駕駛汽車和自動駕駛。

And I think it was one of the first real use cases and applications of AI that I think caught the imagination of the world.

認為這是 AI 第一個真實用例和應用之一，我認為它引起了世界的想像。

You know, what if we could have unlimited easy eco-friendly transportation everywhere in the world through autonomous vehicles?

如果我們可以通過自動駕駛汽車在世界各地享受無限便捷的環保交通呢？

One of the examples that we get really excited about is in health care.

我們真正感到興奮的案子之一是醫療保健。

In health care, there's a huge bottleneck in the number of doctors, trained doctors all around the world.

在醫療保健領域，全世界受過培訓的醫生數量存在巨大瓶頸。

And there's incredible potential for AI and machine learning to actually analyze as many of the cases possible automatically before needing escalation.

人工智能和機器學習具有不可思議的潛力，可以在需要升級之前自動分析盡可能多的病例。

So the doctor can spend their time on cases with anomalies or erratic data or whatnot.

因此，醫生可以將時間花在有異常或數據不穩定或諸如此類的情況上。

And so at Scale, we actually did research with MIT on using AI and machine learning to analyze dermatology data and dermatology imaging to see how AI can actually automate that process and then, therefore, unblock the sort of dr bottleneck.

在 Scale，我們跟麻省理工學院進行了研究，研究使用 AI 和機器學習來分析皮膚病學數據和皮膚病學成像，以了解AI 能如何真正實現該過程的自動化，從而消除醫生瓶頸。

Another use case that I'm really passionate about is using AI to help solve some of the largest geopolitical problems and working with governments in being able to sort of provide technology to aid in some of these very tough and tricky situations.

我真正熱衷的另一個用例是使用 AI 來幫助解決一些最大的地緣政治問題，並與政府合作，能夠提供技術來幫助解決其中一些非常棘的情況。

In the war with Russia-Ukraine, we actually deployed Scales technology in understanding satellite imagery of major Ukrainian cities, Kharkiv, Kiev and Dnipro to understand what was the amount of damage in key parts of these cities.

在與烏俄戰爭中，我們部署了 Scales 技術來了解烏克蘭主要城市哈爾科夫、基輔和第聶伯羅的衛星圖像，以了解這些城市重要地區的破壞程度。

And so we analyzed using machine learning as well as satellite and identified all sorts of structures in these cities where there was meaningful damage that wasn't otherwise being addressed or captured by humanitarian efforts.

我們使用機器學習和衛星進行了分析，並確定了這些城市中存在嚴重破壞的各種結構，而這些破壞並沒有被人道主義努力解決或捕獲。

And so I'm incredibly excited by our work there and actually enabling sort of humanitarian efforts, enabling us to respond to some of the world's most pressing and exigent problems in the world of AI.

我對我們在那裡的工作感到無比興奮，並且實這促成了某種人道主義努力，使我們能夠應對 AI 世界中一些世界上最緊迫的問題。

There's, I think a lot of very smart people but who are focused, you know, so far out in the future that it's almost unhelpful, you know, there's so many people focused on what's gonna happen when we have AGI or what's gonna happen, you know, two or three decades in the future.

非常聰明的人很多，但是他們專注於遙遠的未來，這幾乎沒有幫助，有很多人專注於當我們擁有 AGI 時會發生什麼，或者未來兩三年後會發生什麼。

And I think there's not enough people who are really focused on what are the problems that we have today and how can we use artificial intelligence and machine learning to really changed the game today?

我認為沒有足夠的人真正關注我們今天遇到的問題以及我們如何使用 AI 和機器學習來真正改變今天的遊戲規則。

And so, I think what's next for us is to be the people, some of the people, hopefully in the world who are focused on how do we solve some of the biggest problem today around climate, around agriculture, around geopolitics, around medicine and really start making an impact, you know, now.

因此，我認為我們接下來要做的是成為其中一些希望是世界上那些關注我們如何解決當今圍繞氣候、農業、地緣政治、醫學和醫學的一些最大問題的人。 從現在開始真正產生影響。