鑽石舞台

I'm Aicha Evans.I am from Senegal, West Africa.And I fell in love with technology, science and engineering at a very young age.Three things happened.I was studying in Paris, and starting at seven years old, flying back and forth between Dakar, Senegal and Paris as an unaccompanied minor.So it wasn't just about the travel.It was really about a portal to knowledge, different environments and adapting.

我是艾莎·埃文斯。我來自西非的一個國家，塞內加爾。在我很小的時候，我就愛上了科技、科學和工程。那時候發生了三件事。那時我在巴黎讀書，從7歲開始上學，在達喀爾、塞內加爾和巴黎三地之間飛來飛去，是一個旅途無人陪伴的未成年人。所以這對我而言不僅僅是旅途。更是讓我收穫了知識、學會適應不同環境。

Second thing that happened was every time I was at home in Senegal, I wanted to talk to my friends in Paris.So my dad got tired of the long-distance bills.So he put a little lock on the phone, the rotary phone.I said, OK, no problem, hacked it.And he kept getting the bills.Sorry again, Dad, if you're watching this someday.And then, obviously, the internet was also emerging.

第二件事是，每次我在塞內加爾的家裡，我都想和我在巴黎的朋友聊天。我爸爸不想支付那些長途話費賬單。所以他給電話加了把小鎖，那種轉盤撥號電話。我說，好啊，沒問題，然後把鎖給破解了。於是他又收到長途話費賬單了。爸爸，如果你某天看到了這個視頻，那實在不好意思了。後來，顯然，互聯網開始蓬勃發展了。

So what really happened was that, in terms of technology, I really saw it as something that shaped your experiences,how you understand the world and wanting to be part of it.And for me, the common thread is that physical and virtual transportation.Because that's really what that rotary phone was for me , are at the center of the innovation flywheel.Now, fast-forward.I'm here today.

所以，就科技而言，我認為它真的可以改變你的經歷、影響你如何認識這個世界並且想成為它的一部分。對於我來說，那些經歷的共同點就是實體和虛擬的傳輸。因為當時轉盤撥號電話對我的真正意義就是創新飛輪的中心。我們快進到現在。我今天在這裡。

I'm part of a movement and an industry that is working on bringing transportation and technology together.Huh.It's not just about your commutes.It's really about changing everything in terms of how we move people, goods and services, eventually.That transformation involves robotaxis.Driverless cars again, really?Yeah, yeah, yeah, I've heard it before.And by the way, they are always coming the next decade.

也是作為整個科技行業的一部分，致力於將交通和科技結合在一起。哈。這不僅僅只是影響到你的通勤。這是在改變一切，改變人們的出行、貨物的運輸，最終改變服務業。這種轉變涉及到無人駕駛出租車。又是無人駕駛汽車，對吧？對，對，對，我聽過這種說法。順便說一句，無人駕駛汽車在未來幾十年一定會出現。

And oh, by the way, there's an alphabet soup of companies working on it.And we can't even remember who's who and who's doing what.Yeah?Audience: Yeah, OK.Well, this is not about personal, self-driving cars.Sorry to disappoint you.This is really about a few things.First of all, personally and individually owned cars are a wasteful expense.And they contribute to, basically, a lot of pollution and also traffic in urban areas.

再順便說一句，有很多頂尖的技術公司都在研究這個。我們甚至記不清，誰是誰，誰在做什麼。是吧？觀眾：是，好的。哈哈，我要說的不是私人的自動駕駛汽車。如果讓你失望了，那抱歉了。我要說的是其他一些事。首先，私家車是一種金錢浪費。基本上，只會造成大量污染以及加重城區裡的交通擁堵程度。

Second of all, there's this notion of self-driving shuttles.But frankly, they are optimized for many.They can't take you specifically from point A to point B.OK, now we have, hm, how am I going to say this, the so-called "personal, self-driving" cars of today.Well, the reality is that those cars still require a human behind the wheel.A safety driver.Make no mistake about it.I own one of those.

其次，我們還有一個概念叫自動駕駛班車。其實就在於服務更多的人群。不能專門帶你從A點到B點。好，我們現在講了這兩個，嗯，我接下來要說的是那些今天所謂的私人自動駕駛汽車。實際上仍然需要人來駕駛。一個安全駕駛的司機。不要誤會。我有一輛這樣的車。

And when I'm in it, I am a safety driver.So the question now becomes, what do we do with this?Well, we think that robotaxis, first of all, they will take you specifically from point A to point B.Second of all, when you're not using them, somebody else will be using them.And they are being tested today.When I say that we're on the cusp of finally delivering that vision, there's actually reason to believe it.

我在車裡就是那個安全駕駛的司機。那麼現在問題變成了對此我們該怎麼辦？我們來講講無人駕駛出租車，首先，它們可以專門帶你從A點到B點。其次，當你不用它們的時候，別人會使用它們。現在無人駕駛出租車已經開始測試了。當我說我們即將實現這樣的設想，你完全有理由可以相信我。

At the core of self-driving technology is computer vision.Computer vision is a real-time representation, digital representation, of the world and the interactions within it.It has benefited from leaps and bounds of advancements thanks to computer, sensors, machine learning and software innovation.At the core of computer vision are camera systems.Cameras basically help you see agents such as cars, their locations and their actions, pedestrians, their locations, their actions and their gestures.

自動駕駛技術的核心是計算機視覺。計算機視覺就是對環境的實時呈現，對於這個世界和其內部交互的數字呈現。計算機視覺技術得益於現代科技突飛猛進的發展，尤其是計算機、傳感器、機器學習和軟件創新的發展。計算機視覺技術的核心是攝像系統。攝像機可以幫你查看周遭環境，像汽車以及它們的位置和行動、行人、他們的位置、他們的行為和手勢。

In addition, there's also been a lot of advancements.So one example is our vehicle can see the skeleton framework to show you the direction of travel.Also to give you details, like,are you dealing with a construction worker in a construction zone or are you dealing with a pedestrian that's probably distracted because they are looking on their phone?Now the reality, though, and this is where it gets interesting,

除此之外，還有其他很多進步的地方。比如，車上可以看到代碼框架，告訴你行駛的方向。還能告訴你其他一些細節信息，比如，告訴你是否會在施工區域遇到建築工人或者你是否碰到了在查看手機而分心的行人。然而現在的實際情況也是它變得有趣的地方就是，

is that the camera and the algorithms that help us really cannot yet match the human brain's ability to understand and interpret the environment.They just can't.Even though they provide you really high-resolution imaging that really gives you continuous coverage.That doesn't get fatigued, impaired or, you know, drunk or anything like that, at the end of the day,there are still things that they can't see and they can't measure.

輔助人類的攝像機和算法尚無法和人類大腦的能力相提並論，無法像人那樣理解和解釋環境。就是做不到啊。即使它們可以給你提供高分辨率的成像，為你提供連續的實況圖像，不會疲勞，受損醉酒，或其他類似的情況，到頭來仍然有一些它們看不到無法測量的情況。

So if we want autonomous-driving robotaxis soon, we have to supplement cameras.Let me walk through some examples.So radar gives you the direction of travel and measures the agent's movement within centimeters per second.Lidar gives you objects and shapes in the real world using depth perception as well as long-range and the all-important night vision.And let me tell you about this.

因此，如果我們想要儘快實現無人駕駛出租車，我們必須要有足夠多的攝像機。我們來看一些例子。雷達告訴你行駛的方向和測量物體的運動，精確到厘米每秒的範圍。激光雷達用深度感知遠距離和夜視功能來為你提供現實世界中的物體和其形狀的信息。我再說一下這個。

Because this is important to me personally and people who look like me.Then you have, also, long-wave infrared where you are able to see agents that are emitting heat, such as animals and humans.And that's again, especially at night, super important.Now, every one of these sensors is very powerful by itself.But when you put them together is when the magic happens.

因為這對我個人和跟我相似的人來說很重要。就是還有長波紅外線，你可以靠這個看到周圍散發熱量的物體，像動物和人類。同樣地，這功能在晚上格外重要。每一個傳感器各自的功能都非常強大。結合在一起，就是見證魔法的時刻。

If you see with this vehicle, for example,you have these multiple sensor modalities at all top four corners of the vehicle that basically provide you a 360-degree field of vision,continuously, in a redundant manner.So that we don't miss anything.And this is that same thing with all of the different outputs fused together.And looking at this, basically,and looking at what we see and how we are able to process the data.

如果你看到這樣一輛車，在車身四個角上裝有這些不同傳感器，它們基本上可以為你提供360度的視野，持續不斷地以絕不放過任何細節的態度來提供信息。這樣我們就不會錯過任何信息。其實原理都是一樣的，只是把不同輸出的信息匯集在一起。這樣看，基本上，就是收集我們看到的信息數據，然後看如何處理這些數據。

Then learn.Then continue to improve our driving, is what tells us that we have confidence.This is the right approach.And this time it's actually coming.Now, this is not, by the way, a brand new concept, OK?Humans have been basically using vision systems to assist them for a long time.Let me back up the boat a little bit.Because I know there's a question that everybody's asking, which is: Hey,how are you going to deal with all the scenarios out there on the streets today?

再學習。然後繼續改進自動駕駛的技術，這讓我們更有信心。這是正確的方法。以及這一次，它真的要實現了。順便一提，這不是全新的概念，好吧？人類使用視覺系統來輔助他們的生活已經有很長一段時間了。讓我來退後一步，解釋這個問題。因為我知道大家都很好奇一個問題，就是，如何處理所有在街上可能會發生的情況？

Most of us are drivers.And it's complicated out there.Well, the truth is that there will always be edge scenarios that sit at the boundary of our real-world testing or that are just too dangerous to test on real streets.That is the truth.And it will be the truth for a very long time.Human beings are pretty underrated in their abilities.So what we do is we use simulation.

我們大部分人都會駕駛。路上的各種交通情況很複雜。事實上，確實會有很極端的情況是我們在真實世界測試很難做到的，或者在真實街道上測試太危險了。這是事實。而且這樣的事實會持續很長時間。人類嚴重低估了自己的能力。所以我們要做的就是運用模擬。

And with simulation, we're able to construct millions of scenarios in a fabricated environment so that we can see how our software would react.And that's the simulation footage.You can see we're building the world.We're putting in scenarios.And we can add things, remove things and see how we would react.In addition, we have what's called a human in the loop.This is very similar to aviation systems today.

通過模擬，我們可以在虛構的環境中構建出百萬個場景，這樣就能評估我們的軟件反應如何。這就是模擬鏡頭。你可以看到，我們構建虛擬的世界。我們設定了各種場景。我們可以添加一些東西也可以拿掉一些東西，再看軟件會如何反應。此外，我們還有所謂的「有人參與其中「。這和現在的導航系統非常相似。

We don't want the vehicle to get stuck.And there are rare times where it's not going to know what to do.So we have a team of teleguidance operators that are sitting at a control center.And if the vehicle knows that it's going to be stuck or it doesn't know what to do,it asks for guidance and help and it receives it remotely and then it proceeds.Now, none of these really are new concepts, as I alluded to earlier.

我們不希望車子陷入無法應對的情況。有些時候，車子會不知道該怎麼應對。所以我們有一組遠程指導操作的人員，他們坐在控制中心。如果車子知道自己要卡住了或者它不知道怎麼做，它可以向指導操作員請求幫助，接着，它遠程接受指令再執行收到的指令。這些技術都不是什麼新的概念，就像我之前說的那樣。

Vision systems have been assisting humans for a long time, especially with things that are not visible to the naked eye.So, microscopes, right?We've been studying microbes and cells for a long time.Telescopes: we've been studying and detecting galaxies millions of light-years away for a long time.And both of these have caused us, for example, to transform industries like medicine, farming, astrophysics and much more.

視覺系統已經輔助人類的生活很長一段時間了，尤其是幫助勘察人類肉眼看不到的東西。比如顯微鏡，對吧？我們研究微生物和細胞已經很長一段時間瞭望遠鏡：我們研究和探測數百萬光年之外的星系也已經很長一段時間了。而這些，我們都能舉出例子來，改變了一些行業，像醫藥、農業、天體物理學、還有其他更多的行業。

So when we talk about computer vision, when it started,it was really a thought experiment to see if we could replicate what humans see using cameras.It has now graduated with sensors, computers, AI and software innovation to be about surpassing what humans can see and perceive.We've made a lot of progress in this field.But at the end of the day, we have a lot more to do.

所以當我們說計算機系統時，當它剛開始發展，這確實是一場思想的實驗，看我們能否用攝像機複製人類看東西的能力。現在它已經配備了傳感器、計算機、人工智能還有軟件創新，即將超越人類能看到和認知的極限。我們在這個領域取得了許多進展。但到頭來，我們還有很多事情要做。

And with an autonomous robotaxi, you want it to be safe, right and reliable every single time, which requires rigorous testing and optimization.And when that happens and we reach that state, we will wonder how we ever accepted or tolerated 94 percent of crashes being caused by human error.So with computer vision, we have the opportunity to move from problem-solving to problem-preventing.

對於無人駕駛出租車，你希望它的每一次出行都是安全、正確和可靠的，這需要嚴格的測試和優化。到那時候，我們達到那種狀態時，我們會想知道我們怎能接受或容忍94%的事故是人為造成的。所以有了計算機視覺，我們就有機會從解決問題轉向預防問題。

And I truly, truly believe that the next generation of scientists and technologists in, yes, Silicon Valley, but in Paris, in Senegal,West Africa and all over the world, will be exposed to computer vision applied broadly.And with that, all industries will be transformed.And we will experience the world in a different way.I hope you can join me in agreeing that this is a gift that we almost owe our next generation that is coming.Because there are a lot of things that computer vision will help us solve.

我真的相信，下一代的科學家和技術人員不僅僅在硅谷還要在巴黎、西非的塞納加爾、以及全世界各地都能夠接觸到廣泛的計算機視覺應用。有了計算機視覺的廣泛應用，所有行業都會改變。我們也將以一種不同的方式體驗這個世界。我希望你也和我一樣覺得這是我們欠即將到來的下一代的禮物。因為計算機視覺能幫助我們解決許多問題。

Thank you.

謝謝大家。