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Let me back up the boat a little bit, because I know there's a question that everybody's asking,
我把这个问题退后一步来解释，因为我知道大家都很好奇一个问题，
which is, "Hey, how are you going to deal with all the scenarios out there on the streets today?"
就是，如何处理所有在街上可能会发生的情况？
Most of us are drivers, and it's complicated out there.
我们大部分人都会开车，路上的各种交通情况很复杂。
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.
事实上，确实会有很极端的情况，是我们在真实世界测试很难做到的，或者在真实街道上测试太危险了。
That is the truth, and it will be the truth for a very long time.
这是事实，而且这样的事实会持续很长时间。
Human beings are pretty underrated in their abilities.
人类严重低估了自己的能力。
So what we do is we use simulation.
所以我们要做的就是运用模拟。
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.
通过模拟，我们可以在虚构的环境中构建出百万个场景，这样就能评估我们的软件反应如何。
And that's the simulation footage.
这就是模拟镜头。
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.
你可以看到，我们构建虚拟的世界，我们设定了各种场景，我们可以添加一些东西，也可以拿掉一些东西，看软件会如何反应。
In addition, we have what's called a human in the loop.
此外，我们还有所谓的“有人参与其中”。
This is very similar to aviation systems today.
这和当今的航空系统非常相似。
We don't want the vehicle to get stuck, and there are rare times where it's not going to know what to do.
我们不希望车子陷入无法应对的情况，会有极少数情况它不知道该做什么。
So we have a team of teleguidance operators that are sitting at a control center,
所以我们有一组远程指导操作的人员，他们坐在控制中心，
and if the vehicle knows that it's going to be stuck or it doesn't know what to do,
如果车子知道自己要卡住了或者它不知道怎么做，
it asks for guidance and help and it receives it remotely and then it proceeds.
它可以向指导操作员请求帮助，接着，它远程接受指令再执行收到的指令。
Now, none of these really are new concepts, as I alluded to earlier.
这些技术都不是什么新的概念，就像我之前说的那样。
Vision systems have been assisting humans for a long time, especially with things that are not visible to the naked eye.
视觉系统已经辅助人类的生活很长一段时间了，尤其是帮助勘察人类肉眼看不到的东西。
So ...microscopes, right?
像显微镜，对吧？
We've been studying microbes and cells for a long time.
我们研究微生物和细胞已经很长一段时间了。
Telescopes: we've been studying and detecting galaxies millions of light-years away for a long time.
望远镜：我们研究和探测数百万光年之外的星系也已经很长一段时间了。
And both of these have caused us, for example, to transform industries like medicine, farming, astrophysics and much more.
这些都导致了我们改变了一些行业，比如医药、农业、天体物理学等还有其他更多的行业。
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.
所以当我们说计算机系统时，当它刚开始发展这确实是一场思想的实验，看我们能否用摄像机复制人类看东西的能力。
It has now graduated with sensors, computers, AI and software innovation to be about surpassing what humans can see and perceive.
现在它已经配备了传感器，计算机、人工智能，还有软件创新即将超越人类能看到和认知的能力。
We've made a lot of progress in this field, but at the end of the day, we have a lot more to do.
我们在这个领域取得了许多进展，但到头来，我们还有很多事情要做。
And with an autonomous robotaxi, you want it to be safe, right and reliable every single time, which requires rigorous testing and optimization.
对于无人驾驶出租车，你希望它的每一次出行都是安全、正确和可靠的，这需要严格的测试和优化。
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].
到那时候，我们实现那种状态时，我们会想知道我们是如何能接受或容忍94%的人为错误造成的事故。
So with computer vision, we have the opportunity to move from problem-solving to problem-preventing.
所以有了计算机视觉，我们就有机会从解决问题转向预防问题。
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.
我真的相信，下一代的科学家和技术人员不仅仅在硅谷，还要在巴黎，西非的塞纳加尔以及全世界各地都能够接触到广泛的计算机视觉应用。
And with that, all industries will be transformed, and we will experience the world in a different way.
有了计算机视觉的广泛应用，所有行业都会改变，我们也将以一种不同的方式体验这个世界。
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.
我希望你也和我一样觉得这是我们欠即将到来的下一代的礼物，因为计算机视觉能帮助我们解决许多问题。
Thank you.
谢谢大家。
