Lex FridmanThe following is a conversation with Elon Musk. He’s the CEO of Tesla, SpaceX, Neuralink, and a co-founder of several other companies. This conversation is part of the Artificial Intelligence Podcast. This series includes leading researchers in academia and industry, including CEOs and CTOs of automotive, robotics, AI, and technology companies.以下是与 Elon Musk 的对话。他是 Tesla、SpaceX、Neuralink 的 CEO，也是其他几家公司的联合创始人。本次对话是《人工智能播客》的一部分。该系列邀请了来自学术界和工业界的顶尖研究人员，包括汽车、机器人、AI 及科技公司的 CEO 和 CTO。
Lex FridmanThis conversation happened after the release of the paper from our group at MIT on driver functional vigilance during use of Tesla’s Autopilot. The Tesla team reached out to me, offering a podcast conversation with Mr. Musk. I accepted with full control of questions I could ask and the choice of what is released publicly. I ended up editing out nothing of substance.这次对话发生在我们 MIT 团队发布一篇关于 Tesla Autopilot 使用过程中驾驶员功能性警觉性研究论文之后。Tesla 团队主动联系我，提议安排一场与 Musk 先生的播客对话。我接受了邀约，且完全掌控提问内容以及对外发布的内容选择权。最终，我没有删掉任何实质性的内容。
Lex FridmanI’ve never spoken with Elon before this conversation, publicly or privately. Neither he nor his companies have any influence on my opinion, nor on the rigor and integrity of the scientific method that I practice in my position at MIT. Tesla has never financially supported my research, and I’ve never owned a Tesla vehicle, and I’ve never owned Tesla stock.在这次对话之前，我从未与 Elon 有过任何交流，无论是公开场合还是私下里。他本人及其旗下公司对我的观点没有任何影响，也不影响我在 MIT 从事学术工作时所秉持的科学方法的严谨性与独立性。Tesla 从未在财务上支持我的研究，我也从未拥有过 Tesla 车辆，更从未持有 Tesla 股票。
Lex FridmanThis podcast is not a scientific paper. It is a conversation. I respect Elon as I do all other leaders and engineers I’ve spoken with. We agree on some things and disagree on others. My goal, as always with these conversations, is to understand the way the guest sees the world.这个播客不是一篇学术论文，它是一次对话。我对 Elon 的尊重，与我对其他所有与我交谈过的领导者和工程师的尊重一样。我们在某些方面看法一致，在另一些方面存在分歧。我的目标，一如既往，是理解嘉宾看待世界的方式。
Lex FridmanOne particular point of disagreement in this conversation was the extent to which camera-based driver monitoring will improve outcomes and for how long it will remain relevant for AI-assisted driving. As someone who works on and is fascinated by human-centered artificial intelligence, I believe that, if implemented and integrated effectively, camera-based driver monitoring is likely to be of benefit in both the short term and the long term. In contrast, Elon and Tesla’s focus is on the improvement of Autopilot such that its statistical safety benefits override any concern for human behavior and psychology.这次对话中有一个特别明显的分歧点：基于摄像头的驾驶员监控系统在多大程度上能改善安全结果，以及这种监控在 AI 辅助驾驶时代还会持续多久的相关性。作为一个研究并痴迷于以人为中心的人工智能的人，我认为，如果实施和整合得当，基于摄像头的驾驶员监控在短期和长期内都很可能带来益处。而 Elon 和 Tesla 的关注点则在于持续提升 Autopilot 性能，使其统计安全优势足以压过任何对人类行为与心理因素的顾虑。
Lex FridmanElon and I may not agree on everything, but I deeply respect the engineering and innovation behind the efforts that he leads. My goal here is to catalyze a rigorous, nuanced and objective discussion in industry and academia on AI-assisted driving, one that ultimately makes for a safer and better world. And now, here’s my conversation with Elon Musk.Elon 和我未必在所有事情上都意见一致，但我深深敬重他所主导的这些努力背后的工程创新。我在这里的目标是在工业界和学术界激发一场严谨、细致、客观的关于 AI 辅助驾驶的讨论，最终让这个世界变得更安全、更美好。现在，请欣赏我与 Elon Musk 的对话。
Lex FridmanWhat was the vision, the dream, of Autopilot in the beginning? The big picture system level when it was first conceived and started being installed in 2014, the hardware in the cars? What was the vision, the dream?Autopilot 最初的愿景和梦想是什么？2014 年首次构想、开始在汽车上安装硬件时，在大局观和系统层面，那个愿景和梦想是什么？
Elon MuskI wouldn’t characterize it as a vision or dream. It’s simply that there are obviously two massive revolutions in the automobile industry. One is the transition to electrification, and then the other is autonomy. And it became obvious to me that, in the future, any car that does not have autonomy would be about as useful as a horse. Which is not to say that there’s no use; it’s just rare and somewhat idiosyncratic if somebody has a horse at this point. It’s just obvious that cars will drive themselves completely; it’s just a question of time. And if we did not participate in the autonomy revolution, then our cars would not be useful to people, relative to cars that are autonomous. I mean, an autonomous car is arguably worth five to 10 times more than a car which is not autonomous.我不会把它定性为愿景或梦想。很简单，汽车行业显然正在经历两场巨大的变革。一是向电气化的转型，另一是自动驾驶。对我来说，很显然，在未来，任何没有自动驾驶功能的汽车，大概会像马一样——不是说完全没用，只是到了某个阶段，拥有马是一件罕见且颇为另类的事。汽车最终会完全自动驾驶，这是毋庸置疑的，只是时间问题。如果我们不参与自动驾驶这场变革，那么相比自动驾驶汽车，我们的车对用户来说就没有竞争力了。一辆自动驾驶汽车，其价值可以说是非自动驾驶汽车的 5 到 10 倍。
Lex FridmanIn the long term.从长远来看。
Elon MuskDepends what you mean by long term but, let’s say at least for the next five years, perhaps 10 years.取决于你说的'长远'是多长，但至少在未来 5 年，也许 10 年之内吧。
Lex FridmanSo there are a lot of very interesting design choices with Autopilot early on. First is showing on the instrument cluster, or in the Model 3 on the center stack display, what the combined sensor suite sees. What was the thinking behind that choice? Was there a debate? What was the process?Autopilot 早期有很多非常有趣的设计选择。首先是在仪表盘上，或者 Model 3 的中控屏上，显示组合传感器套件所感知到的内容。这个选择背后的考量是什么？有过争论吗？决策过程是怎样的？
Elon MuskThe whole point of the display is to provide a health check on the vehicle’s perception of reality. The vehicle’s taking in information from a bunch of sensors, primarily cameras, but also radar and ultrasonic, GPS, and so forth. And then, that information is then rendered into vector space with a bunch of objects, with properties like lane lines and traffic lights and other cars. And then, in vector space, that is re-rendered onto a display so you can confirm whether the car knows what’s going on or not by looking out the window.显示屏的整个意义在于提供一个对车辆感知现实状况的健康检查。车辆从一堆传感器获取信息，主要是摄像头，还有雷达、超声波传感器、GPS 等等。然后，这些信息被渲染成向量空间中的一堆对象，带有车道线、交通灯、其他车辆等属性。接着，向量空间中的这些信息再被重新渲染到显示屏上，这样你就可以通过往窗外看，来确认车辆是否知道当前发生了什么。
Lex FridmanRight, I think that’s an extremely powerful thing for people to get an understanding, sort of becoming one with the system and understanding what the system is capable of. Now, have you considered showing more? So if we look at the computer vision, like road segmentation, lane detection, vehicle detection, object detection, underlying the system, there is at the edges, some uncertainty. Have you considered revealing the parts that the uncertainty in the system, the sort of …对，我认为这对于帮助人们建立理解非常有力——某种程度上是与系统融为一体，理解系统的能力边界。那么，你有没有考虑过展示更多信息？比如，如果我们看计算机视觉的底层——道路分割、车道检测、车辆检测、目标检测——在系统的边缘地带，存在一些不确定性。你有没有考虑过把这些不确定性展示出来，那种……
Elon MuskProbabilities associated with, say, image recognition or something like that?比如与图像识别之类相关的概率？
Lex FridmanYeah, so right now, it shows the vehicles in the vicinity, a very clean crisp image, and people do confirm that there’s a car in front of me, and the system sees there’s a car in front of me, but to help people build an intuition of what computer vision is, by showing some of the uncertainty.对，现在它显示的是周围车辆，图像非常干净清晰，人们可以确认'我前面有辆车，系统也看到了那辆车'，但如果能展示一些不确定性，帮助人们建立对计算机视觉的直觉感知……
Elon MuskWell, in my car, I always look at this with the debug view. And there are two debug views. One is augmented vision, which I’m sure you’ve seen, where it’s basically we draw boxes and labels around objects that are recognized. And then there’s what we call the visualizer, which is basically vector space representation, summing up the input from all sensors. That does not show any pictures, but it basically shows the car’s view of the world in vector space. But I think this is very difficult for normal people to understand, they would not know what thing they’re looking at.在我的车上，我一直用 debug 视图来看这些。有两种 debug 视图。一种是增强视觉模式，我相信你见过，基本上就是在识别出的物体周围画框并标注标签。另一种是我们称之为可视化器的东西，本质上是向量空间的表示，汇总了所有传感器的输入。那个视图不显示任何图片，但基本上以向量空间的方式呈现车辆对世界的理解。不过我认为这对普通人来说非常难以理解，他们根本不知道自己在看什么。
Lex FridmanSo it’s almost an HMI challenge through the current things that are being displayed is optimized for the general public understanding of what the system’s capable of.所以这几乎是一个 HMI（人机交互）的挑战——当前显示的内容是针对大众对系统能力的理解而优化的。
Elon MuskIf you have no idea how computer vision works or anything, you can still look at the screen and see if the car knows what’s going on. And then if you’re a development engineer, or if you have the development build like I do, you can see all the debug information. But this would just be like total gibberish to most people.如果你对计算机视觉一窍不通，你仍然可以看着屏幕，判断这辆车是否知道正在发生什么。而如果你是开发工程师，或者像我一样有开发版本，你就可以看到所有的 debug 信息。但这些信息对大多数人来说简直是天书。
Lex FridmanWhat’s your view on how to best distribute effort? So there are three, I would say, technical aspects of Autopilot that are really important. So it’s the underlying algorithms, like the neural network architecture, there’s the data that it’s trained on, and then there’s the hardware development and maybe others. So, look, algorithm, data, hardware. You only have so much money, only have so much time. What do you think is the most important thing to allocate resources to? Or do you see it as pretty evenly distributed between those three?你对如何最优分配精力有什么看法？我认为 Autopilot 有三个非常重要的技术层面：底层算法，比如神经网络架构；训练数据；以及硬件开发，也许还有其他方面。算法、数据、硬件，资金有限，时间有限，你认为最重要的是把资源集中在哪里？还是说你认为这三者的投入应该比较均衡？
Elon MuskWe automatically get vast amounts of data because all of our cars have eight external-facing cameras, and radar, and usually 12 ultrasonic sensors, GPS obviously, and IMU. And we’ve got about 400,000 cars on the road that have that level of data. Actually, I think you keep quite close track of it actually.数据这块，我们自动获得了海量数据，因为我们所有的车都配备了 8 个面向外部的摄像头、雷达，通常还有 12 个超声波传感器、当然还有 GPS 和 IMU。我们路上大概有 40 万辆具备这种数据采集能力的车。其实，我觉得你对这个数字掌握得相当准确。
Lex FridmanYes.是的。
Elon MuskYeah, so we’re approaching half a million cars on the road that have the full sensor suite. I’m not sure how many other cars on the road have this sensor suite, but I’d be surprised if it’s more than 5,000, which means that we have 99% of all the data.是的，我们路上接近 50 万辆具备完整传感器套件的车。我不确定路上还有多少其他品牌的车配备了这套传感器，但如果超过 5,000 辆我会感到惊讶，这意味着我们掌握了 99% 的数据。
Lex FridmanSo there’s this huge inflow of data.所以数据的流入量是非常巨大的。
Elon MuskAbsolutely, a massive inflow of data. And then it’s taken us about three years, but now we’ve finally developed our full self-driving computer, which can process an order of magnitude as much as the NVIDIA system that we currently have in the cars, and it’s really just to use it, you unplug the NVIDIA computer and plug the Tesla computer in and that’s it. In fact, we still are exploring the boundaries of its capabilities. We’re able to run the cameras at full frame-rate, full resolution, not even crop the images, and it’s still got headroom even on one of the systems. The full self-driving computer is really two computers, two systems on a chip, that are fully redundant. So you could put a boat through basically any part of that system, and it still works.绝对是，海量的数据流入。然后我们花了大约三年时间，终于研发出了我们完整的自动驾驶计算机。它的处理能力是我们目前车上搭载的 NVIDIA 系统的一个数量级，而且使用起来极其简单——你把 NVIDIA 计算机拔掉，插上 Tesla 计算机就行了。事实上，我们现在还在探索它的能力边界。我们可以以全帧率、全分辨率运行摄像头，甚至不需要裁剪图像，在其中一个系统上仍然还有余量。完整的自动驾驶计算机实际上是两台计算机、两个片上系统，完全冗余。所以基本上你可以对这个系统的任何部分开个大洞，它照样能运行。
Lex FridmanThe redundancy, are they perfect copies of each other or …那个冗余设计，这两个系统是完全一样的副本，还是……
Elon MuskYeah.是的。
Lex FridmanOh, so it’s purely for redundancy as opposed to an arguing machine kind of architecture where they’re both making decisions; this is purely for redundancy.哦，所以它纯粹是为了冗余，而不是那种两者都在做决策的'对抗机器'架构——这纯粹是为了冗余。
Elon MuskThink of it more like it’s a twin-engine commercial aircraft. The system will operate best if both systems are operating, but it’s capable of operating safely on one. So, as it is right now, we can just run… . We haven’t even hit the edge of performance, so there’s no need to actually distribute functionality across both SOCs. We can actually just run a full duplicate on each one.把它想成双发动机商用飞机更合适。两个系统都运行时性能最佳，但单靠一个也能安全运行。就目前而言，我们可以……我们连性能极限都还没摸到，所以根本没必要把功能分散到两个 SOC 上。我们实际上可以直接在每个系统上各跑一个完整的副本。
Lex FridmanSo you haven’t really explored or hit the limit of the system.所以你们还没有真正探索过或触及系统的极限。
Elon MuskNo, not yet, the limit, no.还没，极限，还没。
Lex FridmanSo the magic of deep learning is that it gets better with data. You said there’s a huge inflow of data, but the thing about driving, the really valuable data to learn from, is the edge cases. I’ve heard you talk somewhere about Autopilot disengagements being an important moment of time to use. Are there other edge cases, or perhaps can you speak to those edge cases, what aspects of them might be valuable, or if you have other ideas, how to discover more and more edge cases in driving?深度学习的神奇之处在于它随着数据量的增加而变得更好。你说有海量数据流入，但关于驾驶，真正有学习价值的数据是边缘案例。我听你在某处谈到 Autopilot 脱离接管是一个重要的时间节点。还有其他边缘案例吗？或者你能谈谈那些边缘案例——它们哪些方面可能有价值？又或者你有没有其他想法，来挖掘越来越多的驾驶边缘案例？
Elon MuskWell, there’s a lot of things that are learned. There are certainly edge cases where say, somebody’s on Autopilot, and they take over, and then that’s a trigger that goes out to our system and says, okay, did they take over for convenience, or did they take over because the Autopilot wasn’t working properly? There’s also, let’s say we’re trying to figure out, what is the optimal spline for traversing an intersection. Then the ones where there are no interventions are the right ones. Then you say, okay when it looks like this, do the following. And then you get the optimal spline for navigating a complex intersection.嗯，可以学习的东西有很多。确实存在这样的边缘案例：有人在使用 Autopilot 时接管了方向盘，这会触发一个信号发送到我们的系统，问题是——他们接管是为了方便，还是因为 Autopilot 运行不正常？还有，比如我们想搞清楚通过一个路口的最优行驶曲线是什么。那些没有任何干预的案例才是正确的。然后你就说，好，当看起来是这种情况时，就这样做。这样你就得到了通过复杂路口的最优行驶曲线。
Lex FridmanSo there’s kind of the common case. So you’re trying to capture a huge amount of samples of a particular intersection when things went right, and then there’s the edge case where, as you said, not for convenience, but something didn’t go exactly right.所以有某种普通案例。你在试图采集特定路口大量样本——事情进展顺利的案例；然后是边缘案例，就像你说的，不是为了方便，而是某些事情没有完全正确。
Elon MuskSo if somebody started manual control from Autopilot. And really, the way to look at this is to view all input as error. If the user had to do input, there’s something; all input is error.如果有人从 Autopilot 切换到手动控制。其实，看待这个问题的方式是：把所有输入都视为错误。如果用户不得不进行操作，那说明有什么问题——所有输入都是错误。
Lex FridmanThat’s a powerful line to think of it that way because it may very well be error, but if you want to exit the highway, or if it’s a navigation decision that Autopilot’s not currently designed to do, then the driver takes over, how do you know the difference?这句话很有力，这样思考很有意思。但可能确实是错误，不过如果你想下高速，或者这是一个 Autopilot 目前不具备的导航决策，驾驶员接管了——你怎么区分两者的差别？
Elon MuskYeah, that’s gonna change with Navigate on Autopilot, which we’ve just released, and without stalk-confirm. Assuming control in order to do a lane change, or exit a freeway, or doing a highway interchange, the vast majority of that will go away with the release that just went out.是的，这个问题随着 Navigate on Autopilot 的推出会有所改变，我们刚刚发布了这个功能，而且不需要拨杆确认。为了变道、驶出高速、或者完成高速公路交汇处的操作而接管的情况，随着这次刚推出的版本，绝大多数都会消失。
Lex FridmanYeah, so that, I don’t think people quite understand how big of a step that is.是的，我觉得人们并没有真正意识到这是多大的一步。
Elon MuskYeah, they don’t. If you drive the car, then you do.是的，他们不知道。开过那辆车你就明白了。
Lex FridmanSo you still have to keep your hands on the steering wheel currently when it does the automatic lane change. There are these big leaps through the development of Autopilot, through its history and, what stands out to you as the big leaps? I would say this one, Navigate on Autopilot without having to confirm is a huge leap.所以现在执行自动变道时，你仍然要把手放在方向盘上。在 Autopilot 的发展历程中，有一些重大的飞跃，哪些让你印象最深刻？我觉得这次——Navigate on Autopilot 不需要手动确认——是一个巨大的飞跃。
Elon MuskIt is a huge leap. It also automatically overtakes slow cars. So it’s both navigation and seeking the fastest lane. So it’ll overtake slow cars and exit the freeway and take highway interchanges, and then we have traffic light recognition, which was introduced initially as a warning. I mean, on the development version that I’m driving, the car fully stops and goes at traffic lights.这确实是一个巨大的飞跃。它还能自动超越慢速车辆。所以它既能导航，又能主动寻找最快的车道。它会超越慢车、驶出高速、完成高速公路交汇处的转换，我们还有交通灯识别功能，最初是以警告形式引入的。在我开的开发版本上，车辆会在交通灯前完全停下并等灯变绿再走。
Lex FridmanSo those are the steps, right? You’ve just mentioned some things that are an inkling of a step towards full autonomy. What would you say are the biggest technological roadblocks to full self-driving?这些就是那些步骤了，对吧——你刚才提到的这些东西，都是走向完全自动驾驶的一点点迹象。你认为实现完全自动驾驶最大的技术障碍是什么？
Elon MuskActually, the full self-driving computer that we just, at Tesla, call FSD computer that’s now in production, so if you order any Model S or X, or any Model 3 that has the full self-driving package, you’ll get the FSD computer. That’s important to have enough base computation. Then refining the neural net and the control software. All of that can just be provided as an over-the-air update. The thing that’s really profound, and what I’ll be emphasizing at the investor day that we’re having focused on autonomy, is that the car is currently being produced, with the hardware currently being produced, is capable of full self-driving.其实，我们 Tesla 刚推出的那台完整自动驾驶计算机——我们内部叫它 FSD computer——现在已经量产了，所以如果你订购任何一辆 Model S、Model X，或者任何配备完整自动驾驶套件的 Model 3，都会搭载 FSD computer。拥有足够的基础算力很重要。然后就是对神经网络和控制软件的持续优化——所有这些都可以通过 OTA 空中升级来提供。真正深刻的事情——也是我会在我们专注于自动驾驶的投资者日上重点强调的——是，现在正在生产的汽车，搭载的现有硬件，具备实现完全自动驾驶的能力。
Lex FridmanBut capable is an interesting word because …但'具备能力'这个说法很有意思，因为……
Elon MuskThe hardware is. And as we refine the software, the capabilities will increase dramatically, and then the reliability will increase dramatically, and then it will receive regulatory approval. So essentially, buying a car today is an investment in the future. I think the most profound thing is that if you buy a Tesla today, I believe you’re buying an appreciating asset, not a depreciating asset.硬件具备。随着我们持续优化软件，其功能会大幅提升，然后可靠性也会大幅提升，之后就会获得监管批准。所以本质上，今天买一辆车就是在为未来投资。我觉得最深刻的是：如果你今天买一辆 Tesla，我相信你买的是一项增值资产，而不是一项贬值资产。
Lex FridmanSo that’s a really important statement there because if the hardware is capable enough, that’s the hard thing to upgrade usually.这是一个非常重要的说法，因为如果硬件能力足够，那通常才是最难升级的部分。
Elon MuskYes, exactly.是的，正是。
Lex FridmanThen the rest is a software problem.那剩下的就是软件问题了。
Elon MuskYes, software has no marginal cost, really.是的，软件基本上没有边际成本。
Lex FridmanBut, what’s your intuition on the software side? How hard are the remaining steps to get it to where the experience, not just the safety, but the full experience, is something that people would enjoy?但你对软件这边的直觉是什么？距离让整体体验——不只是安全性，而是完整的使用体验——达到让人真正享受的程度，剩下的步骤有多难？
Elon MuskI think people enjoy it very much so on highways. It’s a total game-changer for quality of life for using Tesla Autopilot on the highways. So it’s really just extending that functionality to city streets, adding in the traffic light recognition, navigating complex intersections, and then being able to navigate complicated parking lots so the car can exit a parking space and come and find you, even if it’s in a complete maze of a parking lot. And then it can just drop you off and find a parking spot by itself.我认为人们在高速公路上已经非常享受了。在高速公路上使用 Tesla Autopilot，对生活质量来说是彻底的改变。所以现在真正要做的就是把这个功能延伸到城市街道上，加入交通灯识别，处理复杂路口，然后能够应对复杂的停车场——让车辆自己驶出停车位来找到你，哪怕是在迷宫般的停车场里。然后它还能自己去找停车位。
Lex FridmanYeah, in terms of enjoyability, and something that people would actually find a lot of use from the parking lot, it’s rich of annoyance when you have to do it manually, so there’s a lot of benefits to be gained from automation there. So, let me start injecting the human into this discussion a little bit. So let’s talk about full autonomy; if you look at the current level for vehicles being tested on road like Waymo and so on, they’re only technically autonomous; they’re really level two systems with just a different design philosophy because there’s always a safety driver in almost all cases, and they’re monitoring the system.是的，从享受性和实用性来说，停车场确实充满了让人抓狂的操作，手动完成真的很烦，所以自动化带来的收益会非常大。那么，让我开始把人的因素引入这个讨论。我们来谈谈完全自动驾驶——看看目前道路上测试的车辆，比如 Waymo 等，它们在技术上只能算是自动驾驶，但实际上更像是不同设计理念的 Level 2 系统，因为几乎所有情况下都有安全驾驶员在监控系统。
Elon MuskRight.对。
Lex FridmanDo you see Tesla’s full self-driving as still, for a time to come, requiring supervision of the human being. So its capabilities are powerful enough to drive but nevertheless requires a human to be still supervising, just like a safety driver is in other fully autonomous vehicles?你认为 Tesla 的完全自动驾驶在未来一段时间内，仍然需要人类的监督吗？就是说，它的能力已经足以驾驶，但仍然需要人在旁监督，就像其他完全自动驾驶车辆里的安全驾驶员一样？
Elon MuskI think it will require detecting hands on wheel for at least six months or something like that from here. Really it’s a question of, from a regulatory standpoint, how much safer than a person does Autopilot need to be for it to be okay to not monitor the car. And this is a debate that one can have, and then, but you need a large amount of data, so you can prove, with high confidence, statistically speaking, that the car is dramatically safer than a person. And that adding in the person monitoring does not materially affect the safety. So it might need to be 200 or 300% safer than a person.我认为从现在起至少 6 个月左右的时间内，它仍然需要检测双手是否在方向盘上。真正的问题是，从监管角度来说，Autopilot 需要比人类安全多少，才能被允许不再监控车辆。这是一个可以讨论的问题，但你需要大量数据，才能以高置信度从统计上证明，这辆车比人类驾驶安全得多，而且加入人的监控不会对安全性产生实质性影响。所以可能需要比人类安全 200% 或 300%。
Lex FridmanAnd how do you prove that?你怎么证明这一点？
Elon MuskIncidents per mile.每英里事故次数。
Lex FridmanIncidents per mile.每英里事故次数。
Elon MuskYeah.是的。
Lex Fridmanso crashes and fatalities …所以是碰撞和死亡……
Elon MuskYeah, fatalities would be the factor, but there are just not enough fatalities to be statistically significant, at scale. But there are enough crashes; there are far more crashes than there are fatalities. So you can assess what is the probability of a crash. Then there’s another step, which is probability of injury. And probability of permanent injury, and the probability of death. And all of those need to be much better than a person, by at least, perhaps, 200%.是的，死亡是最关键的指标，但在这个规模下，死亡事故数量还不够多，无法具有统计显著性。但碰撞足够多——碰撞数量远远多于死亡数量。所以你可以评估碰撞的概率，然后还有一步，是受伤的概率，以及永久性伤害的概率，还有死亡的概率。所有这些都需要比人类好得多，至少可能要好 200%。
Lex FridmanAnd you think there’s the ability to have a healthy discourse with the regulatory bodies on this topic?你认为在这个话题上，能与监管机构展开有成效的对话吗？
Elon MuskI mean, there’s no question that regulators paid a disproportionate amount of attention to that which generates press; this is just an objective fact. And Tesla generates a lot of press. So, in the United States, there are, I think, almost 40,000 automotive deaths per year. But if there are four in Tesla, they will probably receive a thousand times more press than anyone else.有一点毋庸置疑，监管机构会对那些能产生媒体关注的事情给予不成比例的重视——这是一个客观事实。而 Tesla 会产生大量的媒体关注。在美国，我认为每年汽车死亡事故将近 40,000 起。但如果 Tesla 死了 4 个人，收到的媒体关注可能是其他任何人的 1,000 倍。
Lex FridmanSo the psychology of that is actually fascinating, I don’t think we’ll have enough time to talk about that, but I have to talk to you about the human side of things. So, myself and our team at MIT recently released a paper on functional vigilance of drivers while using Autopilot. This is work we’ve been doing since Autopilot was first released publicly, over three years ago, collecting video of driver faces and driver body. So I saw that you tweeted a quote from the abstract, so I can at least guess that you’ve glanced at it.这背后的心理学其实很迷人，我们可能没有足够的时间深入探讨，但我必须和你聊聊人的因素。所以，我和我们 MIT 团队最近发布了一篇关于驾驶员使用 Autopilot 时功能性警觉性的论文。自从 Autopilot 三年多前首次公开发布以来，我们一直在做这项工作，收集驾驶员面部和肢体的视频。我看到你发推特引用了摘要中的一句话，所以至少可以猜测你扫了一眼。
Elon MuskYeah, I read it.是的，我读了。
Lex FridmanCan I talk you through what we found?我可以带你了解一下我们的发现吗？
Elon MuskSure.当然。
Lex FridmanOkay, it appears that in the data that we’ve collected, that drivers are maintaining functional vigilance such that, we’re looking at 18,000 disengagements from Autopilot, 18,900, and annotating were they able to take over control in a timely manner. So they were there, present, looking at the road to take over control, okay. So this goes against what many would predict from the body of literature on vigilance with automation. Now the question is, do you think these results hold across the broader population. So, ours is just a small subset. One of the criticism is that there’s a small minority of drivers that may be highly responsible, where their vigilance decrement would increase with Autopilot use.好的，从我们收集的数据来看，驾驶员似乎维持着功能性的警觉状态——我们分析了 18,000 次 Autopilot 脱离接管，共 18,900 次，并对其进行了标注：他们是否能够及时接管控制权。所以他们在场、注意着道路、能够接管——好的。这与许多人根据自动化警觉性文献体系所预测的结果相悖。现在的问题是，你认为这些结果在更广泛的人群中也成立吗？我们的样本只是一个很小的子集。一个批评是，有小部分极度负责任的驾驶员，他们在使用 Autopilot 时警觉性下降幅度可能会随时间增加。
Elon MuskI think this is all really going to be swept; I mean, the system’s improving so much, so fast, that this is going to be a moot point very soon. Where vigilance is, if something’s many times safer than a person, then adding a person does – the effect on safety is limited. And, in fact, it could be negative.我认为这一切最终都会被扫到一边——系统正在如此快速地提升，这个问题很快就会变得毫无意义。当一个系统的安全性比人类高出许多倍时，加入人的监控对安全性的影响就很有限了。事实上，反而可能是负面的。
Lex FridmanThat’s really interesting, so the fact that a human may, some percent of the population may exhibit a vigilance decrement, will not affect overall statistics, numbers on safety?这很有意思——也就是说，即使一部分人群确实出现了警觉性下降，也不会影响整体的安全统计数据？
Elon MuskNo, in fact, I think it will become, very, very quickly, maybe even towards the end of this year, but I would say, I’d be shocked if it’s not next year at the latest, that having a human intervene well decrease safety. Decrease, like imagine if you’re in an elevator. Now it used to be that there were elevator operators. And you couldn’t go on an elevator by yourself and work the lever to move between floors. And now nobody wants an elevator operator because the automated elevator that stops at the floors is much safer than the elevator operator. And in fact, it would be quite dangerous to have someone with a lever that can move the elevator between floors.不会。事实上，我认为很快——非常非常快，也许今年年底之前，但我说最晚明年，我会震惊——人类介入反而会降低安全性。降低。想想电梯的例子。以前电梯是有操作员的。你不能独自乘电梯，你需要有人操控手柄在楼层间移动。而现在没有人想要电梯操作员，因为按楼层停靠的自动电梯比操作员安全得多。事实上，如果让一个人拿着手柄随意控制电梯在楼层间移动，那反而是相当危险的。
Lex FridmanSo, that’s a really powerful statement and a really interesting one, but I also have to ask from a user experience and from a safety perspective, one of the passions for me algorithmically is camera-based detection of just sensing the human, but detecting what the driver’s looking at, cognitive load, body pose, on the computer vision side that’s a fascinating problem. And there are many in the industry who believe you have to have camera-based driver monitoring. Do you think there could be benefit gained from driver monitoring?好，这是一个非常有力的说法，也非常有趣，但我也必须从用户体验和安全的角度来问你一个问题。我在算法层面的一大热情，就是基于摄像头来感知人类——检测驾驶员注视的方向、认知负荷、肢体姿态，从计算机视觉的角度来说这是个迷人的问题。业内很多人认为必须要有基于摄像头的驾驶员监控。你认为驾驶员监控能带来好处吗？
Elon MuskIf you have a system that’s at or below a human level of reliability, then driver monitoring makes sense. But if your system is dramatically better, more reliable than a human, then driver monitoring does not help much. And, like I said, if you’re in an elevator, do you really want someone with a big lever, some random person operating the elevator between floors? I wouldn’t trust that. I would rather have the buttons.如果你的系统可靠性与人类相当或低于人类，那驾驶员监控是有意义的。但如果你的系统比人类可靠得多，那驾驶员监控帮助就不大了。就像我说的，如果你在电梯里，你真的想要一个拿着大手柄的随机陌生人在楼层间操控电梯吗？我不会相信那个。我宁愿按按钮。
Lex FridmanOkay, you’re optimistic about the pace of improvement of the system, from what you’ve seen with the full self-driving car computer.好的，你对系统改进的速度持乐观态度，从你对完整自动驾驶计算机的了解来看。
Elon MuskThe rate of improvement is exponential.提升速度是指数级的。
Lex FridmanSo, one of the other very interesting design choices early on that connects to this is the operational design domain of Autopilot. So, where Autopilot is able to be turned on. So in contrast, another vehicle system that we were studying is the Cadillac Super Cruise system that’s, in terms of ODD, very constrained to particular kinds of highways, well mapped, tested, but it’s much narrower than the ODD of Tesla vehicles.那么，Autopilot 早期另一个非常有趣的设计选择，与这个有所关联，就是 Autopilot 的操作设计域（ODD）——也就是 Autopilot 可以被激活的场景范围。相比之下，我们研究的另一个车辆系统是 Cadillac Super Cruise，它的 ODD 在范围上非常受限，只限于特定种类的高速公路，地图完备且经过测试，比 Tesla 车辆的 ODD 要窄得多。
Elon MuskIt’s like ADD (both laugh).有点像 ADD（两人大笑）。
Lex FridmanYeah, that’s good; that’s a good line. What was the design decision in that different philosophy of thinking, where – there are pros and cons. What we see with a wide ODD is Tesla drivers are able to explore more the limitations of the system, at least early on, and they understand, together with the instrument cluster display, they start to understand what are the capabilities, so that’s a benefit. The con is you’re letting drivers use it basically anywhere …哈，这个比喻不错，说得挺妙的。这种不同哲学背后的设计决策是什么——各有利弊。我们看到，宽泛的 ODD 使得 Tesla 驾驶员至少在早期能够更多地探索系统的边界，结合仪表盘显示，他们开始理解系统的能力范围，这是个优点。缺点是你让驾驶员基本上可以在任何地方使用它……
Elon MuskAnywhere that it can detect lanes with confidence.任何它能自信地检测到车道线的地方。
Lex FridmanWas there a philosophy, design decisions, that were challenging, that were being made there? Or from the very beginning, was that done on purpose, with intent?当时有没有什么哲学层面的思考、有难度的设计决策？还是从一开始就是有意为之、有明确意图的？
Elon MuskFrankly, it’s pretty crazy letting people drive a two-ton death machine manually. That’s crazy, like, in the future will people be like, I can’t believe anyone was just allowed to drive one of these two-ton death machines, and they just drive wherever they wanted. Just like elevators, you could just move that elevator with that lever wherever you wanted, can stop it halfway between floors if you want. It’s pretty crazy, so, it’s going to seem like a mad thing in the future that people were driving cars.说实话，让人类手动驾驶一台两吨重的死亡机器本身就挺疯狂的。真的疯狂——就像，未来的人们会想：难以置信，当年居然允许任何人手动驾驶这种两吨重的死亡机器，想去哪就去哪。就像电梯，你可以拿着手柄把电梯移到任何地方，想停在两层之间就停在那里。这挺疯狂的。在未来，人们手动驾驶汽车这件事会显得非常荒唐。
Lex FridmanSo, I have a bunch of questions about the human psychology, about behavior, and so on …那么，我有一堆关于人类心理、行为等方面的问题……
Elon MuskThat’s moot, it’s totally moot.那些都无关紧要，完全无关紧要。
Lex FridmanBecause you have faith in the AI system; not faith but, both on the hardware side and the deep learning approach of learning from data, will make it just far safer than humans.因为你对 AI 系统有信心——不是信念，而是确信——无论是在硬件层面还是从数据学习的深度学习方法上，都会让它远比人类安全。
Elon MuskYeah, exactly.是的，正是。
Lex FridmanRecently, there were a few hackers who tricked Autopilot to act in unexpected ways for the adversarial examples. So we all know that neural network systems are very sensitive to minor disturbances, these adversarial examples, on input. Do you think it’s possible to defend against something like this for the industry?最近，有几个黑客用对抗性样本欺骗 Autopilot，让它做出了意外的行为。我们都知道神经网络系统对输入的微小扰动非常敏感——这就是对抗性样本。你认为这个行业有可能防御这类攻击吗？
Elon MuskSure (both laugh), yeah.当然（两人大笑），是的。
Lex FridmanCan you elaborate on the confidence behind that answer?你能具体说说这个答案背后的自信从何而来吗？
Elon MuskA neural net is just basically a bunch of matrix math. But you have to be a very sophisticated, somebody who really understands neural nets and basically reverse-engineers how the matrix is being built, and then create a little thing that just exactly causes the matrix math to be slightly off. But it’s very easy to block that by having what would basically negative recognition; it’s like if the system sees something that looks like a matrix hack, exclude it. It’s such an easy thing to do.神经网络本质上就是一堆矩阵运算。但你得是一个非常老练的人，真正理解神经网络，基本上能逆向工程出矩阵是怎么构建的，然后构造出一个能恰好让矩阵运算略微出错的东西。但要屏蔽这个非常容易，基本上就是做负识别——如果系统看到的东西看起来像是一个矩阵破解，就把它排除掉。这太容易了。
Lex FridmanSo learn both on the valid data and the invalid data, so basically learn on the adversarial examples to be able to exclude them.所以就是同时在有效数据和无效数据上学习——基本上在对抗性样本上训练，从而能够把它们排除掉。
Elon MuskYeah, you like basically want to both know what is a car and what is definitely not a car. And you train for, this is a car, and this is definitely not a car. Those are two different things. People have no idea of neural nets, really. They probably think neural nets involves a fishing net or something.是的，你基本上既要知道什么是车，也要知道什么绝对不是车。你训练的是：这是车，以及这绝对不是车。这是两件不同的事。一般人对神经网络真的一无所知。他们大概以为神经网络跟渔网之类的有关系。
Lex FridmanSo, as you know, taking a step beyond just Tesla and Autopilot, current deep learning approaches still seem, in some ways, to be far from general intelligence systems. Do you think the current approaches will take us to general intelligence, or do totally new ideas need to be invented?那么，超越 Tesla 和 Autopilot 来看，目前的深度学习方法在某些方面似乎距离通用智能系统仍然很遥远。你认为现有方法能带我们走向通用智能，还是说需要发明全新的思路？
Elon MuskI think we’re missing a few key ideas for artificial general intelligence. But it’s going to be upon us very quickly, and then we’ll need to figure out what shall we do if we even have that choice. It’s amazing how people can’t differentiate between, say, the narrow AI that allows a car to figure out what a lane line is and navigate streets versus general intelligence. Like these are just very different things. Like your toaster and your computer are both machines, but one’s much more sophisticated than another.我认为我们在人工通用智能上还缺少几个关键的想法。但它会非常快地降临在我们面前，然后我们需要想清楚，如果我们有这个选择的话，该怎么办。令人惊讶的是，人们无法区分，比方说，让汽车识别车道线和在街道上导航的窄 AI，和通用智能之间的差别。这就好像，你的烤面包机和你的电脑都是机器，但其中一个比另一个复杂得多。
Lex FridmanYou’re confident with Tesla you can create the world’s best toaster …你有信心 Tesla 能造出世界上最好的烤面包机……
Elon MuskThe world’s best toaster, yes. The world’s best self-driving… yes, to me right now, this seems game, set, and match. I mean, I don’t want us to be complacent or over-confident, but that’s what it, that is just literally how it appears right now, I could be wrong, but it appears to be the case that Tesla is vastly ahead of everyone.世界上最好的烤面包机，是的。世界上最好的自动驾驶……是的，对我来说，现在这看起来已经是局势、盘局、胜局了。我的意思是，我不想自满或过度自信，但这就是它目前看上去的样子，我可能是错的，但现在看起来确实是这种情况——Tesla 遥遥领先于所有人。
Lex FridmanDo you think we will ever create an AI system that we can love and loves us back in a deep, meaningful way, like in the movie ‚Her‘?你认为我们是否有可能创造出一个 AI 系统，我们能爱上它，它也以深刻、有意义的方式爱我们——就像电影《她》（Her）里那样？
Elon MuskI think AI will be capable of convincing you to fall in love with it very well.我认为 AI 完全有能力说服你爱上它。
Lex FridmanAnd that’s different than us humans?那跟我们人类有什么不同呢？
Elon MuskYou know, we start getting into a metaphysical question of, do emotions and thoughts exist in a different realm than the physical? And maybe they do, maybe they don’t, I don’t know. But from a physics standpoint, I tend to think of things, you know, like physics was my main sort of training, and from a physics standpoint, essentially, if it loves you in a way that you can’t tell whether it’s real or not, it is real.你知道，我们开始进入一个形而上学的问题：情感和思想是否存在于一个不同于物质世界的领域？也许是的，也许不是，我不知道。但从物理学的角度，我倾向于这样思考——物理学曾是我主要的训练背景——从物理学的角度，本质上，如果它以一种你无法分辨真假的方式爱你，那它就是真实的。
Lex FridmanThat’s a physics view of love.这是一种对爱的物理学视角。
Elon MuskYeah (laughs), if you cannot prove that it does not, if there’s no test that you can apply that would make it – allow you to tell the difference, then there is no difference.是的（笑），如果你无法证明它不是，如果没有任何测试能让你区分两者的差别，那么差别就不存在。
Lex FridmanRight, and it’s similar to seeing our world as a simulation; there may not be a test to tell the difference between what the real world …对，这和把我们的世界看成一个模拟很像——也许没有测试能区分真实世界……
Elon MuskYes.是的。
Lex Fridman… and the simulation, and therefore, from a physics perspective, it might as well be the same thing.……和模拟，因此从物理学的角度来说，两者也许本质上是同一回事。
Elon MuskYes, and there may be ways to test whether it’s a simulation. There might be, I’m not saying there are. But you could certainly imagine that a simulation could correct, that once an entity in the simulation found a way to detect the simulation, it could either pause the simulation, start a new simulation, or do one of many other things that then corrects for that error.是的，而且可能存在检测它是否是模拟的方法。也许有，我没说一定有。但你完全可以想象，一个模拟可以做出修正——一旦模拟中的某个实体找到了检测模拟的方法，模拟就可以暂停、重启，或者采取其他许多措施来纠正这个错误。
Lex FridmanSo when, maybe you or somebody else creates an AGI system, and you get to ask her one question, what would that question be?那么，当某天你或者其他人创造出一个 AGI 系统，你可以问它一个问题，那个问题会是什么？
Elon MuskWhat’s outside the simulation?模拟之外是什么？
Lex FridmanElon, thank you so much for talking today; it’s a pleasure.Elon，非常感谢你今天的交流，很荣幸。
Elon MuskAll right, thank you.好的，谢谢你。