马斯克：今年搞定L5级自动驾驶基本功能，正组建中国研发团队【附对话实录】

[本文来自：www.ii77.com]

[转载出处：www.ii77.com]

马斯克：特斯拉本年搞定L5级主动驾驶根基功能

7月9日上午，2020年世界人工智能大会在上海揭幕，马斯克作为受邀嘉宾线上出席此次大会，并接管采访。

▲世界人工智能大会现场

在采访中，马斯克在主动驾驶、人工智能手艺两个方面阐述了特斯拉的最新进展。他透露，特斯拉将在本年根基实现L5级主动驾驶手艺根基功能的研发工作。

针对主动驾驶手艺，马斯克透露，今朝特斯拉主动辅助驾驶在中国市场应用还不错。

不外，因为特斯拉主动驾驶的工程斥地集中在美国加州，所以主动辅助驾驶功能在美国的应用的更好，在加州最好。

是以，为适应列国各区域分歧的交通状况，今朝特斯拉正在中国竖立主动驾驶工程团队。在中国，还要进行很多原创性的工程斥地，而且特斯拉如今已经起头雇用精良的研发工程师。

对于高级主动驾驶手艺，马斯克透露，他对L5级主动驾驶手艺非常有决心，将在本年完成斥地L5级主动驾驶系统的根基功能。

他透露，L5级主动驾驶系统最难题的处所在于平安级别需要更高，若是仅达到人类驾驶的平安水平远远不敷。

针对AI芯片的成长，马斯克透露，Autopilot主动辅助驾驶芯片鞭策了AI芯片的成长。而特斯拉之所以自研芯片，就是因为市面上算力强的芯片功耗高，功耗低的芯片，算力实在不成。

他透露：“若是我们使用传统的GPU, CPU或其他相似的产物，将花消数百瓦的功率，而且后备箱会被较量机，GPU伟大的冷却系统占有，由此一来成本奋发，占用车辆体积，并且高耗能。要知道能耗对于电动汽车的行驶里程很要害。”

在特斯拉上海工场建成后，特斯拉在上海超等工场也进行了很多人工智能的应用，提高生产效率。

马斯克透露，估计将来上海工场将有更多人工智能和加倍智能化的软件。

跟着人工智能手艺络续提高，机械需要更多工程师来斥地，将来也能缔造更多就业。

附：马斯克接管采访原文中文实录（未经本人核实）

主持人：Elon您好！固然今天您无法到上海现场，然则很愉快可以经由视频连线，再次与您活着界人工智能大会相会。

马斯克：感激邀请。再次列入大会太好了。我非常等候将来有机会能能够亲自来到现场。

Q：那就让我们直接切入正题吧，有几个问题想与您商量。首先，我们都知道，Autopilot 主动辅助驾驶是特斯拉纯电动车非常受迎接的一项功能。它在中国市场的应用情形若何？

A：特斯拉主动辅助驾驶在中国市场应用还不错。但因为我们与主动驾驶相关的工程斥地集中在美国，所以主动辅助驾驶功能在美国的应用的更好，在加州最好，这也首要是因为我们相关的工程师在加州。在我们确定这项功能在加利福尼亚运作精巧后，我们会将其推送到世界其他区域。今朝我们正在中国竖立相关的工程团队。若是你想成为特斯拉中国的工程师，我们会非常迎接，这将会非常好。

我想强调下，在中国我们要做的是进行好多原创性的工程斥地。所以并不是简洁的将美国的器材直接照搬到中国，而是就在中国进行原创的设计和原创的工程斥地。所以，若是您考虑工作，请考虑在特斯拉中国工作。

Q：您对于我们最终实现L5级别主动驾驶有多大决心？您感觉这一天什么时候会到来？

A：我对将来实现L5级别主动驾驶或是完全主动驾驶非常有决心，并且我认为很快就会实现。

在特斯拉，我感觉我们已经非常接近L5级主动驾驶了。我有决心，我们将在本年完成斥地L5级其余根基功能。对于L5级别主动驾驶，需要考虑相对于人类驾驶，实际道路可接管的平安级别是几多？达到人类驾驶平安性的两倍就充沛了吗？我不认为监管机构会承认L5级别主动驾驶达到与人类驾驶员一致的平安性是充沛的。

问题是，L5级别主动驾驶的平安性需要达到要求的两倍，三倍，五倍，照样十倍？是以，你能够将L5级别主动驾驶的平安性想像成9的序列。像需要99.99％平安性照样99.99999％？您想要几个9？可接管的水平是几多？然后，需要几多数据量才能使监管者确信该数据充沛平安？我认为，若是要问到有关主动驾驶L5级其余实际深入问题，这些是必然会被说起的。

我认为实现主动驾驶L5今朝不存在底层的基本性的挑战,然则有好多细节问题。我们面临的挑战就是要解决所有这些小问题，然后整合系统，持续解决这些长尾问题。你会发现你能够处理绝大多数场景的问题，然则又会不时显现一些新鲜不平常的场景，所以你必需有一个系统来找出并解决这些新鲜不平常场景的问题。这就是为什么你需要实际世界的场景。没有什么比实际世界更复杂了。我们建立的任何模拟都是实际世界复杂性的子集。

是以，我们今朝非常专注于处理L5级别主动驾驶的细节问题上。而且我相信这些问题完全可基于特斯拉车辆今朝搭载的硬件版正本解决，我们只需改善软件，就能够实现L5级别主动驾驶。

Q：您感觉人工智能和机械人手艺的三大支柱：感知、认知和行为，今朝在各自范畴的进展若何？

A：我不确定人工智能手艺是否能够如许分类。若是按照这个分类尺度，在感知层面，以识别物体为例，今朝的手艺取得了伟大进展。能够说，尽量是在专业范畴，目前的高级图像识别系统也比人类都要好。

问题的实质在于需要多强的较量能力，几多较量机和多长较量时间来练习感知能力？图像识别练习系统的效率若何？就图像识别或声音识别而言，对于给定的字撙节,人工智能系统可否正确识别处理？谜底是非常好。

认知或者是最微弱的范畴，人工智能是否能够懂得概念？是否会有效推理？可否缔造有意义的事物？今朝有好多非常有缔造力的手艺进步的人工智能，然则它们无法很好地掌握其缔造运动。至少如今在我们看来不太对，不外将来它会看起来像样些。

然后是行为。这个能够以游戏打譬喻。在任何划定明确的游戏中，或许自由施展空间对照有限的游戏，人工智能就像超人类一般。就今朝而言，很难想像有什么游戏，人工智能游戏玩家不克施展超人类水平的，这甚至都不去考虑到人工智能更快的回响时间。

Q：Autopilot主动辅助驾驶在哪些方面鞭策了AI算法和芯片的成长？它又若何改变了我们对AI手艺的懂得？

A：在为主动辅助驾驶斥地人工智能芯片时，我们发现市场上没有成本合理且低功耗的系统。若是我们使用传统的GPU, CPU或其他相似的产物，将花消数百瓦的功率，而且后备箱会被较量机，GPU伟大的冷却系统占有，由此一来成本奋发，占用车辆体积，并且高耗能。要知道能耗对于电动汽车的行驶里程很要害。

为此我们斥地了特斯拉自有的人工智能芯片，即具有双系统的特斯拉完全主动驾驶电脑，该芯片具有8位元和加快器，用于点积运算。在座列位或者有好多人都有所认识，人工智能包含好多点积运算，若是你知道什么是点积运算，那么便知道点积运算量伟大，这意味着我们的电脑必需做好多点积运算。我们事实上还未完全施展出特斯拉完全主动驾驶电脑的能力。实际上，几个月前我们才审慎地启动了芯片的第二套系统。充裕行使特斯拉完全主动驾驶电脑的能力，或者还需要至少一年摆布的时间。

我们还斥地了特斯拉Dojo练习系统，旨在可以快速处理大量视频数据，以改善对人工智能系统的练习。Dojo系统就像一个FP16练习系统，首要受芯片的发烧量和通信的速度的限制。所以我们也正在斥地新的总线和散热冷却系统，用于斥地更高效的较量机，从而能更有效处理视频数据。

我们是若何对待人工智能算法的成长呢？我不确定这是不是最好的懂得体式，神经收集首要是从实际中获取大量信息，好多来自无源光学方面，并建立矢量空间，素质大将大量光子压缩为矢量空间。我今天早上开车的时候还在想，人们是否可以进入大脑中的矢量空间呢？我们平日以类比的体式，将实际视为理所当然。但我认为，其实你能够进入本身大脑中的矢量空间，并认识你的大脑是若何处理所有外部信息的。事实上它在做的是记忆尽或者少的信息。

它获取并过滤大量信息，只保留相关的部门。那人们是若何在大脑中建立一个矢量空间呢？它的信息仅占原始数据很小一部门，却能够凭据这个矢量空间的表达做决议。这实际上就雷同一个大规模的压缩息争压缩的过程，有点像物理学，因为物理学公式素质上是对实际的压缩算法。

这就是物理学的感化。很显着，物理公式是实际的压缩算法。简言之，我们人类就是物理学感化的证据。若是你对宇宙做一个真正物理学意义上的模拟，就需要大量的较量。若是有足够时间，最终会发生觉知。人类就是最佳证实。若是你相信物理学和宇宙的演化史，便知道宇宙一起头是夸克电子，很长一段时间是氢元素，然后显现了氦和锂元素，接着显现了超新星。重元素在数十亿年后形成，个中一些重元素学会了表达。那就是我们人类，素质上由氢元素进化而来。若将氢元素放一段时间，它就会慢慢改变为我们。我感觉人人或者不太赞成这一点。所以有人会问，specialist的感化是什么？觉知的感化又是什么？整个宇宙是一种特别的觉知或许不存在特别性？又或许，在氢元素改变为人类的过程中何时发生了知觉?

Q：最后一个问题。祝贺特斯拉本年超卓的业绩，我们也想知道，特斯拉上海超等工场今朝的进展怎么样？在上海超等工场有没有一些制造业相关的AI应用？

A：感谢，特斯拉上海工场进展顺利，我为特斯拉团队感应无比高傲，他们做得很棒！我等候能尽快接见上海超等工场，他们超卓地工作的确让我深感欣慰。我不知道该若何表达，真的非常感激特斯拉中国团队。

估计将来我们的工场中会运用更多的人工智能和更智能化的软件。但我认为在工场，真正有效地使用人工智能还需要破费一些时间。你能够将工场看作一个复杂的鸠合体，掌握论鸠合体，个中涉及人也涉及机械。实际上所有公司都是如斯，但稀奇是制造业企业或许至少是制造业企业中，机械人掌握部门要更为复杂。所以有意思的是，跟着人工智能络续成长，或者将会缔造更多就业，甚至是否还需要工作也是纷歧定的。

主持人：再次感激您列入世界人工智能大会，也感激您的出色分享，我们等候着来岁的大会能在现场见到您！

马斯克：感谢您的线上采访。我进展来岁能有机会能亲自列入，我很喜欢到中国。中国老是给我惊喜，中国有好多既伶俐又用功的人，中国布满了正能量，中国人对将来满怀等候。我会让将来成为实际，所以我非常等候再次回来。

附：马斯克在大会上接管采访原文英文实录

主持人：Hello, Elon. Even though you cannot be in Shanghai right now, it's nice to have you at the 2020 world artificial intelligence conference over video.

马斯克：Thanks for having me. Yes, but it is great to be here again. I look forward to attending in person in the future.

Q：Great. Let's get started with a couple of questions. First, in terms of Tesla products, we know that Autopilot is one of its most popular features. How does it work in China?

A：Tesla Autopilot does work reasonably well in China. It does not work quite as well in China as it does in the US because still most of our engineering is in the US so that tends to be the local group of optimization. So Autopilot tends to work the best in California because that is where the engineers are. And then once it works in California, we then extend it to the rest of the world. But we are building up our engineering team in China. And so if you're interested in working at Tesla China as an engineer, we would love to have you work there. That will be great.

I really want to emphasize it is a lot that we are going to be doing original engineering in China. It's not just converting sort of stuff from America to work in China, we will be doing original design and engineering in China. So please do consider Tesla China, if you're thinking about working somewhere.

Q：Great. How confident are you that level five autonomy will eventually be with us? And when do you think we will reach full level five autonomy?

A：I'm extremely confident that level five or essentially complete autonomy will happen, and I think will happen very quickly.

I think at Tesla, I feel like we are very close to level five autonomy.I think I remain confident that we will have the basic functionality for level five autonomy complete this year. The thing to appreciate for level five autonomy is what level of safety is acceptable for the public streets relative to human safety? And then, so is it enough to be twice as safe as humans? Like I do not think that the regulators will accept equivalent safety to humans.

So the question is, will it be twice as safe as a requirement, three times as safe, five times as safe, 10 times as safe? So you can think of really level five autonomy as kind of like a march of 9s. Like do you have 99.99% safety? 99.99999%? How many 9s do you want? what is the acceptable level? And then what amount of data is required to convince regulators that it is sufficiently safe? Those are the actual in-depth questions, I think, to be asking about level five autonomy. That it will happen is a certainty.

So yes, I think there are no fundamental challenges remaining for level five autonomy. There are many small problems. And then there's the challenge of solving all those small problems and then putting the whole system together, and just keep addressing the long tail of problems. So you'll find that you're able to handle the vast majority of situations. But then there will be something very odd. And then you have to have the system figure out a train to deal with these very odd situations. This is why you need a kind of a real world situation. Nothing is more complex and weird than the real world. Any simulation we create is necessarily a subset of the complexity of the real world.

So we are really deeply enmeshed in dealing with the tiny details of level five autonomy. But I'm absolutely confident that this can be accomplished with the hardware that is in Tesla today, and simply by making software improvements, we can achieve level five autonomy.

Q：Great. If we look at the three building blocks of AI and robotics: perception, cognition, and action, how would you assess the progress respectively so far?

A：I am not sure I totally agree with dividing it into those categories:  perception, cognition, and action. But if you do use those categories, I’d say that probably perception we've made, if you can say like the recognition of objects, we've made incredible progress in recognition of objects. In fact, I think it would probably fair to say that advanced image recognition system today is better than almost any human, even in an expert field.

So it is really a question of how much compute power, how many computers were required to train it? How many compute hours? What was the efficiency of the image training system? But in terms of image recognition or sound recognition, and really any signal you can say, generally speaking any byte stream, Can an AI system recognize things accurately with a given byte stream？Extremely well.

Cognition. This is probably the weakest area. Do you understand concepts？Are you able to reason effectively? And can you be creative in a way that makes sense? You have so many advanced AIs that are very creative, but they do not curate their creative actions very well. We look at it and it is not quite right. It will become right though.

And then action, sort of like things like games, as maybe something part of the action part of thing. Obviously at this point, any game with rules, AI will be superhuman at any game with an understandable set of rules, essentially any game below a certain degree of freedom level. Let us say at this point, any game, it would be hard-pressed to think of a game where if there was enough attention paid to it, that we would not make it superhuman AI that could play it. That's not even taking into account the faster reaction time of AI.

Q：In what ways does Autopilot stimulate the development of AI algorithms and chips? And how do you does it refresh our understanding of AI technology?

A：In developing AI chips for Autopilot, what we found was that there was no system on the market that was capable of doing inference within a reasonable cost or power budget. So if we had gone with a conventional GPUs, CPUs and that kind of thing, we would have needed several hundred watts and we would have needed to fill up the trunk with computers and GPUs and a big cooling system. It would have been costly and bulky and have taken up too much power, which is important for range for an electric car.

So we developed our own AI chip, the Tesla Full Self-Driving computer with dual system on chips with the eight bit and accelerators for doing the dot products. I think probably a lot of people in this audience are aware of it. But AI consists of doing a great many dot products. This is like, if you know what a dot product is, it's just a lot of dot products, which effectively means that our brain must be doing a lot of dot products. We still actually haven't fully explored the power of the Tesla Full Self-Driving computer. In fact, we only turned on the second system on chip harshly a few months ago. So making full use of Tesla Full-Self Driving computer will probably take us at least another year or so.

Then we also have the Tesla Dojo system, which is a training system. And that's intended to be able to process fast amounts of video data to improve the training for the AI system. The Dojo system, that's like an fp16 training system and it is primarily constrained by heat and by communication between the chips. We are developing new buses and new sort of heat projection or cooling systems that enable a very high operation computer that will be able to process video data effectively.

How do we see the evolution of AI algorithms? I'm not sure how the best way to understand it, except that neural net seems to mostly do is to take a massive amount of information from reality, primarily passive optical, and create a vector space, essentially compress a massive amount of photons into a vector space. I am just thinking actually on the drive this morning, have you tried accessing the vector space in your mind? Like we normally take reality just granted in kind of analog way. But you can actually access the vector space in your mind and understand what your mind is doing to take in all the world data. What we actually doing is trying to remember the least amount of information possible.

So it's taking a massive amount of information, filtering it down, and saying what is relevant. And then how do you create a vector space world that is a very tiny percentage of that original data?  Based on that vector space representation, you make decisions. It is like a really compression and decompression that is just going on a massive scale, which is kind of how physics is like. You think of physics out physics algorithms as essentially compression algorithms for reality.

That is what physics does. Those physics formulas are compression algorithms for reality, which may sound very obvious. But if you simplify what it means, we are the proof points of this. If you simply ran a true physics simulation of the universe, it also takes a lot of compute. If you are given enough time, eventually you will have sentience. The proof of that is us. And if you believe in physics and the arches of the universe, it started out as sort of quarks electrons. And there was hydrogen for quite a while, and then helium and lithium. And then there were supernovas, the heavy elements formed billions of years later, some of those heavy elements learned to talk. We are essentially evolved hydrogen. If you just leave hydrogen out for a while, it turns into us. I think people don't quite appreciate this. So if you say, where does the specialist come in? Where does sentience come in? The whole universe is sentience special or nothing is? Or you could say at what point from hydrogen to us did it become sentient?

Q：Great. Our last question, congratulations on an incredible year so far at Tesla. How are things going at Gigafactory Shanghai? Is there any application of AI to manufacturing specifically at Giga Shanghai?

A：Thank you. Things are going really well at Giga Shanghai. I'm incredibly proud of the Tesla team. They're doing an amazing job. And I look forward to visiting Giga Shanghai as soon as possible. It's really an impressive work that's been done. I really can't say enough good things. Thank you to the Tesla China team.

We expect over time to use more AI and essentially smarter software in our factory. But I think it will take a while to really employ AI effectively in a factory situation. You can think of a factory as a complex, cybernetic collective involving humans and machines. This is actually how all companies are really, but especially manufacturing companies, or at least the robot component of manufacturing companies is much higher. So now that interesting thing about this is that I think over time there will be both more jobs and having jobs will be optional.

One of the false premises sometimes people have about economics is that there's a finite number of jobs. There is definitely not a finite number of jobs. An obvious, reductive example would be if you had the populations increased tenfold in a century, If there's a finite number of jobs and 90% of people would be unemployed? Or think of the transition from an agrarian to an industrial society where at an agrarian society, 90% people or more would be working in the farm. Now we have 2% or 3% of people working in the farm. So at least the short to medium term, my biggest concern about growth is being able to find enough humans. That is the biggest constraint in growth.

主持人：Thanks again you on for your time and joining us at this year's world artificial intelligence conference. We hope to see you next year in person.

马斯克：Thank you for having me in virtual form. I look forward to visiting physically next year, and I always enjoy visiting China. I am always amazed by how many smart, hardworking people that are in China and just that how much positive energy there is, and that people are really excited about the future. I want to make things happen.  I cannot wait to be back.