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】

原标题: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】

马斯克参添上海世界人造智能大会,屡次为中国打Call。

文 | James

车东西7月9日新闻,今天上午,2020年世界人造智能大会在上海正式开幕。受新冠肺热疫情影响,今年的世界人造智能大会的受邀嘉宾大片面都经由过程视频通话参添。马斯克行为受邀嘉宾之一,在今天上午经由过程线上视频的手段参添本届世界人造智能大会。

▲马斯克线上参添世界人造智能大会

马斯克在采访中泄露,在自动驾驶和电动汽车方面,特斯拉正在中国地区组建工程团队,这一团队将针对中国的道路进走自动驾驶研发,让自动驾驶一向提高。此外,马斯克宣布,特斯拉将在今年完善L5级自动驾驶基本功能的研发做事,并且特斯拉的L5级自动驾驶体系会更添坦然。

在人造智能方面,特斯拉的Autopilot自动驾驶芯片经由过程降矮芯片功耗,达成很高的识别实在度。由于特斯拉自动驾驶芯片HardWare 3.0性能专门强劲,现在也只发挥了其片面运算能力。要足够行使HardWare 3.0的性能,恐怕还必要一年旁边的时间。

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另外,特斯拉上海工厂建成后,业行使更众人造智能柔件优化车辆生产流程,今后还会创造更众就业。

马斯克:特斯拉今年搞定L5级自动驾驶基本功能

7月9日上午,2020年世界人造智能大会在上海开幕,马斯克行为受邀嘉宾线上出席这次大会,并批准采访。

▲世界人造智能大会现场

在采访中,马斯克在自动驾驶、人造智能技术两个方面阐述了特斯拉的最新挺进。他外示,特斯拉将在今年基本实现L5级自动驾驶技术基本功能的研发做事。

针对自动驾驶技术,马斯克外示,现在特斯拉自动辅助驾驶在中国市场行使还不错。

不过,由于特斯拉自动驾驶的工程开发荟萃在美国添州,因而自动辅助驾驶功能在美国的行使的更益,在添州最益。

因此,为适宜各国各地区差别的交通状况,现在特斯拉正在中国竖立自动驾驶工程团队。在中国,还要进走很众原创性的工程开发,并且特斯拉现在已经最先雇用特出的研发工程师。

对于高级自动驾驶技术,马斯克外示,他对L5级自动驾驶技术专门有信念,将在今年完善开发L5级自动驾驶体系的基本功能。

他外示,L5级自动驾驶体系最难得的地方在于坦然级别必要更高,倘若仅达到人类驾驶的坦然程度远远不足。

针对AI芯片的发展,马斯克外示,Autopilot自动辅助驾驶芯片推动了AI芯片的发展。而特斯拉之因而自研芯片,就是由于市面上算力强的芯片功耗高,功耗矮的芯片,算力实在不走。

他外示:“ 倘若吾们行使传统的GPU, CPU 或其他相通的产品,将消耗数百瓦的功率,并且后备箱会被计算机,GPU 重大的冷却体系占有,由此一来成本振奋,占用车辆体积,而且高耗能。要清新能耗对于电动汽车的走驶里程很关键。”

在特斯拉上海工厂建成后,特斯拉在上海超级工厂也进走了很众人造智能的行使,挑高生产效果。

马斯克外示,展望异日上海工厂将有更众人造智能和更添智能化的柔件。

随着人造智能技术一向提高,机器必要更众工程师来开发,异日也能创造更众就业。

附:马斯克批准采访原文中文实录(未经本人核实)

主办人:Elon您益!固然今天您无法到上海现场,但是很起劲能够经由过程视频连线,再次与您活着界人造智能大会相会。

马斯克:感谢邀请。再次参添大会太益了。吾专门憧憬异日有机会能能够亲自来到现场。

Q:那就让吾们直接切入正题吧,有几个题目想与您探讨。最先,吾们都清新,Autopilot 自动辅助驾驶是特斯拉纯电动车专门受迎接的一项功能。它在中国市场的行使情况如何?

A:特斯拉自动辅助驾驶在中国市场行使还不错。但由于吾们与自动驾驶相关的工程开发荟萃在美国,因而自动辅助驾驶功能在美国的行使的更益,在添州最益,这也主要是由于吾们相关的工程师在添州。在吾们确定这项功能在添利福尼亚运作卓异后,吾们会将其推送到世界其他地区。现在吾们正在中国竖立相关的工程团队。倘若你想成为特斯拉中国的工程师,吾们会专门迎接,这将会专门益。

吾想强调下,在中国吾们要做的是进走很众原创性的工程开发。因而并不是浅易的将美国的东西直接照搬到中国,而是就在中国进走原创的设计和原创的工程开发。因而,倘若您考虑做事,请考虑在特斯拉中国做事。

Q:您对于吾们最后实现L5级别自动驾驶有众大信念?您觉得这镇日什么时候会到来?

A:吾对异日实现L5级别自动驾驶或是十足自动驾驶专门有信念,而且吾认为很快就会实现。

在特斯拉,吾觉得吾们已经专门挨近L5级自动驾驶了。吾有信念,吾们将在今年完善开发L5级别的基本功能。对于L5级别自动驾驶,必要考虑相对于人类驾驶,实际道路可批准的坦然级别是众少?达到人类驾驶坦然性的两倍就有余了吗?吾不认为监管机构会认可L5级别自动驾驶达到与人类驾驶员一致的坦然性是有余的。

题目是,L5级别自动驾驶的坦然性必要达到请求的两倍,三倍,五倍,照样十倍?因此,你能够将L5级别自动驾驶的坦然性想像成9的序列。像必要99.99%坦然性照样99.99999%?您想要几个9?可批准的程度是众少?然后,必要众幼批据量才能使监管者确信该数占有余坦然?吾认为,倘若要问到相关自动驾驶L5级别的实际深入题目,这些是肯定会被挑及的。

吾认为实现自动驾驶L5现在不存在底层的根本性的挑衅,但是有很众细节题目。吾们面临的挑衅就是要解决一切这些幼题目,然后整相符体系,赓续解决这些长尾题目。你会发现你能够处理绝大无数场景的题目,但是又会往往展现一些稀奇不清淡的场景,因而你必须有一个体系来找出并解决这些稀奇不清淡场景的题目。这就是为什么你必要实际世界的场景。异国什么比实际世界更复杂了。吾们创建的任何模拟都是实际世界复杂性的子集。

因此,吾们现在专门凝神于处理L5级别自动驾驶的细节题目上。并且吾自夸这些题目十足可基于特斯拉车辆现在搭载的硬件版原本解决,吾们只需改进柔件,就能够实现L5级别自动驾驶。

Q:您觉得人造智能和机器人技术的三大支撑:感知、认知和走为,现在在各自周围的挺进如何?

A:吾不确定人造智能技术是否能够如许分类。倘若根据这个分类标准,在感知层面,以识别物体为例,现在的技术取得了重大挺进。能够说,即便是在专科周围,当今的高级图像识别体系也比人类都要益。

题目的内心在于必要众强的计算能力,众少计算机和众长计算时间来训练感知能力?图像识别训练体系的效果如何?就图像识别或声音识别而言,对于给定的字节流,人造智能体系能否实在识别处理?答案是专门益。

认知能够是最单薄的周围,人造智能是否能够理解概念?是否会有效推理?能否创造有意义的事物?现在有很众专门有创造力的技术先辈的人造智能,但是它们无法很益地控制其创造运动。起码现在在吾们望来不太对,不过异日它会望首来像样些。

然后是走为。这个能够以游玩打比方。在任何规则清晰的游玩中,或者解放发挥空间比较有限的游玩,人造智能就像超人类相通。就现在而言,很难想像有什么游玩,人造智能游玩玩家不克发挥超人类程度的,这甚至都不往考虑到人造智能更快的逆答时间。

Q:Autopilot自动辅助驾驶在哪些方面推动了AI算法和芯片的发展?它又如何转折了吾们对AI技术的理解?

A:在为自动辅助驾驶开发人造智能芯片时,吾们发现市场上异国成原形符理且矮功耗的体系。倘若吾们行使传统的GPU, CPU或其他相通的产品,将消耗数百瓦的功率,并且后备箱会被计算机,GPU重大的冷却体系占有,由此一来成本振奋,占用车辆体积,而且高耗能。要清新能耗对于电动汽车的走驶里程很关键。

为此吾们开发了特斯拉自有的人造智能芯片,即具有双体系的特斯拉十足自动驾驶电脑,该芯片具有8位元和添速器,用于点积运算。在座各位能够有很众人都有所晓畅,人造智能包含很众点积运算,倘若你清新什么是点积运算,那么便清新点积运算量重大,这意味着吾们的电脑必须做很众点积运算。吾们原形上还未十足发挥出特斯拉十足自动驾驶电脑的能力。实际上,几个月前吾们才郑重地启动了芯片的第二套体系。足够行使特斯拉十足自动驾驶电脑的能力,能够还必要起码一年旁边的时间。

吾们还开发了特斯拉Dojo训练体系,旨在能够迅速处理大量视频数据,以改善对人造智能体系的训练。Dojo体系就像一个FP16训练体系,主要受芯片的发热量和通讯的速率的限定。因而吾们也正在开发新的总线和散热冷却体系,用于开发更高效的计算机,从而能更有效处理视频数据。

吾们是如何望待人造智能算法的发展呢?吾不确定这是不是最益的理解手段,神经网络主要是从实际中获取大量新闻,产品展示很众来自无源光学方面,并创建矢量空间,内心上将大量光子压缩为矢量空间。吾今天早晨开车的时候还在想,人们是否能够进入大脑中的矢量空间呢?吾们清淡以类比的手段,将实际视为理所自然。但吾认为,其实你能够进入本身大脑中的矢量空间,并晓畅你的大脑是如那里理一切外部新闻的。原形上它在做的是记忆尽能够少的新闻。

它获取并过滤大量新闻,只保留相关的片面。那人们是如何在大脑中创建一个矢量空间呢?它的新闻仅占原首数据很幼一片面,却能够根据这个矢量空间的外达做决策。这实际上就雷联相符个大周围的压缩休争压缩的过程,有点像物理学,由于物理学公式内心上是对实际的压缩算法。

这便是物理学的作用。很清晰,物理公式是实际的压缩算法。简言之,吾们人类就是物理学作用的证据。倘若你对宇宙做一个真实物理学意义上的模拟,就必要大量的计算。倘若有优裕时间,最后会产生觉知。人类便是最佳表明。倘若你自夸物理学和宇宙的演化史,便清新宇宙一路先是夸克电子,很长一段时间是氢元素,然后展现了氦和锂元素,接着展现了超新星。重元素在数十亿年后形成,其中一些重元素学会了外达。那就是吾们人类,内心上由氢元素进化而来。若将氢元素放一段时间,它就会徐徐变化为吾们。吾觉得行家能够不太赞许这一点。因而有人会问,specialist的作用是什么?觉知的作用又是什么?整个宇宙是一栽稀奇的觉知或者不存在稀奇性?又或者,在氢元素变化为人类的过程中何时产生了知觉?

Q:末了一个题目。祝贺特斯拉今年特出的业绩,吾们也想清新,特斯拉上海超级工厂现在的挺进怎么样?在上海超级工厂有异国一些制造业相关的AI行使?

A:谢谢,特斯拉上海工厂挺进顺手,吾为特斯拉团队感到无比自夸,他们做得很棒!吾憧憬能尽快访问上海超级工厂,他们特出地做事实在让吾深感安慰。吾不清新该如何外达,真的专门感谢特斯拉中国团队。

展望异日吾们的工厂中会行使更众的人造智能和更智能化的柔件。但吾认为在工厂,真实有效地行使人造智能还必要消耗一些时间。你能够将工厂望作一个复杂的荟萃体,控制论荟萃体,其中涉及人也涉及机器。实际上一切公司都是如此,但稀奇是制造业企业或者起码是制造业企业中,机器人控制片面要更为复杂。因而有有趣的是,随着人造智能一向发展,能够将会创造更众就业,甚至是否还必要做事也是纷歧定的。

主办人:再次感谢您参添世界人造智能大会,也感谢您的精彩分享,吾们憧憬着明年的大会能在现场见到您!

马斯克:谢谢您的线上采访。吾期待明年能有机会能亲自参添,吾很爱到中国。中国总是给吾惊喜,中国有很众既智慧又辛勤的人,中国足够了正能量,中国人对异日满怀憧憬。吾会让异日成为实际,因而吾专门憧憬再次回来。

附:马斯克在大会上批准采访原文英文实录

主办人: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.

主办人: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.