車東西（公眾號：chedongxi）
文 | James
車東西7月9日消息，今天上午，2020年世界人工智能大會在上海正式開幕。受新冠肺炎疫情影響，今年的世界人工智能大會的受邀嘉賓大部分都通過視頻通話參加。馬斯克作為受邀嘉賓之一，在今天上午通過線上視頻的方式參加本屆世界人工智能大會。
▲馬斯克線上參加世界人工智能大會
馬斯克在採訪中透露，在自動駕駛和電動汽車方面，特斯拉正在中國地區組建工程團隊，這一團隊將針對中國的道路進行自動駕駛研發，讓自動駕駛不斷進步。此外，馬斯克宣佈，特斯拉將在今年完成L5級自動駕駛基本功能的研發工作，並且特斯拉的L5級自動駕駛系統會更加安全。
在人工智能方面，特斯拉的Autopilot自動駕駛芯片通過降低芯片功耗，達成很高的識別準確度。由於特斯拉自動駕駛芯片HardWare 3.0性能非常強勁，目前也只發揮了其部分運算能力。要充分使用HardWare 3.0的性能，恐怕還需要一年左右的時間。
另外，特斯拉上海工廠建成後，也運用更多人工智能軟件優化車輛生產流程，今後還會創造更多就業。
一、馬斯克線上參加人工智能大會 特斯拉已非常接近L5級自動駕駛
7月9日上午，2020年世界人工智能大會在上海開幕，馬斯克作為受邀嘉賓線上出席這次大會，並接受採訪。
▲世界人工智能大會現場
在採訪中，馬斯克在自動駕駛、人工智能技術兩個方面闡述了特斯拉的最新進展。他表示，特斯拉將在今年基本實現L5級自動駕駛技術基本功能的研發工作。
針對自動駕駛技術，馬斯克表示，目前特斯拉自動輔助駕駛在中國市場應用還不錯。
不過，由於特斯拉自動駕駛的工程開發集中在美國加州，所以自動輔助駕駛功能在美國的應用的更好，在加州最好。
因此，為適應各國各地區不同的交通狀況，目前特斯拉正在中國建立自動駕駛工程團隊。在中國，還要進行許多原創性的工程開發，並且特斯拉現在已經開始招聘優秀的研發工程師。
對於高級自動駕駛技術，馬斯克表示，他對L5級自動駕駛技術非常有信心，將在今年完成開發L5級自動駕駛系統的基本功能。
他表示，L5級自動駕駛系統最困難的地方在於安全級別需要更高，如果僅達到人類駕駛的安全水平遠遠不夠。
針對AI芯片的發展，馬斯克表示，Autopilot自動輔助駕駛芯片推動了AI芯片的發展。而特斯拉之所以自研芯片，就是因為市面上算力強的芯片功耗高，功耗低的芯片，算力實在不行。
他表示：“如果我們使用傳統的GPU, CPU或其他相似的產品，將耗費數百瓦的功率，並且後備箱會被計算機，GPU巨大的冷卻系統佔據，由此一來成本高昂，佔用車輛體積，而且高耗能。要知道能耗對於電動汽車的行駛里程很關鍵。”
在特斯拉上海工廠建成後，特斯拉在上海超級工廠也進行了許多人工智能的應用，提高生產效率。
馬斯克表示，預計未來上海工廠將有更多人工智能和更加智能化的軟件。
隨著人工智能技術不斷進步，機器需要更多工程師來開發，未來也能創造更多就業。
二、附：馬斯克在大會上接受採訪原文中文實錄（未經本人核實）
主持人：Elon您好！雖然今天您無法到上海現場，但是很高興能夠通過視頻連線，再次與您在世界人工智能大會相會。
馬斯克：感謝邀請。再次參加大會太好了。我非常期待未來有機會能可以親自來到現場。
Q：那就讓我們直接切入正題吧，有幾個問題想與您探討。首先，我們都知道，Autopilot 自動輔助駕駛是特斯拉純電動車非常受歡迎的一項功能。它在中國市場的應用情況如何？
A：特斯拉自動輔助駕駛在中國市場應用還不錯。但因為我們與自動駕駛相關的工程開發集中在美國，所以自動輔助駕駛功能在美國的應用的更好，在加州最好，這也主要是因為我們相關的工程師在加州。在我們確定這項功能在加利福尼亞運作良好後，我們會將其推送到世界其他地區。目前我們正在中國建立相關的工程團隊。如果你想成為特斯拉中國的工程師，我們會非常歡迎，這將會非常好。
我想強調下，在中國我們要做的是進行很多原創性的工程開發。所以並不是簡單的將美國的東西直接照搬到中國，而是就在中國進行原創的設計和原創的工程開發。所以，如果您考慮工作，請考慮在特斯拉中國工作。
Q：您對於我們最終實現L5級別自動駕駛有多大信心？您覺得這一天什麼時候會到來？
A：我對未來實現L5級別自動駕駛或是完全自動駕駛非常有信心，而且我認為很快就會實現。
在特斯拉，我覺得我們已經非常接近L5級自動駕駛了。我有信心，我們將在今年完成開發L5級別的基本功能。對於L5級別自動駕駛，需要考慮相對於人類駕駛，實際道路可接受的安全級別是多少？達到人類駕駛安全性的兩倍就足夠了嗎？我不認為監管機構會認可L5級別自動駕駛達到與人類駕駛員同等的安全性是足夠的。
問題是，L5級別自動駕駛的安全性需要達到要求的兩倍，三倍，五倍，還是十倍？因此，你可以將L5級別自動駕駛的安全性想像成9的序列。像需要99.99％安全性還是99.99999％？您想要幾個9？可接受的水平是多少？然後，需要多少數據量才能使監管者確信該數據足夠安全？我認為，如果要問到有關自動駕駛L5級別的實際深入問題，這些是一定會被提及的。
我認為實現自動駕駛L5目前不存在底層的根本性的挑戰,但是有很多細節問題。我們面臨的挑戰就是要解決所有這些小問題，然後整合系統，持續解決這些長尾問題。你會發現你可以處理絕大多數場景的問題，但是又會不時出現一些奇怪不尋常的場景，所以你必須有一個系統來找出並解決這些奇怪不尋常場景的問題。這就是為什麼你需要現實世界的場景。沒有什麼比現實世界更復雜了。我們創建的任何模擬都是現實世界複雜性的子集。
因此，我們目前非常專注於處理L5級別自動駕駛的細節問題上。並且我相信這些問題完全可基於特斯拉車輛目前搭載的硬件版本來解決，我們只需改進軟件，就可以實現L5級別自動駕駛。
Q：您覺得人工智能和機器人技術的三大支柱：感知、認知和行為，目前在各自領域的進展如何？
A：我不確定人工智能技術是否可以這樣分類。如果按照這個分類標準，在感知層面，以識別物體為例，目前的技術取得了巨大進展。 可以說，即便是在專業領域，當今的高級圖像識別系統也比人類都要好。
問題的實質在於需要多強的計算能力，多少計算機和多長計算時間來訓練感知能力？圖像識別訓練系統的效率如何？就圖像識別或聲音識別而言，對於給定的字節流,人工智能系統能否準確識別處理？ 答案是非常好。
認知可能是最薄弱的領域，人工智能是否可以理解概念？是否會有效推理？能否創造有意義的事物？目前有很多非常有創造力的技術先進的人工智能，但是它們無法很好地控制其創造活動。至少現在在我們看來不太對，不過未來它會看起來像樣些。
然後是行為。這個可以以遊戲打比方。在任何規則明確的遊戲中，或者自由發揮空間比較有限的遊戲，人工智能就像超人類一樣。就目前而言，很難想像有什麼遊戲，人工智能遊戲玩家不能發揮超人類水平的，這甚至都不去考慮到人工智能更快的反應時間。
Q：Autopilot自動輔助駕駛在哪些方面推動了AI算法和芯片的發展？它又如何改變了我們對AI技術的理解？
A：在為自動輔助駕駛開發人工智能芯片時，我們發現市場上沒有成本合理且低功耗的系統。如果我們使用傳統的GPU, CPU或其他相似的產品，將耗費數百瓦的功率，並且後備箱會被計算機，GPU巨大的冷卻系統佔據，由此一來成本高昂，佔用車輛體積，而且高耗能。要知道能耗對於電動汽車的行駛里程很關鍵。
為此我們開發了特斯拉自有的人工智能芯片，即具有雙系統的特斯拉完全自動駕駛電腦，該芯片具有8位元和加速器，用於點積運算。在座各位可能有很多人都有所瞭解，人工智能包含很多點積運算，如果你知道什麼是點積運算，那麼便知道點積運算量巨大，這意味著我們的電腦必須做很多點積運算。我們事實上還未完全發揮出特斯拉完全自動駕駛電腦的能力。實際上，幾個月前我們才審慎地啟動了芯片的第二套系統。充分利用特斯拉完全自動駕駛電腦的能力，可能還需要至少一年左右的時間。
我們還開發了特斯拉Dojo訓練系統，旨在能夠快速處理大量視頻數據，以改善對人工智能系統的訓練。Dojo系統就像一個FP16訓練系統，主要受芯片的發熱量和通訊的速率的限制。所以我們也正在開發新的總線和散熱冷卻系統，用於開發更高效的計算機，從而能更有效處理視頻數據。
我們是如何看待人工智能算法的發展呢？我不確定這是不是最好的理解方式，神經網絡主要是從現實中獲取大量信息，很多來自無源光學方面，並創建矢量空間，本質上將大量光子壓縮為矢量空間。我今天早上開車的時候還在想，人們是否能夠進入大腦中的矢量空間呢？我們通常以類比的方式，將現實視為理所當然。但我認為，其實你可以進入自己大腦中的矢量空間，並瞭解你的大腦是如何處理所有外部信息的。事實上它在做的是記憶儘可能少的信息。
它獲取並過濾大量信息，只保留相關的部分。那人們是如何在大腦中創建一個矢量空間呢？它的信息僅佔原始數據很小一部分，卻可以根據這個矢量空間的表達做決策。這實際上就類似一個大規模的壓縮和解壓縮的過程，有點像物理學，因為物理學公式本質上是對現實的壓縮算法。
這便是物理學的作用。很明顯，物理公式是現實的壓縮算法。簡言之，我們人類就是物理學作用的證據。如果你對宇宙做一個真正物理學意義上的模擬，就需要大量的計算。如果有充足時間，最終會產生覺知。人類便是最佳證明。如果你相信物理學和宇宙的演化史，便知道宇宙一開始是夸克電子，很長一段時間是氫元素，然後出現了氦和鋰元素，接著出現了超新星。重元素在數十億年後形成，其中一些重元素學會了表達。那就是我們人類，本質上由氫元素進化而來。若將氫元素放一段時間，它就會慢慢轉變為我們。我覺得大家可能不太贊成這一點。所以有人會問，specialist的作用是什麼？覺知的作用又是什麼？整個宇宙是一種特殊的覺知或者不存在特殊性？又或者，在氫元素轉變為人類的過程中何時產生了知覺?
Q：最後一個問題。祝賀特斯拉今年出色的業績，我們也想知道，特斯拉上海超級工廠目前的進展怎麼樣？在上海超級工廠有沒有一些製造業相關的AI應用？
A：謝謝，特斯拉上海工廠進展順利，我為特斯拉團隊感到無比自豪，他們做得很棒！ 我期待能儘快訪問上海超級工廠，他們出色地工作確實讓我深感欣慰。我不知道該如何表達，真的非常感謝特斯拉中國團隊。
預計未來我們的工廠中會運用更多的人工智能和更智能化的軟件。但我認為在工廠，真正有效地使用人工智能還需要花費一些時間。你可以將工廠看作一個複雜的集合體，控制論集合體，其中涉及人也涉及機器。實際上所有公司都是如此，但特別是製造業企業或者至少是製造業企業中，機器人控制部分要更為複雜。 所以有意思的是，隨著人工智能不斷髮展，可能將會創造更多就業，甚至是否還需要工作也是不一定的。
主持人：再次感謝您參加世界人工智能大會，也感謝您的精彩分享，我們期待著明年的大會能在現場見到您！
馬斯克：謝謝您的線上採訪。 我希望明年能有機會能親自參加，我很喜歡到中國。 中國總是給我驚喜，中國有很多既聰明又勤奮的人，中國充滿了正能量，中國人對未來滿懷期待。我會讓未來成為現實，所以我非常期待再次回來。
三、附：馬斯克在大會上接受採訪原文英文實錄
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.
馬斯克：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.
馬斯克：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.
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