導語：Eye on A.I.是由紐約時報資深記者 Craig S. Smith 主持的一檔雙週部落格節目。每一期節目，Craig 都將與這一領域有影響力的人物進行交流，推進廣義環境中的機器智慧新發展，思考技術發展新蘊意。
機器之心為此係列對話的中文合作方。以下為此係列內容的第三篇，Craig Smith 與 Yandex AI負責人Misha Bilenko展開的對話。
Hi, this is Craig Smith with a new podcast about artificial intelligence. I’m a former New York Times correspondent now focused on AI and I’ll be talking to people who are making a difference in the space. I was recently in Stockholm at this year’s ICML, the International Conference on Machine Learning, and had a few conversations with Misha Bilenko, the head of AI at Yandex, which is often described as the Google of Russia. I found much of what Misha had to say enlightening and hope you do, too.
大家好，我是 Craig Smith，這是一個有關人工智慧的新播客。我之前是《紐約時報》的記者，現在專注於 AI，我將與正在該領域做貢獻的人對話。我目前正在斯德哥爾摩參加今年的國際機器學習會議 ICML，並與 Yandex的 AI負責人 Misha Bilenko進行了對話。Yandex可以說是俄羅斯版的谷歌。我認為 Misha說的很多內容都很有啟發性，希望你也這樣認為。
We started talking about the role of state actors in artificial intelligence research and the many national AI strategies that have been announced. Vladimir Putin famously said last year that “whoever becomes the leader in AI will rule the world.” I asked Misha whether he saw AI research in national terms and whether there is a risk of an AI “arms race.”
我們首先談到了人工智慧研究中的國家行為者的角色。目前已有很多國家級的 AI戰略出臺。弗拉基米爾·普京去年有個著名的言論：“誰成為 AI領域的領導者，誰就將掌控世界。”我問 Misha他是否看到了國家層面的 AI研究，他認為是否存在 AI“軍備競賽”的風險。
MISHA: I think in the research field, people don't think in terms of national strategies given how communal and international the field is. If you look at the make-up of researchers in terms of current affiliations, and then affiliations of their educational paths and their origins, everyone has moved around so much and the things they care about are typically in the scientific field rather than in some sort of policy field that I think it's easy to think of anecdotes or think of, basically, come up with stories about national strategies but that exists in a separate realm from the technical field where what people care about is algorithms, where people generally are very eager to collaborate and they have friends and colleagues from all over the world and they, you know, they've worked with people from all over the world in the past and now they're working with people from all over the world in the present and in the process they're moving all around the world.
Misha：考慮到這個領域的社群性和國際性，我認為在研究領域人們不會從國家戰略的角度來思考。如果你看看當前研究的作者所屬機構的研究者組成，然後再瞭解一下他們的教育路徑和來源，就能看到每個人都在到處走動，而且他們所關心的通常都是科學領域，而非某個政策領域。我認為人們很容易想到那些有關國家政策的傳言軼事或者編造一些故事，但技術領域則不同，人們關心的是演算法，這個領域的人通常非常渴望合作，而且他們有來自世界各地的朋友和同事，而且他們過去和現在也一直在與來自世界各地的人合作。他們不僅在與來自世界各地的人合作，而且在此過程中還會在世界各地移動。
So, it is a very cosmopolitan community and that's where this - any sort of rhetoric that exists in the political sphere of national strategies is really, just does not, does not jive and does not belong on the ground here because it's just not the way people think and that's not the way they operate. Everybody thinks of it in terms of the, you know, the technical problems we have and the challenges and the algorithms and the progress going forward, and then the general kind of assumption is that everybody is here to push science and technology further.
所以，這是一個非常世界性的社群。在這個領域，任何存在於國家戰略的政治領域中的言辭都不具有說服力，與這裡的基調不符，因為這並不是這個領域的人的思考方式，也不是該領域的運作方式。每個人想的都是我們具有的技術問題、挑戰和演算法以及進展情況，可以認為這個領域的每個人都是為了推動科學和技術的進一步發展。
CRAIG: And there is no Russian, quote unquote, AI, or American, quote unquote, AI. I mean everything is being published in open forums - everything significant - other than perhaps military applications, but very little of that is basic research.
Craig：而且並不存在“俄羅斯”或“美國”的 AI。我的意思是每項重要的研究成果都會公開發布——也許軍事應用除外，但軍事應用很少有基礎研究。
MISHA: I know nothing about military applications - I never worked on military applications - but the venues themselves are international so ICML moves from continent to continent every year by design. Everything that's interesting gets published in the core conferences – ICML, NIPS, ICLR - it goes into arxiv, which is just an Internet archive, literally, of scientific work. And because of that there is no, kind of, like I said, people don't even think of national security and policy in the same realm as the technical work. It just exists in a whole separate sphere of technical achievement and algorithmic excellence in science.
Misha：我對軍事應用一無所知——我從沒開發過軍事應用，但這個領域本身是國際性的，所以 ICML有意設計成每年都從一個大陸轉到另一個大陸舉辦。每一項有趣的研究都能在這些核心會議上得到發表——ICML、NIPS、ICLR；論文還會在 arXiv上釋出，這就像是一個網際網路科學研究檔案館。因此，就像我說的，和做技術領域的人不同，這個領域搞科研的人基本都不思考國家安全和政策。技術成就和科學的演算法發展是完全不同的區域。
CRAIG: The significance then of these national strategies that are being published are simply to build up a national cohort of competent researchers and engineers so that each country that has a national strategy feels that it's got a stake in the game.
Craig：正在出臺的這些國家戰略的意義只是構建一個由出色的研究者和工程師組成的國家隊，這樣每個有國家戰略的國家都會覺得自己在這場遊戲中佔據了一席之地。
MISHA: The way I think of it is, if you look at the pipeline of science, it starts with education and basically you need to find certain professors and labs and directions to make sure that you have, you know, experts being educated in those fields and then those experts can go on and become academics or found companies and then create businesses and create technologies.
Misha：我認為是這樣，看看科學的流程，首先是從教育開始的，基本上你需要找到特定的教授和實驗室以及研究方向，以確保你有接受過這個領域教育的專家，然後這些專家會繼續進行學術研究或創立公司，然後再創造商業應用和技術。
And so, in that sense I view it as an economic question - is that if there is so much economy that is being transformed by AI the implication of that is that for every economy to progress it is essential to create experts in the fields that are the fields of the future. And so that's where it's a vital economic imperative for every, you know, technically advanced country to have academic pipelines that prepare people and so as a result, since economic pipelines are typically funded by governments, therefore governments must fund and must adjust their spending priorities to, to fund those fields. And that's where I don't view it through the lens of security at all, I view it through the lens of technology and, and economics first and foremost.
因此，在這個意義上，我將其看作是一個經濟問題——如果有那麼多經濟體正被 AI改變，那麼這意味著每個經濟體要想進步，創造這些未來領域的專家是至關重要的。因此，對於每個技術先進的國家，從經濟角度上看必須要做的事情是擁有創造人才的學術流程。因為經濟流程通常是由政府資助的，因為政府必須資助或調整它們的開支優先順序，以資助這些領域。因此，我不會從國家安全形度看待這個問題，而會通過技術角度，尤其最重要的是經濟角度。
CRAIG: Yeah. And we were talking before about how many competent scientists there are in the field globally right now and it's anywhere from 20 or 30 thousand to 200,000 but it still remains a relatively small number.
Craig：我們之前談到了目前全球這一領域內稱職的科學家的數量，估計是在 2萬或三萬到 20萬之間，但相對來說仍然很少。
MISHA: It really depends on where you draw the line between scientists and engineers and data scientists - because they straddle both worlds, they are as much analyst and scientist as they are engineers. Hundreds of thousands sounds right. But again, it very much depends on where you draw the bar in terms of what makes one.
Misha：這實際上取決於你劃在的科學家和工程師和資料科學家之間的界線在哪——資料科學家橫跨兩個世界，他們既是分析師和科學家，也是工程師。數十萬聽起來很合理。但重申一下，這非常取決於你設定的區分他們的位置。
CRAIG: Do you have any, any sense of where Russia stands in that vis a vis China and the United States, in terms of numbers of…
Craig：你能估計俄羅斯與中國和美國的人才數量多寡嗎？
MISHA: Oh, it's definitely high. I mean if you look, I mean Russia is just like China. And this is where the distinction by states is not as relevant anymore because if you look at names, from which you could infer ethnic origin, you'll see of course lots of Russian names and lots of Chinese names. But, at the same time, if you look at their affiliations people are all over the place and likewise they will see some non-Russian or non-Chinese names being affiliated with Russian or Chinese institutions. So that's where it's, there's definitely a high presence, I mean of, of Chinese scientists or Russian scientists. But there is definitely a global ecosystem of everybody moving around and kind of, and really collaborating, so that's where it's not as much of a national issue.
Misha：哦，俄羅斯肯定很多。我覺得俄羅斯就像是中國。這也是說明國家之間的區別不再重要的原因，因為你可以看看研究者的名字，從中你可以推斷作者的族裔，你能看到很多俄羅斯人和中國人的名字。同樣，如果你看看他們的所屬機構，可以看到它們位於世界各地；你也能看到俄羅斯和中國的機構下有一些非俄羅斯或非中國的名字。所以我認為中國科學家或俄羅斯科學家的存在感肯定很強。但這是一個全球性的體系，每個人都在到處移動以及合作，所以這很大程度上不是國家的問題。
CRAIG: So, the public perception of there being a sort of arms race in AI is mistaken or is a misnomer.
Craig：所以，公眾認知中存在某種 AI軍備競賽的看法是錯誤的或用詞不當。
MISHA: I think it's a misnomer. I don't think it's the right... I mean it's a very kind of competitive and very antagonistic view of the world. I think now - if one sort of tends to view the world this way they certainly can, but, but it's - I think it's not the way anybody in this field views it or most people in the field wouldn't. Most people in the field view it much more in terms of areas you work on and then in terms of collaboration - everybody collaborates with everybody so you know there's not – ‘arms race’ implies, you know, hard competition and, you know, hard distinctions and that's not the case where it's a, it's a very much a collaborative enterprise for everybody.
Misha：我認為是用詞不當。我並不認為這是正確的……我認為這是一種非常具有競爭意識和非常對抗性的世界觀。我現在在想，如果存在某種以這種方式看待世界的趨勢——肯定有人這麼看，但我認為這不是這個領域內的任何人或大部分人看待的方式。這個領域內的大部分人更多是關注自己研究的領域，然後從合作的角度看問題——每個人都與每個人合作，這不是“軍備競賽”的意思。軍備競賽意味著激烈的競爭以及顯著的區分，但目前不存在這種情況，這仍然是一個非常合作性的領域。
CRAIG: There's a lot of talk about AI safety as the systems become more generalized and also as the militaries start looking at military applications. Are you optimistic about there being international conventions that different countries will adhere to?
Craig：隨著系統變得越來越通用化，以及軍方也開始研發相關軍事應用，有關 AI安全的話題也正得到越來越多的討論。你認為是否會出現不同國家都會遵守的國際性公約？
MISHA: I'm not an expert on this but I am generally optimistic - certainly as a person - so I certainly view it as with any other new technology. It will have applications in military as in other areas. And then it's up to basically both the community and the ruling bodies and of all flavors state and nonstate to come up with regulations and policies that are required to basically prevent gross misuses of the technology. So, I think it will certainly emerge and there's certainly awareness of potential harms of technology. But just like with everything else, you know, from lasers to semiconductors, it will basically develop as we get more cases that really set the boundaries of what is okay and what's not okay.
Misha：我不是這方面的專家，但我一般是樂觀的，這當然是個人看法，我也這麼看待其它新技術。將會出現在軍事領域的應用，正如在其它領域內一樣。然後這就需要社群和各種型別的國家和非國家的設定規則的機構來構想出法規和政策來防止對這項技術的濫用。所以，我認為這肯定會出現，人們肯定也會清楚這項技術的潛在危害。就像其它所有技術一樣，從鐳射到半導體，它基本上還會繼續發展，到有更多案例時，我們就能真正知道什麼是可行的，什麼不可行。
CRAIG: Are there areas that you feel either Yandex or Russia generally are ahead on, in either research or application. We talked before about some lack of the kinds of constraints that exist on the chatbots in the U.S.
Craig：你認為 Yandex或俄羅斯在哪些領域處於領先，不管是研究還是應用方面。我們之前談到了美國的聊天機器人還缺少某些約束。
MISHA: There’s not really, they're not hard constraints, right. I think the key question is that, whether we've been able to go forward at a much more rapid pace and that's one area where we have been moving very fast and at this point in, in terms of AI being in products, we are ahead of the other companies.
Misha：並不是沒有約束，只是不是嚴格的約束。我認為關鍵的問題是我們能否以遠遠更快的速度前進，而這是一個我們進展非常快的領域。目前，在 AI的產品化方面我們要領先其它公司。
The fact that our personal assistant includes both the pre-programmed intents - you know, things about, asking about weather, for example, or asking about, you know, facts, or asking to play music - as well as the general, what's known as chitchat, where it can converse on any topic whenever the, you know, the user wants to just talk to a bot. And so that's something that no other large-scale personal assistant has deployed.
事實上我們的個人助理既能執行預程式設計的任務——比如查詢天氣、查詢事實、命令播放音樂，也能進行一般化的聊天，也就是閒聊，它基本上能與使用者談論他們所想談論的任何主題。這是其它大規模個人助理還沒有部署的功能。
If look at Siri, if you look at Google Assistant, if you look at Alexa or Cortana – let’s say the big four of the personal assistants - they all have those hardwired intents, and then they have some pre-edited phrases for certain common requests, such as greetings for example or some Easter eggs. But none of them have a true AI sequence-to-sequence engine that is whenever the user just wants to chat, will chat back, on always, and try to be relevant. And we definitely have gotten quite a bit ahead of everybody. First of all because we have it out we have now millions of users using it. We have it being used across many services, in the phones and desktops, in cars and now in a smart speaker.
可以看看 Siri、GoogleAssistant、Alexa或小娜，可以說它們是個人助理四巨頭，它們都能執行預設定的任務，有一些預編輯的短語來執行特定的常見要求，還有問候或某些彩蛋。但它們都沒有真正的 AI序列到序列引擎，而如果使用者想要聊天以及讓聊天內容具有相關性，就需要這樣的引擎。在這方面我們肯定比其它公司領先。我們最早推出了這一功能，現在已有數百萬使用者。我們讓它執行在很多服務上，有手機和桌面電腦，有汽車，現在還有智慧音響。
Also, if you look at the core metrics of quality such as relevance, that's where we have made a lot of progress on relevance which is why people actually do it, is because they're able to get pretty, you know, what, what they view as snappy interesting answers that actually are not silly.
還有，如果你去測試一下相關性等核心指標，你會看到我們在相關性方法取得了很大的進展，所以人們才會真正使用它，因為他們能夠得到簡明有趣同時看起來又不傻里傻氣的答案。
CRAIG: And that's a learning system, so is it continually improving?
Craig：所以那是一個學習系統？會持續學習嗎？
MISHA: We definitely are learning from user feedback and as people talk to it, there's plenty of cues, such as, well, if somebody was talking for longer, if they were engaged, that's a good sign. And so, we definitely are continually improving the system.
Misha：我們肯定會根據使用者的反饋和使用者的談論來進行學習。有很多線索能說明問題，如果某人的交談時間變長了，如果他們會回話，那就是個好現象。而且我們肯定會持續改進這一系統。
In terms of the system changing itself with every - there's multiple ways in which it can learn, what's known as online, and that's where it gets very tricky because we've seen in the past where some of the experiments with true online learning can lead to the system basically being corrupted or a system can be exploited or trolled. And so that's where we're very careful to make sure that this evolution of the system is not basically making it worse or it's not, it cannot be exploited to start producing, you know, saying terrible things.
在改變自身的系統方面——系統有很多學習的方式，線上學習是一種著名的學習方式，但這是非常困難的，因為過去我們已經看到，某些使用真正的線上學習的實驗基本上都會導致系統遭到破壞，或系統被利用或被釣魚。所以我們在這方面很謹慎，以確保系統的演化不會導致系統變得更糟糕，使得其不會被人利用，說出些糟糕的東西。
CRAIG: And you have this function, uh, ‘Ask Pushkin?’ Can you talk about that? Is that a hard-wired intent or is that, uh ...
Craig：你們有一個叫做“問問普希金（Ask Pushkin）”的功能？你能談談這個功能嗎？這是預程式設計的還是聊天式的？
MISHA: It's a third-party intent. Actually that's, so, of one of the things we have is that, like other major platforms, we have a third party skills platform where basically anybody, whether it's a, say a pizza delivery service can come in and say ‘hey, you can now, you can now ask Alice - Alice is the name of our assistant - to order pizza’ or you know you can ask, for example, like Reebok made a personal training dial-up system that they're shipping through Alice.
Misha：這是一個第三方的功能。實際上這只是我們的功能中的一個。就像其它主要平臺一樣，我們有一個第三方的技能平臺，基本上任何人都可以加入，比如一個披薩外賣服務可以加入進來，然後使用者就可以讓 Alice幫助點披薩了——Alice是我們的個人助理的名字；還比如 Reebok製作了一個個人訓練的撥號新系統，也可通過 Alice使用
So, talk to Pushkin - so talking to a poet or Pushkin, who is the most famous Russian poet of all time, is a third-party skill. So, we actually don't know the specific details. It is definitely amusing. It's hard to say whether, you know, Pushkin has produced an amazing body of work that it's very easy to always find a relevant phrase, or it's the folks at Arzamas, who produced the skill, have done such a good job on matching that it actually does give you fairly good poetic advice, most the time. But yeah. But we're very happy that Alice can be also a pathway to talk to a famous dead poet.
與普希金交談是一個第三方的技能——普希金是有史以來最有名的俄羅斯詩人。所以我們實際上並不清楚具體細節。這肯定很迷人。你知道的，普希金寫出了很多精美的詩篇，找到與當前話題相關的片段並不容易；開發這個技能的 Arzamas 做得很好，能夠相當好地進行匹配，從而給出相當好的詩歌建議。我們也很高興 Alice能夠成為一條路徑，讓人們能與已經去世的著名詩人交談。
CRAIG: What is the company that produced it?
Craig：製作這個技能的公司是？
MISHA: So Arzamas has produced some very interesting content in Russian in the past. They produce podcasts as well that are about science, about technology. They're a great production shop so we’re very glad to host them on the platform as well.
Misha：Arzamas過去已經創造了一些非常有意思的俄語內容。他們也會製作有關科學和技術的播客。他們是一家很棒的產品商店，我們很高興他們能加入我們的平臺。
CRAIG: What are some of the other areas of research that you're focused on?
Craig：你們還關注哪些其它的研究領域？
MISHA: So, if you look at the core applications of AI from – so there's the ones that everybody hears about is vision, speech, both synthesis and speech understanding, linear regression. There is also machine translation.
Misha：AI有一些核心應用，人們常常聽到的有視覺、語音（包括合成和語音理解）、線性迴歸。另外還有機器翻譯。
Machine translation is especially essential to us because obviously there's a lot of content out there in English and then because we are based in Russia and most of our users are Russian speaking, for them, we view it as a really core mission for us to basically make all the information out there available to them in the language that they understand best.
機器翻譯對我們而言尤其重要，因為很顯然有很多內容都是英語的，而我們在俄羅斯，我們的大多數使用者都說俄語。對於我們的使用者，我們認為讓他們能夠用他們能最好理解的語言使用所有資訊是我們的一項核心使命。
Translation is where we have made a lot of progress in recent couple years and that's where the quality is actually really high and we appreciate that the users, we hear from users quite a bit, and we're actively integrating it in multiple products, like our browser for example.
過去兩年，我們在翻譯方面進展頗豐，現在的準確度已經相當高了，我們很感謝我們的使用者，我們從使用者那裡聽取了很多意見。我們正在積極地將其整合進多個產品中，比如我們的瀏覽器。
In all of these applications though there is - if you look at vision, there is a really cool new applications like super resolution where we're able, using neural networks, to make the image basically higher resolution and much prettier and you can do this with everything from TV channels that you're streaming to old movies that you can now show in much better quality.
我們所做的應用有很多，比如視覺方面有個很酷的應用叫超解析度，我們可以使用神經網路來讓影象具有更高的解析度和更清楚。我們可以使用這項技術來在電視訊道上以更高的質量播放老電影。
And then just core image search, which is improving really fast. And now besides image search, you can do things like detection of certain, certain objects. So, there's lots of applications there.
還有核心的影象搜尋應用，這方面提升得非常快。現在除了影象搜尋，還能檢測特定的物體。所以這方面有很多應用。
And then in speech, of course, and dialogue, there is a ton of exciting stuff happening with both the core quality going up and the error going down where people are just now much more likely to make queries and to use voice because it is just being recognized correctly – to the production of speech, text to speech becoming much more natural sounding. We've, we've been working on that very heavily. There's been a lot of public recognition of progress in English that Google and DeepMind have done in recent years.
當然還有語音和對話，這方面有很多激動人心的進展——質量在提升，錯誤率在下降。現在人們越來越多地使用語音來進行查詢了，因為現在它不僅能正確地識別，而且語音生成和文字轉語音的結果也正變得越來越自然。這方面我們投入了很多努力。谷歌和 DeepMind過去幾年已經在英語方面做出了很多已被公眾認知的成果。
But besides that, I think one thing that is also changing very rapidly is that a lot of the time when we're dictating, we use not just sort of common dictionary words but we'll use proper names or will use and even names that are personal to us like say names from the address book. And then those systems becoming aware of basically being personalized and becoming the speech recognition recognizing not just you know the literary text but all sorts of strange names you may have in your address book or you know difficult proper names of say restaurants. That’s definitely kind of a core area that has, English improvement, has been very strong. And that's what has been driving the systems of becoming more widely used and very helpful.
但除此之外，我認為還有一件事也改變得非常快。很多時候在我們喊人時，我們不會使用詞典中的那些常用詞，而會使用適當的名字，這些名字是非常個人化的，躺在地址薄中。現在，這些語音識別系統正變得越來越個性化，它們不僅能識別你說的話語，而且還能識別你的地址薄中的各種奇怪的名字。這肯定是一個核心領域，英語的提升非常強大。也能推動系統得到更廣泛的使用，從而提供更大的幫助。
So, in mission translation, just to show an example of how collaboration in the scientific sense is happening in the technology sense is that there is a topology of neural networks known as transformer that was invented by Google scientists. And we've basically taken it and then built on top of it improving both the topology but also the larger system within it to really dramatically improve the quality of translation. And so, on getting the quality of English to Russian and Russian to English translation dramatically in the past year and now the same quality increases are propagating to other directions for translation.
在翻譯任務中，舉個例子說明下科學和技術方面的合作方式。谷歌的科學家發明了一種名叫 transformer 的神經網路結構。我們基本上就將其拿來用了，然後基於其進行了開發。我們改進了這種結構，並且在其中構建了更大的系統，從而極大地提升了翻譯質量。過去一年，我們的英俄翻譯和俄英翻譯的質量得到了極大提升，現在這樣的提升效果也正在向其它翻譯方向傳播。
CRAIG: And on the computer vision, this improving video quality, is that using NVIDIA's extrapolation?
Craig：在計算機視覺方面，這能提升視訊質量，這使用了英偉達的 extrapolation嗎？
MISHA: So, we use NVIDIA for the cards but there the networks are entirely our own. So that's something that our teams have, basically they, you know, they read all the literature but the core net nets that are being used they are something that actually we're very proud of our team coming up with through lots of experimentation.
Misha：我們使用了英偉達的顯示卡，但網路完全是我們自己的。我們團隊會閱讀所有文獻，但我們使用的核心網路實際上是我們自己開發的，我們為此進行了大量實驗，我們也對此非常自豪。
CRAIG: And we also talked last time a little bit on your view of artificial general intelligence. I mean it's the topic everybody likes to talk about.
Craig：上次我們也談到了一點你對通用人工智慧的看法。這是一個人人都樂於談論的主題。
MISHA: Well, I think it's the - what is general intelligence keeps shifting in public view because as you know things that we, have become commonplace they are no longer as dramatically exciting as they used to be and so I think in that sense if we look at assistants and what they do in terms of they're able to basically help with the variety of tasks, they're able to provide information and now they're getting to a point we're able to also, you know, have small talk with us routinely. The most stringent definitions of general intelligence will also go beyond what the system can do towards what, what is inside it. And there, you know, you would, you define it as having much higher capacity reasoning capabilities for example.
Misha：嗯，我認為公眾眼裡的通用智慧概念一直在不斷變化，因為當這些東西變得隨處可見時，就沒法像過去那樣激動人心了。比如個人助理，它們能做的基本上是幫助執行各種任務，能夠提供資訊，現在還能與我們進行些日常的閒聊。對通用智慧的最嚴格的定義也會從系統能做到的事情轉向系統內部的情況。比如，定義它應該具有遠遠更高的推理能力。
But the alternative view is saying like, well, no matter what happens inside as long as it gives, as long as it can give you relevant and good answers - whether it can be trivialized as just, you know, search and pattern matching, even though done by very powerful algorithms and networks, but still is that intelligent or not?
但也有另外的看法，比如不管系統內部狀況如何，只要它能提供相關的優質答案就行——不管問題多麼微不足道，比如搜尋和模式匹配，但也許使用了非常強大的演算法和網路。這能否依然說是智慧的？
I mean there is a famous Chinese Room argument which crystallizes this paradox of like, well, do you care about what comes out and is it intelligent or is it really what goes on inside that defines what intelligence implies. And so, I think there is a continual improvement in terms of the quality of what comes out.
有一個非常著名的“中文屋”問題提出了一個悖論：只看結果能否確定系統是智慧的，還是說需要從系統內部定義智慧？而輸出結果的質量一直在不斷提升。
But there is when people discuss general intelligence a lot of time, they also focus on the fact that there needs to be much higher order processes inside. But that's, that's as much an engineering task as much as it is a scientific challenge. So that's where there's going to be, you know, there's lots of remaining challenges and continual progress. At the end of the day, it just comes down to better answers and better services.
人們花了很多時間來探討通用智慧，他們也很重視這樣一個事實，即還需要多很多數量級的處理能力。而且這既是一個工程任務，也是一個科學難題。所以，這就是未來的發展方向，還有很多難題有待解決，還需要持續不斷的進步。每天結束時，AI都能得到更好的答案，成就更好的服務。
CRAIG: Thanks, Misha, for your time. That’s all for this episode. Those of you who want to go into greater depth about the things we talked about today can find a transcript of this show in the program notes. Let us know whether you find the podcast interesting or useful and whether you have any suggestions about how we can improve.
Craig：Misha，感謝你抽空與我們分享。這就是本集的全部內容。請注意，奇點也許尚未臨近，但人工智慧即將改變這個世界。