对话Yandex AI负责人Misha Bilenko:AI发展依靠社区与合作,不是国家间的军备竞赛

导语:Eye on A.I.是由纽约时报资深记者 Craig S. Smith 主持的一档双周博客节目。每一期节目,Craig 都将与这一领域有影响力的人物进行交流,推进广义环境中的机器智能新发展,思考技术发展新蕴意。

机器之心为此系列对话的中文合作方。以下为此系列内容的第三篇,Craig Smith 与 Yandex AI负责人Misha Bilenko展开的对话。

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,这是一个有关人工智能的新播客。我之前是《纽约时报》的记者,现在专注于 AI,我将与正在该领域做贡献的人对话。我目前正在斯德哥尔摩参加今年的国际机器学习会议 ICML,并与 Yandex的 AI负责人 Misha Bilenko进行了对话。Yandex可以说是俄罗斯版的谷歌。我认为 Misha说的很多内容都很有启发性,希望你也这样认为。

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.”

我们首先谈到了人工智能研究中的国家行为者的角色。目前已有很多国家级的 AI战略出台。弗拉基米尔·普京去年有个著名的言论:“谁成为 AI领域的领导者,谁就将掌控世界。”我问 Misha他是否看到了国家层面的 AI研究,他认为是否存在 AI“军备竞赛”的风险。

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:考虑到这个领域的社区性和国际性,我认为在研究领域人们不会从国家战略的角度来思考。如果你看看当前研究的作者所属机构的研究者组成,然后再了解一下他们的教育路径和来源,就能看到每个人都在到处走动,而且他们所关心的通常都是科学领域,而非某个政策领域。我认为人们很容易想到那些有关国家政策的传言轶事或者编造一些故事,但技术领域则不同,人们关心的是算法,这个领域的人通常非常渴望合作,而且他们有来自世界各地的朋友和同事,而且他们过去和现在也一直在与来自世界各地的人合作。他们不仅在与来自世界各地的人合作,而且在此过程中还会在世界各地移动。

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,这是一个非常世界性的社区。在这个领域,任何存在于国家战略的政治领域中的言辞都不具有说服力,与这里的基调不符,因为这并不是这个领域的人的思考方式,也不是该领域的运作方式。每个人想的都是我们具有的技术问题、挑战和算法以及进展情况,可以认为这个领域的每个人都是为了推动科学和技术的进一步发展。

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:而且并不存在“俄罗斯”或“美国”的 AI。我的意思是每项重要的研究成果都会公开发布——也许军事应用除外,但军事应用很少有基础研究。

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:我对军事应用一无所知——我从没开发过军事应用,但这个领域本身是国际性的,所以 ICML有意设计成每年都从一个大陆转到另一个大陆举办。每一项有趣的研究都能在这些核心会议上得到发表——ICML、NIPS、ICLR;论文还会在 arXiv上发布,这就像是一个互联网科学研究档案馆。因此,就像我说的,和做技术领域的人不同,这个领域搞科研的人基本都不思考国家安全和政策。技术成就和科学的算法发展是完全不同的区域。

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:正在出台的这些国家战略的意义只是构建一个由出色的研究者和工程师组成的国家队,这样每个有国家战略的国家都会觉得自己在这场游戏中占据了一席之地。

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:我认为是这样,看看科学的流程,首先是从教育开始的,基本上你需要找到特定的教授和实验室以及研究方向,以确保你有接受过这个领域教育的专家,然后这些专家会继续进行学术研究或创立公司,然后再创造商业应用和技术。

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,在这个意义上,我将其看作是一个经济问题——如果有那么多经济体正被 AI改变,那么这意味着每个经济体要想进步,创造这些未来领域的专家是至关重要的。因此,对于每个技术先进的国家,从经济角度上看必须要做的事情是拥有创造人才的学术流程。因为经济流程通常是由政府资助的,因为政府必须资助或调整它们的开支优先级,以资助这些领域。因此,我不会从国家安全角度看待这个问题,而会通过技术角度,尤其最重要的是经济角度。

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:我们之前谈到了目前全球这一领域内称职的科学家的数量,估计是在 2万或三万到 20万之间,但相对来说仍然很少。

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:这实际上取决于你划在的科学家和工程师和数据科学家之间的界线在哪——数据科学家横跨两个世界,他们既是分析师和科学家,也是工程师。数十万听起来很合理。但重申一下,这非常取决于你设定的区分他们的位置。

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:你能估计俄罗斯与中国和美国的人才数量多寡吗?

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,俄罗斯肯定很多。我觉得俄罗斯就像是中国。这也是说明国家之间的区别不再重要的原因,因为你可以看看研究者的名字,从中你可以推断作者的族裔,你能看到很多俄罗斯人和中国人的名字。同样,如果你看看他们的所属机构,可以看到它们位于世界各地;你也能看到俄罗斯和中国的机构下有一些非俄罗斯或非中国的名字。所以我认为中国科学家或俄罗斯科学家的存在感肯定很强。但这是一个全球性的体系,每个人都在到处移动以及合作,所以这很大程度上不是国家的问题。

CRAIG: So, the public perception of there being a sort of arms race in AI is mistaken or is a misnomer.

Craig:所以,公众认知中存在某种 AI军备竞赛的看法是错误的或用词不当。

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:我认为是用词不当。我并不认为这是正确的……我认为这是一种非常具有竞争意识和非常对抗性的世界观。我现在在想,如果存在某种以这种方式看待世界的趋势——肯定有人这么看,但我认为这不是这个领域内的任何人或大部分人看待的方式。这个领域内的大部分人更多是关注自己研究的领域,然后从合作的角度看问题——每个人都与每个人合作,这不是“军备竞赛”的意思。军备竞赛意味着激烈的竞争以及显著的区分,但目前不存在这种情况,这仍然是一个非常合作性的领域。

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:随着系统变得越来越通用化,以及军方也开始研发相关军事应用,有关 AI安全的话题也正得到越来越多的讨论。你认为是否会出现不同国家都会遵守的国际性公约?

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:我不是这方面的专家,但我一般是乐观的,这当然是个人看法,我也这么看待其它新技术。将会出现在军事领域的应用,正如在其它领域内一样。然后这就需要社区和各种类型的国家和非国家的设定规则的机构来构想出法规和政策来防止对这项技术的滥用。所以,我认为这肯定会出现,人们肯定也会清楚这项技术的潜在危害。就像其它所有技术一样,从激光到半导体,它基本上还会继续发展,到有更多案例时,我们就能真正知道什么是可行的,什么不可行。

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:你认为 Yandex或俄罗斯在哪些领域处于领先,不管是研究还是应用方面。我们之前谈到了美国的聊天机器人还缺少某些约束

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:并不是没有约束,只是不是严格的约束。我认为关键的问题是我们能否以远远更快的速度前进,而这是一个我们进展非常快的领域。目前,在 AI的产品化方面我们要领先其它公司。

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.

事实上我们的个人助理既能执行预编程的任务——比如查询天气、查询事实、命令播放音乐,也能进行一般化的聊天,也就是闲聊,它基本上能与用户谈论他们所想谈论的任何主题。这是其它大规模个人助理还没有部署的功能。

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,可以说它们是个人助理四巨头,它们都能执行预设定的任务,有一些预编辑的短语来执行特定的常见要求,还有问候或某些彩蛋。但它们都没有真正的 AI序列到序列引擎,而如果用户想要聊天以及让聊天内容具有相关性,就需要这样的引擎。在这方面我们肯定比其它公司领先。我们最早推出了这一功能,现在已有数百万用户。我们让它运行在很多服务上,有手机和桌面电脑,有汽车,现在还有智能音箱。

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.

还有,如果你去测试一下相关性等核心指标,你会看到我们在相关性方法取得了很大的进展,所以人们才会真正使用它,因为他们能够得到简明有趣同时看起来又不傻里傻气的答案。

CRAIG: And that's a learning system, so is it continually improving?

Craig:所以那是一个学习系统?会持续学习吗?

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:我们肯定会根据用户的反馈和用户的谈论来进行学习。有很多线索能说明问题,如果某人的交谈时间变长了,如果他们会回话,那就是个好现象。而且我们肯定会持续改进这一系统。

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.

在改变自身的系统方面——系统有很多学习的方式,在线学习是一种著名的学习方式,但这是非常困难的,因为过去我们已经看到,某些使用真正的在线学习的实验基本上都会导致系统遭到破坏,或系统被利用或被钓鱼。所以我们在这方面很谨慎,以确保系统的演化不会导致系统变得更糟糕,使得其不会被人利用,说出些糟糕的东西。

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:你们有一个叫做“问问普希金(Ask Pushkin)”的功能?你能谈谈这个功能吗?这是预编程的还是聊天式的?

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:这是一个第三方的功能。实际上这只是我们的功能中的一个。就像其它主要平台一样,我们有一个第三方的技能平台,基本上任何人都可以加入,比如一个披萨外卖服务可以加入进来,然后用户就可以让 Alice帮助点披萨了——Alice是我们的个人助理的名字;还比如 Reebok制作了一个个人训练的拨号新系统,也可通过 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.

与普希金交谈是一个第三方的技能——普希金是有史以来最有名的俄罗斯诗人。所以我们实际上并不清楚具体细节。这肯定很迷人。你知道的,普希金写出了很多精美的诗篇,找到与当前话题相关的片段并不容易;开发这个技能的 Arzamas 做得很好,能够相当好地进行匹配,从而给出相当好的诗歌建议。我们也很高兴 Alice能够成为一条路径,让人们能与已经去世的著名诗人交谈。

CRAIG: What is the company that produced it?

Craig:制作这个技能的公司是?

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过去已经创造了一些非常有意思的俄语内容。他们也会制作有关科学和技术的播客。他们是一家很棒的产品商店,我们很高兴他们能加入我们的平台。

CRAIG: What are some of the other areas of research that you're focused on?

Craig:你们还关注哪些其它的研究领域?

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有一些核心应用,人们常常听到的有视觉、语音(包括合成和语音理解)、线性回归。另外还有机器翻译

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.

机器翻译对我们而言尤其重要,因为很显然有很多内容都是英语的,而我们在俄罗斯,我们的大多数用户都说俄语。对于我们的用户,我们认为让他们能够用他们能最好理解的语言使用所有信息是我们的一项核心使命。

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.

过去两年,我们在翻译方面进展颇丰,现在的准确度已经相当高了,我们很感谢我们的用户,我们从用户那里听取了很多意见。我们正在积极地将其整合进多个产品中,比如我们的浏览器。

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.

我们所做的应用有很多,比如视觉方面有个很酷的应用叫超分辨率,我们可以使用神经网络来让图像具有更高的分辨率和更清楚。我们可以使用这项技术来在电视频道上以更高的质量播放老电影。

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.

还有核心的图像搜索应用,这方面提升得非常快。现在除了图像搜索,还能检测特定的物体。所以这方面有很多应用。

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.

当然还有语音和对话,这方面有很多激动人心的进展——质量在提升,错误率在下降。现在人们越来越多地使用语音来进行查询了,因为现在它不仅能正确地识别,而且语音生成和文本转语音的结果也正变得越来越自然。这方面我们投入了很多努力。谷歌和 DeepMind过去几年已经在英语方面做出了很多已被公众认知的成果。

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,我认为还有一件事也改变得非常快。很多时候在我们喊人时,我们不会使用词典中的那些常用词,而会使用适当的名字,这些名字是非常个人化的,躺在地址薄中。现在,这些语音识别系统正变得越来越个性化,它们不仅能识别你说的话语,而且还能识别你的地址薄中的各种奇怪的名字。这肯定是一个核心领域,英语的提升非常强大。也能推动系统得到更广泛的使用,从而提供更大的帮助。

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.

在翻译任务中,举个例子说明下科学和技术方面的合作方式。谷歌的科学家发明了一种名叫 transformer 的神经网络结构。我们基本上就将其拿来用了,然后基于其进行了开发。我们改进了这种结构,并且在其中构建了更大的系统,从而极大地提升了翻译质量。过去一年,我们的英俄翻译和俄英翻译的质量得到了极大提升,现在这样的提升效果也正在向其它翻译方向传播。

CRAIG: And on the computer vision, this improving video quality, is that using NVIDIA's extrapolation?

Craig:在计算机视觉方面,这能提升视频质量,这使用了英伟达的 extrapolation吗?

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:我们使用了英伟达的显卡,但网络完全是我们自己的。我们团队会阅读所有文献,但我们使用的核心网络实际上是我们自己开发的,我们为此进行了大量实验,我们也对此非常自豪。

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:上次我们也谈到了一点你对通用人工智能的看法。这是一个人人都乐于谈论的主题。

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,我认为公众眼里的通用智能概念一直在不断变化,因为当这些东西变得随处可见时,就没法像过去那样激动人心了。比如个人助理,它们能做的基本上是帮助执行各种任务,能够提供信息,现在还能与我们进行些日常的闲聊。对通用智能的最严格的定义也会从系统能做到的事情转向系统内部的情况。比如,定义它应该具有远远更高的推理能力。

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,比如不管系统内部状况如何,只要它能提供相关的优质答案就行——不管问题多么微不足道,比如搜索和模式匹配,但也许使用了非常强大的算法和网络。这能否依然说是智能的?

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.

有一个非常著名的“中文屋”问题提出了一个悖论:只看结果能否确定系统是智能的,还是说需要从系统内部定义智能?而输出结果的质量一直在不断提升。

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.

人们花了很多时间来探讨通用智能,他们也很重视这样一个事实,即还需要多很多数量级的处理能力。而且这既是一个工程任务,也是一个科学难题。所以,这就是未来的发展方向,还有很多难题有待解决,还需要持续不断的进步。每天结束时,AI都能得到更好的答案,成就更好的服务。

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,感谢你抽空与我们分享。这就是本集的全部内容。请注意,奇点也许尚未临近,但人工智能即将改变这个世界。

产业Misha BilenkoYandex计算机视觉神经网络语音识别机器翻译机器学习
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相关数据
模式匹配技术

在计算机科学中,模式匹配就是检查特定序列的标记是否存在某种模式的组成部分。 与模式识别相比,匹配通常必须是精确的。 模式通常具有序列或树结构的形式。 模式匹配的使用包括输出令牌序列内的模式的位置(如果有的话),输出匹配模式的某个分量,以及用另一个令牌序列(即搜索和替换)替换匹配模式。

机器学习技术

机器学习是人工智能的一个分支,是一门多领域交叉学科,涉及概率论、统计学、逼近论、凸分析、计算复杂性理论等多门学科。机器学习理论主要是设计和分析一些让计算机可以自动“学习”的算法。因为学习算法中涉及了大量的统计学理论,机器学习与推断统计学联系尤为密切,也被称为统计学习理论。算法设计方面,机器学习理论关注可以实现的,行之有效的学习算法。

感知技术

知觉或感知是外界刺激作用于感官时,脑对外界的整体的看法和理解,为我们对外界的感官信息进行组织和解释。在认知科学中,也可看作一组程序,包括获取信息、理解信息、筛选信息、组织信息。与感觉不同,知觉反映的是由对象的各样属性及关系构成的整体。

人工智能技术

在学术研究领域,人工智能通常指能够感知周围环境并采取行动以实现最优的可能结果的智能体(intelligent agent)

计算机视觉技术

计算机视觉(CV)是指机器感知环境的能力。这一技术类别中的经典任务有图像形成、图像处理、图像提取和图像的三维推理。目标识别和面部识别也是很重要的研究领域。

机器翻译技术

机器翻译(MT)是利用机器的力量「自动将一种自然语言(源语言)的文本翻译成另一种语言(目标语言)」。机器翻译方法通常可分成三大类:基于规则的机器翻译(RBMT)、统计机器翻译(SMT)和神经机器翻译(NMT)。

神经网络技术

(人工)神经网络是一种起源于 20 世纪 50 年代的监督式机器学习模型,那时候研究者构想了「感知器(perceptron)」的想法。这一领域的研究者通常被称为「联结主义者(Connectionist)」,因为这种模型模拟了人脑的功能。神经网络模型通常是通过反向传播算法应用梯度下降训练的。目前神经网络有两大主要类型,它们都是前馈神经网络:卷积神经网络(CNN)和循环神经网络(RNN),其中 RNN 又包含长短期记忆(LSTM)、门控循环单元(GRU)等等。深度学习是一种主要应用于神经网络帮助其取得更好结果的技术。尽管神经网络主要用于监督学习,但也有一些为无监督学习设计的变体,比如自动编码器和生成对抗网络(GAN)。

线性回归技术

在现实世界中,存在着大量这样的情况:两个变量例如X和Y有一些依赖关系。由X可以部分地决定Y的值,但这种决定往往不很确切。常常用来说明这种依赖关系的最简单、直观的例子是体重与身高,用Y表示他的体重。众所周知,一般说来,当X大时,Y也倾向于大,但由X不能严格地决定Y。又如,城市生活用电量Y与气温X有很大的关系。在夏天气温很高或冬天气温很低时,由于室内空调、冰箱等家用电器的使用,可能用电就高,相反,在春秋季节气温不高也不低,用电量就可能少。但我们不能由气温X准确地决定用电量Y。类似的例子还很多,变量之间的这种关系称为“相关关系”,回归模型就是研究相关关系的一个有力工具。

聊天机器人技术

聊天机器人是经由对话或文字进行交谈的计算机程序。能够模拟人类对话,通过图灵测试。 聊天机器人可用于实用的目的,如客户服务或资讯获取。有些聊天机器人会搭载自然语言处理系统,但大多简单的系统只会撷取输入的关键字,再从数据库中找寻最合适的应答句。

语音识别技术

自动语音识别是一种将口头语音转换为实时可读文本的技术。自动语音识别也称为语音识别(Speech Recognition)或计算机语音识别(Computer Speech Recognition)。自动语音识别是一个多学科交叉的领域,它与声学、语音学、语言学、数字信号处理理论、信息论、计算机科学等众多学科紧密相连。由于语音信号的多样性和复杂性,目前的语音识别系统只能在一定的限制条件下获得满意的性能,或者说只能应用于某些特定的场合。自动语音识别在人工智能领域占据着极其重要的位置。

查询技术

一般来说,查询是询问的一种形式。它在不同的学科里涵义有所不同。在信息检索领域,查询指的是数据库和信息系统对信息检索的精确要求

序列到序列技术

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