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Last month, some of the biggest names in technology signed a pledge promising not to develop lethal autonomous weapons. Coming just after the recent employee-led protest over Google’s Project Maven, some have praised these initiatives as ethical and moral victories. Some, but not all. For Sandro Gaycken, a senior advisor to Nato, such initiatives are supremely complacent and risk granting authoritarian states an asymmetric advantage. “These naive hippy developers from Silicon Valley don’t understand – the CIA should force them,” says Gaycken, founder of the digital society institute at ESMT, a Berlin-based business school.

上个月,一些科技界大佬签署了一份协议,保证不会发展自主性致命武器。谷歌员工针对该公司的“专家”计划发起了抗议,有人赞许这些抗议是伦理道德的胜利。有的人则不这样认为。北约高级顾问盖肯认为,这样的抗议极其自以为是,会不对称地给独裁国家以优势。盖肯说,“这些硅谷里天真的嬉皮士开发人员不懂,中情局应该强迫他们执行计划”。盖肯是伯林商业学校ESMT数字社会研究所的创始人。

Gaycken’s hard advice reveals a schism emerging in the future development of AI for military purposes. On the one side are those that believe pursuing the development of military AI will lead to an unstoppable arms race. On the other side, people like Gaycken believe the AI arms-race has already begun. For them, prohibiting AI research for military purposes will not lead to peace but give the upper hand to authoritarian systems. Therefore, if the West wants to stay in the lead, it needs to unify around a concerted strategy. “Within most military and intelligence organisations it’s a concern,” Gaycken argues. “And it’s about to become a much larger concern.”

盖肯强硬的建议表明,人们在军用人工智能的未来发展上出现了分歧。有些人认为追求军事人工智能发展会导致无休止的军备竞赛,还有些人像盖肯一样认为人工智能军备竞赛已经开始了。对他们来说,禁止军用人工智能的研究不会带来和平,而是会给专制体制以优势。因此,如果西方想要保持领先地位,就需要团结一心。盖肯争论道,“很多军事和情报机构都担忧此事,这种担忧即将变得非常重大”。



Developing superior AI cyber weapons will enable one side to identify and exploit computational weaknesses within an adversary’s ICT infrastructure. From a military perspective, this opens up a great deal of creativity. “You could attack a military command and control centre, you could attack military vehicles, weapons systems and platforms, you could attack entire battleships and even drones,” says Gaycken.

发展优秀的人工智能武器将让你能够识别并利用敌人的信息与计算机技术基础设施的内部计算缺陷。从军事角度来看,这让你可以尽情发挥想象力。“你可以攻击一个军事指挥和控制中心,你可以攻击军事载具,武器系统和平台,你可以攻击整支战列舰队,甚至可以攻击无人机”,盖肯说。

As AI cyber weapons move beyond speculation, militaries are beginning to formulate methods for their tactical and strategic use. Nato recently released a paper which lay the theoretical framework for “AI Cyber hunters” – defensive AI agents, which patrol friendly systems and detect enemy malware. Offensive AI cyber weapons are already in development, but in Gaycken’s opinion they are still rudimentary. Nevertheless, the advantages that superior AI grants, means that nations are trying to gain dominance in this area. But how exactly do we measure AI power? And is it clear who is winning?

由于人工智能网络武器让人无法捉摸,军方人员正在着手规划其在战术和战略上的应用。北约最近发布了一份文件,该文件为“人工智能网络猎人”搭建了理论框架。“人工智能网络猎人”是一种防御性的人工智能特工,它们在友方系统中巡逻以及检测敌方的恶意软件。攻击性人工智能网络武器已经处于研发之中,但是在盖肯看来,研发中的网络武器还只是初步性的。不过,拥有出众的人工智能等于拥有各种优势,这意味着各国会设法在这个领域取得主导权。但是我们到底该如何衡量一个国家人工智能的实力?我们能否看出谁会赢?

Trying to measure AI power is no easy task. The tools, technologies and know-how are all “dual-use” – they lie scattered across civilian and military spheres, in locations around the world. Understanding a country’s relative AI power requires a deep knowledge of both the public and private sectors, with information often classified or deliberately misleading. Gaycken bemoans the commercial hype surrounding machine learning that, at the moment, makes it almost impossible to determine true capabilities.

衡量人工智能的实力不是件容易的事情。其中涉及的方法、科技和技能都是“军民两用的”,它们遍布于全球各地的军事和民事领域。要想知道一个国家人工智能的相对实力,就需要对该国政府和私营机构有深刻的了解,但是这些机构的信息通常是机密或故意误导的。盖肯叹息道,目前,围绕在机器学习周围的天花乱坠的商业广告,让人几乎不可能判断一个国家真正的人工智能实力。

Even if accurate information can be obtained, knowing how these capabilities might be deployed in a conflict scenario remains a mystery. Military power is only truly understood in real conflict scenarios. “War has a way of surprising you and exposing exactly how good your AI and all the money you’ve invested in it actually is,” Payne cautions. “It’s hard to know that before the fact.” Just as the battleship was unexpectedly stripped of its superweapon status by the aircraft carrier during the Second World War, the real deployment of military AI could lead to completely unexpected results.

即使你能获得准确的情报,你也不知道敌人会在何种情况中使用这些能力。只有在真正的战斗情境中,你才能了解对方的军事实力。佩恩说,“战争会让你感到意外,会让你知道你的人工智能和你投入的资金到底有多少回报。战争之前,你是很难知道这些的”。就像二战期间战列舰超级武器的地位意外地被航空母舰剥夺一样,军事人工智能的实战应用也将导致完全意外的结果。

Despite these uncertainties, experts are trying to gain a rough understanding of AI strengths and weaknesses. “What we have in reality is a sort of mixed, messy development in AI,” says Gaycken. “It’s not an equal, linear development, where everybody is getting equally good in all different fields or the same fields.”

尽管有这些不确定性,专家们正试图对人工智能的优缺点进行大概的了解。“在现实中,人工智能的发展有点混乱,大家在所有领域或同一领域取得的进展都是不一样的,不是均衡而线性的发展”,盖肯表示。

The US is still considered to be at the forefront of AI research, leading the way in industrial and military applications of AI. The country retains dominance across many of the standard metrics used as proxies to uate AI power, particularly intellectual talent, research breakthroughs and superior hardware. But despite US dominance, China’s strategic investments and vision, enshrined in the government’s ten year plan, is enabling it to catch up at a rapid pace. “China is rapidly emerging as an AI powerhouse,” says Elsa Kania, adjunct fellow with the Center for a New American Security’s technology and national security program, a Washington DC-based think-tank.

美国仍被认为是人工智能研究的领头羊,领导着人工智能的工业和军事应用。用于评估人工智能实力的很多标准都是美国主导的,在人才、研究突破和硬体等方面尤其如此。 但是尽管美国处于领先地位,但是中国的战略投资和战略眼光正在让其以飞快的速度赶上美国,中国政府已将人工智能纳入了十年计划。位于华盛顿特区的智囊团,“新美国安全中心”之技术与国家安全计划的客座研究员艾尔莎表示,“中国正在迅速成为人工智能大国”

From a military perspective, China is using AI to develop a range of unmanned aerial, ground, surface, and underwater vehicles that are becoming increasingly autonomous. It is also attempting to use AI as a way to get around one of its serious military disadvantages – a lack of real conflict experience. “The People’s Liberation Army (PLA) is also focused on the potential of AI in war-gaming, simulations, and realistic training that could help to compensate for its lack of actual combat training,” says Kania.

从军事角度来看,中国正在使用人工智能发展一系列用于空中、地下、地面和水下的无人设备,其水下设备变得越来越无人化。中国也正在尝试利用人工智能消除它的其中一个重大军事弱点——缺乏实战经验。艾尔莎说,“解放军同时还专注于挖掘人工智能在战争游戏、战争模拟和战争演习上的潜力,这有助于弥补缺乏实战经验导致的不足”。

Then there is the use of AI in disrupting and degrading adversary communications. Given its consistent concentration on electronic warfare as the workforce of its information operations capabilities, Kania believes that the PLA is also likely prioritising cognitive electronic warfare capabilities. And China is not the only nation pioneering the use of AI in information operations. “We know that the Russians are very good at AI combined with information warfare,” says Gaycken.

接下来谈谈使用人工智能干扰和弱化敌人的通信。由于解放军坚持把精力集中于电子战并把电子战作为其信息作战力量,艾尔莎认为,解放军也可能会优先发展以实际经验为依据的电子作战能力。中国并非唯一一个在信息战中使用人工智能的先锋。盖肯表示,“我们知道俄罗斯人非常擅长于把人工智能融入信息战”。

The most important components in achieving superior AI are data and talent. Access to new, large, structured datasets will give one side’s AI a considerable advantage over an adversary. The more consolidated and complete one side’s data becomes, the greater potential their AI has to make deeper and more accurate inferences. The quality and newness of this data is also crucial and depends upon where it is from and how accurately it has been labelled.

要想获得高人一筹的人工智能,最重要的要素是数据和人才。一个国家能够接触最新、海量、有组织的数据集,该国就能获得人工智能带来大量优势,从而让对手处于劣势。一个国家的数据越统一和完整,该国的人工智能做出更深层、更准备推论的可能性就越大。数据的质量和新鲜度也是至关重要的,这两个指标是由数据来源和数据归类的准确程度决定的。

The size of datasets is particularly important. “You have to have a lot of data, you have to be able to structure it and you have to be able to understand the causal relations from the simple statistical correlations or common-cause correlations,” says Gaycken. Put simply, more data makes it easier for machine learning systems to distinguish genuine causal relationships, from those that have arisen by chance.

数据集的大小特别重要。“你必须有大量数据,必须能够对其进行组织,必须能够明白统计相关性或普通相关性的因果关系”,盖肯表示。简而言之,数据越多,机器学习系统就越容易从偶发事件中辨别真实的因果关系。

In terms of access to consolidated data, China has an advantage. As the Chinese AI expert Kai-Fu Lee summarised in a recent report, China has 1.39 billion mobile phone and internet users; three times more than in the US and India. Chinese citizens also use their mobile phone to pay for goods 50 times more often than Americans.

说到统一的数据,中国具有优势 。就像中国人工智能专家李开复在最近的报告中总结的,中国有13.9亿手机和互联网用户,比美国和印度多3倍。中国公民用手机为商品付款的频率比美国人多50倍。

And China’s data superiority shows no sign of waning. National infrastructure is being designed to maximise the amount of data creation, capture and analysis. Nationwide programmes like the Social Credit System will add to an already vast, centralised trove of data. And the close relationship between the private sector, government, military and intelligence communities will make the sharing of data much easier – not to mention the relative absence of privacy concerns.

中国在数据上优势没有减弱的迹象,其国家基础设施的设计目标是将数据量的产生、获取和分析达到最大化。像社会信用体系这样的全国性项目将继续扩大其已经很庞大和集中的数据。民间、政府、军方以及情报界之间密切的关系将会使数据分享更容易,更别说中国人对隐私的担忧相对比较缺乏。

In contrast, the picture in the US, UK and EU is less centralised and more fragmented. Large US technology companies like Google, Facebook, Amazon and Apple, have access to huge amounts of data. However they protect it fiercely. Unlike in China, cooperation between organisations, whether public or private, is much harder to insist upon.

与之相反,美国、英国和欧盟的数据比较松散和碎片化。大型美国科技公司如谷歌、脸书、亚马逊和苹果都能获取大量数据,但是他们对此强烈反对。不像中国的组织,美国的组织,不管是政府还是民间组织,彼此之间的合作是非常难以持续的。

It’s here that the West’s fragmented startup ecosystem may present some drawbacks. An ecosystem of many, small AI companies can help foster competition, a plurality of opinion and innovation. However the competitive divisions which exist between these companies – and their reluctance to share data – makes for patchy and fragmented data-sharing. For developing stronger AI, Gaycken suggests that the startup ecosystem is not the optimal solution. “Startups have to be embedded into large corporate structures, to have access to the kind of data they require, to build high-quality AI,” he argues.

在这方面,西方碎片化的初创企业生态系统可能会存在一些缺陷。人工智能生态系统中的众多小型公司有助于培养竞争力,以及观点和创新的多重性。但是,这些公司之间由竞争产生的分裂,以及对分享数据的不情愿,造成了零散而碎片化的数据共享。盖肯指出,对于发展较强的人工智能,初创公司的生态系统并非最佳选择。他说:“初创企业不得不把自己镶嵌入大型组织架构中,才能获得他们需要的那种建立高质量人工智能的数据”

Just as important as the amount and quality of data, are the brains and engineering talent, which need to make sense of it. “You have to have the brains to work on the customisation and the improvement of the algorithms to fit to the specific vertical where you want to apply it,” says Gaycken.

跟数据数量与质量同样重要的是设计师和工程师,他们需要让数据变得有意义。“为了使其适用于你想要应用的特定目标,你不得不请设计师定制和完善相关的算法”,盖肯表示。

In regards to engineering talent, America leads the way followed by the UK, Canada and some parts of the EU. Kai-Fu Lee’s recent report claims that Google has as much as 50 per cent of the world’s top 100 AI scientists, working across Google Brain, Google Cloud and DeepMind. Much of this talent is spread throughout the US, UK, Canada and Europe. “There’s a limited pool of AI talent out there and where does that talent want to go to work? It wants to go to San Francisco, London, Toronto and Paris,” says Payne.

至于工程人才,美国有着世界上最多的顶尖人工智能工程师,排在美国后面的是英国、加拿大和部分欧洲国家。李开复最近的报告指出,世界上100位最出色的人工智能科学家有50%在谷歌,他们在谷歌大脑、谷歌云和迪普曼任职。这些顶尖人才大部分都在美国、英国、加拿大和欧洲。佩恩说,“人工智能人才的数量有限,这些人希望去哪工作?他们想去旧金山、伦敦、多伦多和巴黎”

In China, the government is making strategic investments to create a new generation of home-grown computer and data scientists. President Xi Jinping has invested considerable capital in overhauling China’s education system and placed great emphasis on mastery of STEM subjects (in 2013 Shanghai’s students ranked first in the OECD’s PISA tests) as well as a new curriculum which emphasises creative thinking, teamwork and innovation.

在中国,为了培养新一代的国内计算机和数据科学家,政府正在进行战略投资。中国已经投入了大量资金整顿中国教育系统,对掌握科学、技术、工程、数学(2013年上海学生在PISA测试中赢得了第一名)等学科以及强调创造性思维、团队协作和创新的新课程给予了极大的重视。

There are also clear differences in how talent can be utilised in more authoritarian systems. The command and control economies of authoritarian countries can compel citizens, experts and scientists to work for the military. “Where you require very good brains to understand what is going on and to find your niche, to find specific weaknesses and build specific strengths – in those countries they simply force the good guys to work for them,” Gaycken explains.

在更为专制的国家中,人才的使用也有明显的区别。这些国家对经济的管控可以让公民、专家和科学家为军队服务。盖肯解释道,“要想搞懂情况,找到你的定位,找到自己特有的弱势并发展特有的优势 ,你需要非常聪明的人才。在那些国家,他们直接强迫人才为他们工作”。

Another practical challenge that Western militaries face is the competition for rare talent with the private sector. “Not even the defence industry is able to compete with the IT industry,” Gaycken says. In the US, graduates with PhDs in machine learning are taking home salaries of between $300,000 to $500,000. And giant technology companies like Amazon, Uber and Google are renowned for raiding the machine learning and robotics departments of top universities.

西方军队面临的另一个挑战是他们要跟私营企业争夺稀缺人才。盖肯说,“连国防工业都无法与IT工业竞争”。在美国,机器学习专业的博士毕业生薪资在30万到50万美元之间。像亚马逊、优步和谷歌等科技巨头以搜刮顶尖大学的机器学习和机器人领域的人才而闻名。

Measuring financial investment in machine learning R&D can also be used as a proxy to estimate AI capabilities. But, as Kenneth Payne argues, “money is a pretty crude indicator”. Financial investment can reveal intent but not necessarily capability. It’s difficult to tell how well money is being spent and whether investment is being used to fund longer-term fundamental research or to achieve short-term commercial gains.

一个国家在机器学习领域投入的研发金额也可以被用于估算其人工智能实力的参照。但是佩恩争论道,“投资额度是一个非常不准确的指标”。金融投资可以揭示其意图,但是不一定能揭示其实力。你很难知道他们的钱是怎么花的,他们是把钱用在了资助长期基础性研究上,还是用在了长期商业投资上。

Looking at publicly available data, China is setting the pace when it comes to public investment. The government’s strategic investment programme, has grown from just over $5 billion in 2008 to $27 billion in 2017. As Kai-Fu Lee notes, there’s also been a large increase in private sector investment, rising from just under $5 billion in 2014 to over $25 billion in 2017. Much of this has flowed into China’s dominant internet companies, including Baidu, Tencent and Alibaba. However it is also supporting a rapidly growing start-up ecosystem, which includes companies such as Face++, iFlyTek, DJI and 4th Paradigm. Investment levels in the US, UK and EU are also growing. But public money is not matching levels of private investment.

从公开的数据来看,中国的公共投资是世界领先的。政府的战略投资从2008年的50多亿增长到了2017年的270亿。据李开复所说,民间的投资也有大幅提升,从2014年的不到50亿增长到了2017年的250多亿。这些钱大部分都流入了中国主要的互联网公司,包括百度、腾迅和阿里巴巴。但是这些钱也支持了迅速成长的初创企业生态系统,包括旷视科技、科大迅飞、大疆和第四范式。美国、英国和欧盟的投资水平也在增长。但是政府投资额度远低于民间投资。

A more accurate proxy to understanding capability is to explore the number and quality of research breakthroughs. Here the US still leads the way, followed by the UK. “It’s Google, it’s DeepMind that have been making some of the big running in their decision-making, in computer games or in their ability to convincingly manipulate video for example,” says Payne.

了解一国人工智能实力更准确的方法是了解其研究数量和质量上的突破。这方面,美国仍然是第一,第二是英国。佩恩说,“比如,在人工智能决策、电脑游戏或处理视频的能力上,谷歌和迪普曼一直都有较大进展”

But China is closing the gap. According to one study, the ethnicity of the top 100 AI journals and conferences increased 43 per cent between 2006 and 2015. The number of citations went up 55 per cent during the same period.

但是中国正在缩小差距。根据一项调查,在2006年到2015年期间,排名前100的人工智能学术期刊和研讨会的多国性提高了43%。同一时期,论文引用数量提高了55%。

The US still leads the way in the development of AI hardware. Led by companies like Nvidia, Intel, Altera and AMD the US still has the edge when it comes to designing and developing AI chips. As Kai-Fu Lee explains, these companies “have a major advantage over these Chinese startups in terms of intellectual property, manpower, resources and industry experience”. But whether through commercial acquisition, domestic innovation or theft, the Chinese are looking to address these weaknesses.

美国仍然领导着人工智能硬件的发展。以英伟达、英特尔、阿尔特拉和AMD为首的硬件企业让美国在设计和开发人工智能芯片方面仍然领先于世界。正如李开复所说的,“相较于中国的初创企业,这些公司在知识产权、人才、资源和行业经验等方面有着巨大的优势”。但是不管是通过商业收购、国内创新还是窃取,中国正渴望着解决这些弱点。

Some of these efforts may have already begun to pay off. Just last month, Baidu announced the creation of a new AI chip. The capabilities of this chip have yet to be revealed and it is not yet ready for manufacture. However, this announcement signals that China’s focus on AI hardware is meaningful. Kania believes that if the Chinese are able to overcome their persistent difficulties in the semiconductor industry and design truly indigenous AI chips, this would represent “a key inflection point” in the race for AI dominance.

这些努力已经开始产生一些回报了。上个月,百度宣布创造了新的人工智能芯片。该芯片的能力没有有被揭露,并且还没有准备好量产。但是,这一消息说明中国非常注重人工智能硬件的发展。艾尔莎认为如果中国能够克服他们在半导体行业持久的难题,设计出真正的国产人工智能芯片,那就代表人工智能主导权之争进入了“关键转折点”

Beyond innovation, theft is another important tactic in the race for AI superiority. Gaycken believes that the theft of intellectual property is occurring at numerous levels: “Stealing certain improvements in the environment, improvements in sensor data, improvements in the speed and quality of computing – everything that is implementing and configuring AIs and that customizes AIs for specific verticals – there’s very, very strong interest from intelligence agencies around the world.” Theft is also focused on talent resources. “The targeted recruitment of talent, particularly researchers with tacit knowledge that is vital to advances in such complex technologies, will become ever more of a priority,” says Kania.

除了创新,人工智能主导权争夺的另一个重要战术是窃取。盖肯相信知识产权的窃取正在很多层面上演:“窃取的东西包括:运行环境的技术改进、传感器数据的技术改进、计算速度与质量的技术改进。所有能实现和配置自动识别系统以及可以为特定目的定制自动识别系统的技术都是窃取的对象。全球的情报机构提供了很高很高的报酬”。各国也专注于对人才的窃取。艾尔莎说,“人才的定向招募将成为一种永远的优势,特别是具有隐性知识的研究人员的招募,其隐性知识在这些复杂的技术中对技术进步极其重要”。



To be sure, there are potential disadvantages to having such a close relationship between the military and civilian sectors. As Elsa Kania has argued in a recent article, “the expansion of the CCP’s presence within tech companies may harm creativity and innovation.” Excessive state involvement could also lead to excessive levels of investment – leading to a tech bubble – as well as formenting power struggles between political and technology leaders. It is not clear that China’s way will win the day.

诚然,军方与民间的关系如此紧密也会有一些潜在的缺点。艾尔莎在最近的文章中表示,“中国政府扩大其在科技公司的存在可能会伤害创造力和创新力”。持续的国家介入可能还会造成过多的热钱涌入,导致科技泡沫,还会造成政治和科技领袖之间的争斗。中国的方式能否赢得人工智能主导权还不清楚。

For the time being, America and other Western nations still possess dominance in technology, knowledge and research breakthroughs. But, according to Gaycken, in order for the West to win this race, it must change approach. “The industries will have to cooperate very strongly and very closely with the military and they will have to exchange their intellectual property with each other.” A scenario which, for the time being, seems unlikely.

目前,美国和其他西方国家仍然主导着科技、知识和研究上的突破。但是盖肯表示,为了赢得这场竞争,西方必须改变方法。“民间机构必须非常强烈、非常密切地跟军方合作,他们彼此之间必须分享知识产权”。目前,这样的景象好像还不可能。