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科技‖作战算法

admin 2019-10-7 19:23 142人围观 C++相关



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AI and war

人工智能与战争

Battle algorithm

作战算法

Artificial intelligence is transforming every aspect of warfare

人工智能正在改变战争的方方面面





AS THE NAVY plane swooped low over the jungle, it dropped a bundle of devices into the canopy below. Some were microphones, listening for guerrilla footsteps or truck ignitions. Others were seismic detectors, attuned to minute vibrations in the ground. Strangest of all were the olfactory sensors, sniffing out ammonia in human urine. Tens of thousands of these electronic organs beamed their data to drones and on to computers. In minutes, warplanes would be on their way to carpet-bomb the algorithmically-ordained grid square. Operation Igloo White was the future of war—in 1970.

海军的飞机俯冲到丛林上方,向下面的树冠投放了一捆装置。其中有侦听游击队脚步声或卡车点火声的拾音器,还有探测地面微小振动的地震探测器,而最奇怪的当属能嗅出人体尿液中的氨气的嗅觉传感器。几万个此类电子装置将收集到的数据发送给无人机和计算机。几分钟之内,战机就会按照算法标定的坐标网格展开地毯式轰炸。这个名为Igloo White的行动展示了未来战争的形态——不过这是在1970年。

America’s effort to cut the Ho Chi Minh trail running from Laos into Vietnam was not a success. It cost around $1bn a year (about $7.3bn in today’s dollars)—$100,000 ($730,000 today) for every truck destroyed—and did not stop infiltration. But the allure of semi-automated war never faded. The idea of collecting data from sensors, processing them with algorithms fuelled by ever-more processing power and acting on the output more quickly than the enemy lies at the heart of military thinking across the world’s biggest powers. And today that is being supercharged by new developments in artificial intelligence (AI).

美国试图通过这项行动切断从老挝进入越南的胡志明小道,不过以失败告终。它每年耗资约10亿美元(相当于现在的73亿美元)——其中每摧毁一辆卡车就要花费10万美元(相当于现在的73万美元),却仍未能切断这条支援路线。但半自动化战争的诱惑却从未消退。利用传感器收集数据,由不断增强的处理能力支持算法分析数据,并根据处理结果先发制人,这种想法是世界列强的核心军事思想。如今,人工智能(AI)的新发展为它提供了强大的驱动力。

AI is “poised to change the character of the future battlefield”, declared America’s Department of Defence in its first AI strategy document, in February. A Joint Artificial Intelligence Centre (JAIC) was launched in the Pentagon in summer 2018, and a National Security Commission on Artificial Intelligence met for the first time in March. The Pentagon’s budget for 2020 has lavished almost $1bn on AI and over four times as much on unmanned and autonomous capabilities that rely on it.

美国国防部2月在首次发布的AI战略文件中宣布,AI“已准备好改变未来战场的特征”。去年夏天,联合人工智能中心(以下简称JAIC)在五角大楼成立。今年3月,国家人工智能安全委员会首次召开会议。五角大楼2020年在AI上的预算高达近10亿美元,此外还有四倍于此的预算用于依赖AI的无人驾驶和自主控制能力。

Rise of the machines

机器的崛起

A similar flurry of activity is under way in China, which wants to lead the world in AI by 2030 (by what measure is unclear), and in Russia, where President Vladimir Putin famously predicted that “whoever becomes the leader in this sphere will become the ruler of the world”. But the paradox is that AI might at once penetrate and thicken the fog of war, allowing it to be waged with a speed and complexity that renders it essentially opaque to humans.

中国也在进行一系列相似的行动,它希望在2030年之前能在AI领域引领世界(按什么标准来衡量就不得而知了)。俄罗斯总统普京有一句著名的预言:“谁成为这个领域的统帅,谁就将成为世界的主宰。”但矛盾的是,AI可能会在穿透战争迷雾的同时让战争更加迷雾重重,它将极大地提升战争的速度和复杂性,让人类难以看透。

AI is a broad and blurry term, covering a range of techniques from rule-following systems, pioneered in the 1950s, to modern probability-based machine learning, in which computers teach themselves to carry out tasks. Deep learning—a particularly fashionable and potent approach to machine learning, involving many layers of brain-inspired neural networks—has proved highly adept at tasks as diverse as translation, object recognition and game playing (see chart). Michael Horowitz of the University of Pennsylvania compares AI to the internal combustion engine or electricity—an enabling technology with myriad applications. He divides its military applications into three sorts. One is to allow machines to act without human supervision. Another is to process and interpret large volumes of data. A third is aiding, or even conducting, the command and control of war.

AI是一个宽泛而模糊的术语,涵盖了一系列技术,从上世纪50年代开创的遵循规则的系统,到现代基于概率的机器学习(计算机自学如何执行任务),不一而足。深度学习是一种特别时兴和有效的机器学习方法,涉及受人类大脑启发而来的多层神经网络,已在翻译、目标识别和游戏等不同类型的任务中展现出很强的能力(见图表)。宾夕法尼亚大学的迈克尔·霍洛维兹认为AI和内燃机或电力一样,是可以支持无数应用的使能技术。他将AI在军事领域的应用分为三类,一是让机器在无人监督的情况下行动,二是处理和解释大量数据,三是协助甚至直接指挥和控制战争。





Start on the battlefield. The appeal of autonomy is obvious—robots are cheaper, hardier and more expendable than humans. But a machine capable of wandering the battlefield, let alone spilling blood on it, must be intelligent enough to carry that burden—an unintelligent drone will not survive for long in a battle; worse still, an unintelligent gun-toting robot is a war crime waiting to happen. So AI is required to endow machines with the requisite skills. Those include simple ones, like perception and navigation, and higher-order skills, like co-ordination with other agents.

先从战场说起。能自主行动的机器显然很吸引人一一机器人比人类士兵成本低、更大胆强悍,也更不怕被牺牲掉。但一台能在战场上驰骋甚至杀戮的机器必须足够聪明才能承担这种重任。低智力的无人机在战斗中存活不了多久。更糟糕的是,低智能的武装机器人随时可能犯下战争罪行。因此AI需要赋予机器必要的技能,包括感知和导航等简单技能,以及与其他战争主体协调行动等高阶技能。

Intelligent machines that combine these abilities can do things that individual humans cannot. “Already, an AI system can outperform an experienced military pilot in simulated air-to-air combat,” notes Kenneth Payne of King’s College London. In February, the Defence Advanced Research Projects Agency (DARPA), the Pentagon’s blue-sky-thinking branch, conducted the latest test of a six-strong drone swarm capable of collaborating in a “high-threat” environment, even when cut off from human contact.

综合了这些技能的智能机器可以做到人类个体无法完成的事情。伦敦国王学院的肯尼斯·佩恩指出:“AI系统在模拟空战中的表现已经优于经验丰富的空军飞行员。”今年2月,五角大楼的创新部门国防部高级研究计划局对六架无人机组成的机群进行了最新测试。这个机群在切断了与人类的联系后仍能在“高威胁”环境中协同作战。

For all that, most such systems embody intelligence that is narrow and brittle—good at one task in a well-defined environment, but liable to fail badly in unfamiliar settings. So existing autonomous weapons are comprised of either loitering missiles that smash into radars or quick-firing guns that defend ships and bases. Useful, but not revolutionary—and neither requires the fancy machine-learning techniques pioneered in recent years.

尽管如此,大多数此类系统所展现的智能都是狭隘和脆弱的一一能在明确定义的环境中完成某项任务,但在不熟悉的环境中容易一败涂地。因此,现有的自主武器要么是打击雷达的巡航导弹,要么是用于护卫船只和基地的速射炮。有用,但并非革命性的——近年来开创的复杂花哨的机器学习技术对它们来说也非必需。

Enhance. Enhance. Enhance

强化,强化,再强化

It would be a mistake to think that AI is useful only for battlefield drudgery. Robots, killer or otherwise, must act on what they see. But for many military platforms, like spy planes and satellites, the point is to beam back raw data that might be turned into useful intelligence. There is now more of that than ever before—in 2011 alone, the most recent year for which there are data, America’s 11,000-or-so drones sent back over 327,000 hours (37 years) of footage.

如果认为AI仅能运用于战场上的苦差事就错了。不管是否要杀戮,机器人必须见机行事。但对于许多侦察机和卫星之类的军事平台而言,关键在于发回可能转化为有用情报的原始数据。现在这类数据比以往任何时候都多一一仅在2011年,也就是有此类数据的最近年份,美国约1.1万架无人机就发回了超过 32.7万小时(37年)的视频。

Most of that has lain unwatched. Luckily, the second major application for AI in the armed forces will be in processing data. In lab-based tests, algorithms surpassed human performance in image classification by 2015 and nearly doubled their performance in a tougher task, object segmentation, which involves picking out multiple objects from single images, between 2015 and 2018, according to Stanford University’s annual index of AI progress. Computer vision is far from perfect and can be exploited in ways that would not fool a human observer. In one study, altering 0.04% of the pixels in an image of a panda—imperceptible to humans—caused the system to see a gibbon instead.

这些视频大部分都闲置着没人看。幸好,AI在军队里的第二项主要应用将是处理数据。根据斯坦福大学每年发布的AI进展指数,在实验室测试中,算法在图像分类方面的表现在2015年超越了人类;在更困难的对象分割(从单张图像中挑选出多个对象)任务方面,从2015年到2018年,算法的表现几乎提高了一倍。计算机视觉远未完善,可以用愚弄不了人类观察者的方式干扰它们。在一项研究中,仅改变了熊猫图像中0.04%的像素一一是人类觉察不到的变化一一就让系统把图像认成了长臂猿。

Those weaknesses notwithstanding, by February 2017 the Pentagon itself concluded that deep-learning algorithms “can perform at near-human levels”. So it established the “Algorithmic Warfare” team, known as Project Maven, which uses deep learning and other techniques to identify objects and suspicious actions, initially in footage from the war against Islamic State and now more widely. The aim is to produce “actionable” intelligence—the sort that often ends with bombs falling or special forces kicking in doors.

尽管存在这些不足,2017年2月,五角大楼自己得出的结论是深度学习算法“可以以接近人类的水平完成任务”。因此,它成立了被称作“行家项目”的“算法战争"团队,利用深度学习等技术来识别对象和可疑行为。最初分析的是对伊斯兰国组织作战的视频,现在的分析范围更广泛。其目的是产生“可执行的”情报,往往会促使发动轰炸或派特种部队突袭行动。

An insider with knowledge of Project Maven says that the benefits to analysts—in terms of time savings and new insights—remain marginal for now. Wide-angle cameras that can see across entire cities throw up large numbers of false positives, for instance. “But the nature of these systems is highly iterative,” he says. Progress is rapid and Project Maven is just the tip of the iceberg.

一位了解该项目的内部人士表示,目前情报分析师在节省时间和获得新洞见方面得到的帮助仍然微不足道。例如,监控整个城市的广角摄像头会产生大量误报。“但这些系统本质上是高度迭代的。”他说。进展很快,而行家项目只是冰山一角。

Earth-i, a British company, can apply machine-learning algorithms from a range of satellites to identify different variants of military aircraft across dozens of bases with over 98% accuracy (see main picture), according to Sean Corbett, a retired air vice-marshal in the Royal Air Force (RAF) who now works for the firm. “The clever bit”, he says, “is then developing methods to automatically identify what is normal and what is not normal.” By watching bases over time, the software can distinguish routine deployments from irregular movements, alerting analysts to significant changes.

英国公司Earth-i可以在一系列卫星上应用机器学习算法,以超过98%的准确度识别数十个军事基地中不同型号的军用飞机(见主图),目前在该公司任职的英国皇家空军退役少将肖恩·科比特表示。“高明的部分就是接下来正在开发的能自动识别正常情况和非正常情况的方法。”他说。观察基地一段时间后,软件可以区分常规部署和非常规调动,提醒分析师注意重大变化。

Algorithms, of course, are omnivorous and can be fed any sort of data, not just images. “Bulk data combined with modern analytics make the modern world transparent,” noted Sir Alex Younger, the head of MI6, Britain’s spy agency, in December. In 2012 leaked documents from the NSA, America’s signals-intelligence agency, described a programme (reassuringly called Skynet), which applied machine learning to Pakistani mobile-phone data in order to pick out individuals who might be couriers for terrorist groups. Who, for instance, had travelled from Lahore to the border town of Peshawar in the past month—and turned off or swapped their handset more often than usual? “It’s beginning to shift intelligence from the old world, where commanders asked a question and intelligence agencies used collection assets to find the answer, to a world where answers are in...the cloud,” says Sir Richard Barrons, a retired general who commanded Britain’s joint forces until 2016.

当然,算法不挑食,任何类型的数据都可以输入,而不限于图像。英国间谍机构军情六处的丞责人亚历克斯·扬格去年12月表示:“大量数据加上现代分析,会让现代世界变得透明。”2012年,美国信号情报机构国家安全局泄露的文件描述了一项计划(它有一个令人安心的名字——“天网"),该计划用机器学习分析巴基斯坦的移动电话数据,以找出可能是恐怖组织信使的人。举例来说,过去一个月谁曾从拉合尔去过边境城镇白沙瓦,而且比往常更频繁地关闭或更换手机?2016年前任英国联合部队司令的退役将军理查德·巴伦说:“情报工作的面貌已经开始转变。从前,指挥官提出问题,情报机构通过情报人员来找答案,现在,答案都在......云上。”

Indeed, the data in question need not always come from an enemy. JAIC’s first project was neither a weapon nor a spying tool, but a collaboration with special forces to predict engine failures in their Black Hawk helicopters. The first version of the algorithm was delivered in April. Air-force tests on command-and-control planes and transporters showed that such predictive maintenance could reduce unscheduled work by almost a third, which might allow big cuts in the $78bn that the Pentagon currently spends on maintenance.

实际上,所涉数据并不总是来自敌人。JAIC的第一个项目既非武器也非间谍工具,而是与特种部队合作预测黑鹰直升机的发动机何时发生故障。其算法的第一个版本于4月交付。空军针对指挥控制型飞机和运输机的测试表明,这种预见性维护可以减少近三分之一的计划外工作量,这也许能大幅减少五角大楼目前780亿美元的维护支出。

Coup d’ AI

AI上位

The point of processing information, of course, is to act on it. And the third way AI will change warfare is by seeping into military decision-making from the lowly platoon to national headquarters. Northern Arrow, a tool built by UNIQAI, an Israeli AI firm, is one of many products on the market that helps commanders plan missions by crunching large volumes of data on variables such as enemy positions, weapon ranges, terrain and weather—a process that would normally take 12 to 24 hours for soldiers the old-fashioned way by poring over maps and charts. It is fed with data from books and manuals—say, on tank speeds at different elevations—and also from interviews with experienced commanders. The algorithm then serves up options to harried decision-makers, along with an explanation of why each was chosen.

处理信息当然是为了据此采取行动。AI将改变战争的第三种方式就是参与从基层连队到国家总部的军事决策。以色列AI公司UNIQAI打造的工具Northern Arrow是市场上的众多产品之一,通过处理大量有关敌方阵地、武器射程、地形和天气等变量的数据来协助指挥官制定任务计划。这一过程如果换做士兵研究地图和图表的老办法,通常耗时12至24小时。 Northern Arrow的数据来源是书籍和手册(例如坦克在不同海拔的速度)以及对经验丰富的指挥官的访谈。然后,算法为焦头烂额的决策者提供不同的方案选项,并解释每个方案背后的逻辑。

These “expert system” platforms, such as Northern Arrow and America’s similar CADET software, can work far quicker than human minds—two minutes for CADET compared with 16 person-hours for humans, in one test—but they tend to employ rule-following techniques that are algorithmically straightforward. By historical standards this would be considered AI, but most use deterministic methods, which means that the same inputs will always produce the same outputs. This would be familiar to the soldiers who used the outputs of ENIAC, the world’s first electronic general-purpose computer, which generated artillery firing tables in 1945.

Northern Arrow和美国类似的CADET软件等“专家系统”平台的效率比人脑高得多——在一次测试中,CADET在两分钟内完成了人类需要16个工时的工作。不过这些平台一般采用在算法上直截了当的遵循规则的技术。从历史标准来看这可以算作AI,但大多用的是确定性方法,也就是说相同的输入将始终产生相同的输出。当年使用“埃尼阿克”(ENIAC, 电子数字积分计算机)的输出数据的士兵对此并不 陌生。埃尼阿克是世界上第一台通用计算机,在 1945年生成了炮弹弹道射表。

In the real world, randomness often gets in the way of making precise predictions, so many modern AI systems combine rule-following with added randomness as a stepping stone to more complex planning. DARPA’s Real-time Adversarial Intelligence and Decision-making (RAID) software aims to predict the goals, movements and even the possible emotions of enemy forces five hours into the future. The system relies on a type of game theory that shrinks down problems into smaller games, reducing the computational power required to solve them.

在现实世界中,随机性经常会妨碍精确预测,因此许多现代AI系统将遵循规则与额外随机性结合起来,以辅助更复杂的规划。DARPA的“实时对抗智能与决策”(RAID)软件力争预测未来五小时内敌人的目标、动向、甚至可能的情绪状态。这个系统依赖的是一种博弈理论,把问题缩小为更小的博弈,降低了解决问题所需的计算能力。

In early tests between 2004 and 2008, RAID performed with greater accuracy and speed than human planners. In simulated two-hour battles in Baghdad, human teams were pitted against either RAID or other humans; they could tell them apart less than half the time. The retired colonels drafted to simulate Iraqi insurgents “got so scared” of the software, notes Boris Stilman, one of its designers, that “they stopped talking to each other and used hand signals instead”. RAID is now being developed for army use.

在2004年至2008年的早期测试中,RAID的准确度和速度都高于人类作战计划人员。在模拟的两小时巴格达战斗中,人类团队与RAID或其他人类展开对抗。人类只在不到一半的时间里分辨出了对手是人还是计算机。征召来模拟伊拉克叛乱分子的退役上校们“很怕”这个软件,RAID的设计师之一鲍里斯·斯蒂尔曼说,“他们不再用言语交谈,而是改用手势”。研发人员正在开发RAID供军队使用。

The latest deep-learning systems can be the most enigmatic of all. In March 2016, AlphaGo, a deep-learning algorithm built by DeepMind, beat one of the world’s best players in Go, an ancient Chinese strategy game. In the process it played several highly creative moves that confounded experts. The very next month, China’s Academy of Military Science held a workshop on the implications of the match. “For Chinese military strategists, among the lessons learned from AlphaGo’s victories was the fact that an AI could create tactics and stratagems superior to those of a human player in a game that can be compared to a war-game,” wrote Elsa Kania, an expert on Chinese military innovation.

最新的深度学习系统可能是最神秘的。2016年3月,由DeepMind构建的深度学习算法AlphaGo击败了一位世界顶尖围棋手。在对局中,它走的几手棋极具创意,让一众高手困惑不已。一个月之后,中国军事科学院就此次人机大赛的影响举办了一场研讨会。研究中国军事创新的专家埃尔莎·卡尼亚写道:“中国的军事战略家从AlphaGo的胜利中汲取的经验教训之一是,AI可以在堪比作战游戏的棋盘游戏中创造出优于人类玩家的战术和策略。”

Shall we play a game?

玩个游戏吧

In December 2018 another of DeepMind’s programs, AlphaStar, trounced one of the world’s strongest players in StarCraft II, a video game played in real-time, rather than turn-by-turn, with information hidden from players and with many more degrees of freedom (potential moves) than Go. Many officers hope that such game-playing aptitude might eventually translate into a flair for inventive and artful manoeuvres of the sort celebrated in military history. Michael Brown, director of the Defence Innovation Unit, a Pentagon body tasked with tapping commercial technology, says that AI-enabled “strategic reasoning” is one of his organisation’s priorities.

去年12月,DeepMind的另一个程序AlphaStar击败了《星际争霸II》的全球最强玩家之一。《星际争霸II》是一款即时电子游戏,而不是一步接一步地进行,信息对玩家隐藏,比围棋的自由度更高(潜在的招数也就更多)。许多军官希望这种玩游戏的能力最终可以转化为军事历史上推崇的那种创新而巧妙的操纵才能。五角大楼负责利用商业技术的国防创新部门的主管迈克尔·布朗表示,AI辅助的“战略推理”是其部门的工作重点之一。

But if algorithms that surpass human creativity also elude human understanding, they raise problems of law, ethics and trust. The laws of war require a series of judgments about concepts such as proportionality (between civilian harm and military advantage) and necessity. Software that cannot explain why a target was chosen probably cannot abide by those laws. Even if it can, humans might mistrust a decision aid that could outwardly resemble a Magic 8-Ball.

但是,如果超越人类创造力的算法同时也超出了人类的理解力,就会引发法律、伦理和信任的问题。战争法要求就比例原则(平民遭受的伤害和军事优势之间的权衡)和必要性等概念做出一系列判断。 如果软件无法解释为何选择某个目标,可能就无法遵守这些法律。即使它可以解释,人类可能也不信任一个看起来好像“魔力八号球”那般随机的决策辅助工具。

“What do we do when AI is applied to military strategy and has calculated the probabilistic inferences of multiple interactions many moves beyond that which we can consider,” asks wing-commander Keith Dear, an RAF intelligence officer, “and recommends a course of action that we don’t understand?” He gives the example of an AI that might propose funding an opera in Baku in response to a Russian military incursion in Moldova—a surreal manoeuvre liable to baffle one’s own forces, let alone the enemy. Yet it might result from the AI grasping a political chain of events that would not be immediately perceptible to commanders.

“AI应用于军事战略后,如果计算出了多重交互的概率推论,大大超出了人类可以考虑的范围,并推荐了一个我们无法理解的行动方案,这时我们该怎么做?”英国皇家空军情报官凯斯·迪尔中校问道。他举了个例子:为了回应俄罗斯对摩尔多瓦的军事入侵,AI可能会提议在巴库资助一部歌剧——敌人自不必说,自己人可能也会为这种离奇的策略挠头。然而,AI提出这样的建议,可能是因为它掌握了指挥官无法立即察觉到的一系列政治事件动向。

Even so, he predicts that humans will accept the trade-off between inscrutability and efficiency. “Even with the limitations of today’s technology, an AI might support, if not take over, decision-making in real-world warfighting” by using a “massive near-real-time simulation”.

即便如此,他预测人类也将会接受在难以理解和决策效率之间做取舍。通过“大规模近实时模拟”,“即使现在仍有技术局限,AI也可能支持甚至接手实战决策”。

That is not as far-fetched as it sounds. Sir Richard Barrons points out that Britain’s defence ministry is already purchasing a technology demonstrator for a cloud-based virtual replication of a complex operating environment—known as a single synthetic environment—essentially a military version of the software that powers large-scale online video games such as “Fortnite”. It is built by Improbable, a gaming company, and CAE, known for its flight simulators, using open standards, so everything from secret intelligence to real-time weather data can be plugged in. “It will revolutionise how command and control is done,” says Sir Richard, as long as there are plentiful data, networks to move it and cloud computing to process it. That would allow a “single synthetic command tool from the national security council down to the tactical commander”.

这并不像听上去那么难以置信。理查德·巴伦指出,英国国防部已经购买了一款技术演示软件,用于对复杂行动环境(称为单一综合环境)进行基于云的虚拟复制,实质上是驱动《堡垒之夜》等大型在线游戏的软件的军用版。它由游戏公司Improbable和以飞行模拟器而闻名的加拿大航空电子设备公司(CAE)设计,使用开放标准,因此可以接入从秘密情报到实时天气数据的所有信息。只要有丰富的数据、传输数据的网络和处理数据的云计算能力,“它将彻底改变指挥和控制的方式。”理查德说。这样,“上至国家安全委员会,下至战术指挥官,都可以使用单一的综合指挥工具”

Automatic without the people?

完全自主,无人参与?

Western governments insist that humans will be “on the loop”, supervising things. But even many of their own officers are not convinced. “It seems likely humans will be increasingly both out of the loop and off the team in decision-making from tactical to strategic,” says Commander Dear. The expectation that combat will speed up “beyond the capabilities of human cognition” recurs in Chinese writing, too, says Ms Kania. The result would not only be autonomous weapons, but an automated battlefield. At the outset of a war, interconnected AI systems would pick out targets, from missile launchers to aircraft-carriers, and choreograph rapid and precise strikes to destroy them in the most efficient order.

西方政府坚持要求人在“回路上”,负责监督。但即使是很多西方国家的军官对此都没有信心。“从战术决策到战略决策,人类似乎将越来越多地被挤出回路之外,排除出决策团队。”迪尔中校说。卡尼亚说,在中国人的著述中,战斗速度将“超越人类认知能力”的预期屡屡出现。结果就是不仅武器将变得自主,战场也会变得自动化。战争开始时,互联的AI系统将挑选目标,可能是导弹发射器,也可能是航空母舰,并设计快速和精准的打击方案,以最高效的顺序摧毁目标。

The wider consequences of that remain unclear. The prospect of accurate and rapid strikes “could erode stability by increasing the perceived risk of surprise attack”, writes Zachary Davis in a recent paper for the Lawrence Livermore National Laboratory. But AI might equally help defenders parry such blows, by identifying the telltale signs of an impending strike. Or, like America’s sensor-scattering spree in the Vietnamese jungle in the 1960s, such schemes could wind up as expensive and ill-conceived failures. Yet no power wants to risk falling behind its rivals. And here, politics, not just technology, may have an impact.

更大范围内的影响仍不得而知。扎克里·戴维斯在最近为劳伦斯·利弗莫尔国家实验室撰写的一篇报告中写道,可以实施快速精准的打击“可能会破坏局势稳定,因为可以想见的是,突袭的风险将增加”。但通过识别即将遭受打击的迹象,AI可能同样可以帮助防御方抵御这样的打击。AI方案也可能代价高昂但计划不周,最后沦为败笔,就像上世纪60年代美国在越南丛林中大量投放传感器的做法那样。然而,没有任何国家愿意冒险落后于竞争对手。而在这一方面,可能会产生影响的不仅仅是技术,还有政治。

The Pentagon’s spending on AI is a fraction of the $20bn-30bn that was spent by large technology firms in 2016. Although many American companies are happy to take defence dollars—Amazon and Microsoft are nearing a $10bn cloud-computing contract with the Pentagon—others are more skittish. In June 2018 Google said it would allow its $9m contract for work on Project Maven to lapse this year, after 4,000 employees protested the company’s involvement in “warfare technology”.

大型科技公司2016年在AI上的支出为200亿至300亿美元,相比之下,五角大楼在这方面的投入是小巫见大巫。虽然许多美国公司乐于发军火财——亚马逊和微软即将与五角大楼达成100亿美元的云计算合同,但其他公司的态度要更加游移不定。2018年6月,谷歌表示其与行家项目的900万美元合同在当年到期后将不再续约,在此之前有4000名谷歌员工抗议公司参与发展“战争技术”。

In China, on the other hand, firms can be easily pressed into the service of the state and privacy laws are a minor encumbrance. “If data is the fuel of AI, then China may have a structural advantage over the rest of the world,” warned Robert Work, a former US deputy secretary of defence, in June. Whether civilian data can fuel military algorithms is not clear, but the question plays on the minds of military leaders. JAIC director General Jack Shanahan expressed his concerns on August 30th: “What I don’t want to see is a future where our potential adversaries have a fully AI-enabled force and we do not.”

而在中国,企业很容易受到压力而为政府服务,隐私法规也只是个小障碍。美国前国防部副部长罗伯特·沃克在6月警告说:“如果说数据驱动了AI,那么中国可能比世界其余地区都具有结构性优势。”民用数据能否驱动军事还很难说,但这个问题盘旋在各国军事领导人的头脑中。JAIC的负责人杰克·沙纳汉中将在8月30日表达了他的担忧:“我不希望看到未来我们的潜在对手拥有全AI赋能的军队,我们却没有。”

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