For some, the spread of artificial intelligence and robotics poses a threat to our privacy, our jobs – even our safety, as more and more tasks are handed over to silicon-based brains.
But even the most vocal critics highlight the potential good that AI and automated systems could do for humanity. As part of BBC Future Now’s Grand Challenges, a panel of experts recently described how they saw our world changing as the machines we use grow smarter.
然而，即便是最直言不讳的批评者，也不得不承认人工智能（AI）和自动化系统为人类带来的诸多潜在好处。作为BBC"Future Now"专栏大挑战系列（Grand Challenges）的一部分，一组专家为我们详细描述了随着我们所使用的机器变得越来越智能，我们周围的世界正在如何发生变化的图景。
Now, in our Grand Ideas series, BBC Future Now has sought out projects where advanced AI and automation is already beginning to tackle some of the world’s knottiest, and dangerous, problems, from illnesses to violence.
今天的"大构想"（Grand Ideas）系列中，BBC"Future Now"专栏将对已经开始应用于解决世界上最棘手、最危险的一些问题的尖端AI和自动化技术进行了盘点，这些问题包括了疾病防治到应对暴力。
“We should view AI not as something competing with us, but as something that can amplify our own capabilities,” says Takeo Kanade, a professor of robotics at Carnegie Mellon University. This is because AI has a tolerance for tedium, plus an ability to spot patterns – an ability that’s far beyond anything humans are capable of.
卡内基梅隆大学机器人教授金出武雄（Takeo Kanade）说:"我们不应该把 AI 视为与人类竞争的东西，而应该看作是可以增强我们自身能力的东西。"这是因为 AI 不仅能做好单调乏味的工作，还能够识别出模式，这种能力甚至远远超过了人类。
And it might what will help keep us safe in the 21st Century.
它可能会在 21 世纪帮助保护我们的安全。
Combating infectious diseases
For billions of people around the world, the buzz of a mosquito past their ear can mean far more than an irritating bite – it can be a harbinger of disease and even death. One species in particular – Aedes aegypti – has spread from Africa into almost all tropical and subtropical regions, carrying Dengue fever, yellow fever, Zika and chikungunya(a virus that causes crippling joint pains) with it. Dengue alone infects an estimated 390 million people every year in 128 countries.
对于全球数十亿人来说，在耳边嗡嗡作响的蚊子不仅会叮咬人们带来令人恼怒的疼或痒，它们还可能带来疾病甚至致命。特别是已经从非洲传播到几乎所有热带和亚热带地区的埃及伊蚊（Aedesaegypti），它们携带登革热（Dengue fever）、黄热病、寨卡（Zika）以及基孔肯雅热（chikungunya，一种导致严重关节痛的病毒）等病毒。在全球 128 个国家和地区，每年仅登革热就会感染 3.9 亿人。
“This mosquito is a tiny demon,” says Rainier Mallol, a computer engineer from the Dominican Republic, a Zika hotspot. Together with Dhesi Raja, a medical doctor from Malaysia (another at-risk country for the virus), the pair have created AI algorithms that predict where outbreaks are most likely to happen.
来自多米尼加共和国的计算机工程师雷尼尔·马洛尔（Rainier Mallol）说："这些蚊子就像小恶魔。"多米尼加共和国是寨卡病毒爆发热点地区。与来自马来西亚（另一个热点）的医学博士达西·拉贾（Dhesi Raja）一起，马洛尔两人开发出一套 AI 算法，能够预测疫情最有可能发生的地方。
Their Artificial Intelligence in Medical Epidemiology (Aime) is a system that combines the time and location of each new dengue casefrom reports filed by local hospitals with 274 other variable factors - such as wind direction, humidity, temperature, population density, housing type. “These are all factors that determine how the mosquito will spread,” explains Mallol.
他们的医学流行病学（Aime） AI 系统可以将所有当地医院新报告的登革热病例出现的时间和地点与包括风向、湿度、温度、人口密度、住房类型等在内的 274 个可变因素结合起来。"这些因素都是确定蚊子如何传播的因素，" 马洛尔解释道。
Trials in Malaysia and Brazil have so far shown it can predict outbreaks with an accuracy of around 88% up to three months in advance. The system can also help to pinpoint the epicentre of an outbreak to within 400 metres, allowing public health officials to intervene early with insecticides and bite protection for locals.
到目前为止，在马来西亚和巴西的试点表明，这套系统可以提前三个月准确预测疫情爆发，准确率达到 88% 左右。此外，该系统还可以帮助查明疫情中心及其 400 米范围内的情况，从而使公共卫生官员能够及早利用杀虫剂进行干预，以防蚊虫对当地居民进行叮咬。
Aime is also being extended to predict Zika and chikungunya outbreaks. And huge tech companies are pursuing their own vision of this grand idea: for example, Microsoft’s Project Premonition uses autonomous drones to locate mosquito hotspots and use robotic carbon dioxide and light traps to collect some of the insects. DNA from the mosquitoes, and the animals they have bitten, can then be analysed using machine learning algorithms - software that learns to recognise patterns from large amounts of data, getting better and better each time - to search for pathogens.
Aime 系统也被用于帮助预测寨卡和基孔肯亚热病毒疫情爆发。大型科技公司也在追求自己的宏伟构想：例如，微软的 Project Premonition 项目使用无人机定位蚊子的热点地区，并利用机器人二氧化碳和光捕捉器来收集蚊子样本，包括蚊子以及它们咬过的动物的 DNA，然后通过机器学习算法进行分析，从而找到病原体。这些算法能够从大量的数据中识别出模式，而且会变得越来越精确和强大。
Tackling gun violence
There were 15,000 deaths due to firearms in the US last year, and the country has the highest rate of gun-related violence in the developed world. To address what many see as an unrelenting tide of shootings and gun-related crime, a number of cities are now turning to technology to find a solution.
去年，美国有 15,000 人死于枪械暴力，美国也是发达国家中枪械暴力发生率最高的国家。为了解决持续不断的枪击和枪械犯罪问题，许多城市正在试图通过科技寻找解决办法。
An automated system that listens for the sounds of gunfire with arrays of sensors can be used to pinpoint where gunshots came from and alert the authorities within 45 seconds of the trigger being pulled. The ShotSpotter system uses 15 to 20 acoustic sensors per square mile to detect the distinctive “pop” of a gunshot, using the time it takes to reach each sensor to and uses algorithms to reveal the location to within 25 metres.
有一种自动化系统可以用传感器阵列监听枪声，然后精确定位枪声所在的位置，并在 45 秒内向相关机构发出警报。这种名为 ShotSpotter 系统需要配备大量声音传感器以探测枪械独特的声响，利用其到达每个传感器的时间，通过算法来定位枪击位置，误差在25米之内。
Machine learning technology is used to confirm the sound is a gunshot and count the number of them, revealing whether police might be dealing with a lone shooter or multiple perpetrators and if they are using automatic weapons.
There are 90 cities - many in the US but some in South Africa and South America - now using ShotSpotter with others in discussions. Smaller systems have also been deployed on nine college campuses in the US in response to recent campus shootings while the US Secret Service has installed it at the White House.
目前有 90 个城市（多在美国，部分在南非和南美）正在使用 ShotSpotter 系统。美国 9 所大学校园也部署了较小的 ShotSpotter 系统，以应对最近频发的校园枪击事件。而且美国特勤局已将其安装在白宫内。
But Ralph Clark, chief executive of ShotSpotter, believes the system could in the future be used for more than simply responding to incidents.
但 ShotSpotter 公司首席执行官拉尔夫·克拉克（Ralph Clark）认为，该系统未来的用途不仅仅是简单地应对突发事件。
“We are keen to see how our data can inform more predictive policing opportunities,” he says. “Machine learning can combine it with weather, traffic data, property crime data to inform the deployment of police patrols more precisely.”
Keeping famine from the door
Around 800 million people worldwide rely upon cassava roots as their main source of carbohydrate. The starchy vegetable, which is similar to yam, is often eaten much like potatoes, but can also be ground into a flour for making bread and cakes. Its ability to grow where other crops do not has turned it into sixth-most-produced food plant in the world. But the woody shrub is also highly vulnerable to disease and pests, which can devastate entire fields of the vegetable.
全世界目前大约有 8 亿人依靠木薯根作为主要碳水化合物（为人体提供热能的主要营养素）的来源。这种淀粉类蔬菜与山药相似，经常被人像土豆那样食用，但也可以磨成粉做面包和蛋糕。它能在其他农作物没法生长的地方种植，这使它成为世界上第六大粮食作物。但是这种木本灌木极易受到疾病和害虫的侵害，可以让整片田地都颗粒无收。
Researchers at Makerere University in Kampala, Uganda, have teamed up with plant disease experts to develop an automated system aimed at combating cassava diseases. The Mcrops project allows local farmers to take pictures of their plants using cheap smartphones and uses computer vision that has been trained to spot the signs of the four main diseases that are responsible for ravaging cassava crops.
“Some of these diseases are really hard to recognise and require different action,” explains Ernest Mwebaze, a computer technology researcher leading the project. “We are giving the farmers an expert in their pocket so they know if they need to spray their crops or rip them up and plant something else.”
The system can now diagnose cassava diseases with 88% accuracy. Normally farmers have to call the government-employed experts to visit their farms to identify diseases, which can take days and even weeks, allowing pests and blights to spread.
MCrops also uses the uploaded images to look for patterns in disease outbreaks, something that could allow officials to halt epidemics that can lead to famine. Mwebaze and his colleagues are hoping to use technology to also look at banana diseases and to automate the detection of other crop pests.
Fighting cancer and sight loss
Cancer causes more than 8.8 million deaths worldwide and 14 million people are diagnosed with some form of cancer every year. Yet catching cancers as early as possible can greatly improve a patient’s chances of survival and reduce the risk of the disease recurring. Screening is one of the key ways to spot cancers early, but trawling through scans and other test results is laborious.
全世界每年有 880 万人死于癌症，另有 1400 万人被诊断出患有某种癌症。尽早发现癌症能够极大地提高患者的生存机会，并降低复发的风险。筛查是早期发现癌症的关键方法之一，但通过扫描和其他方法检测结果费时费力。
But both DeepMind, owned by Google’s parent company Alphabet, and IBM have been applying their AI technology to this problem. DeepMind has teamed up with UK National Health Service doctors at University College London Hospitals to train its AI help plan treatments for cancer by identifying areas of healthy tissue from tumours in head and neck scans. It is also working with Moorfields Eye Hospital in London to identify the early signs of sight loss in eye scans.
不过，谷歌母公司 Alphabet 旗下 AI 子公司 DeepMind 和 IBM 都在应用自己的 AI 技术来解决这个问题。DeepMind 与伦敦大学学院医院的英国国家卫生署（National Health Service）医生合作，通过识别头部和颈部肿瘤中的健康组织区域，来训练其 AI 帮助制定治疗癌症的方法。此外，该公司还与伦敦 Moorfields 眼科医院合作，在眼部扫描中识别失明的早期迹象。
“Our algorithms are able to interpret visual information in the scans,” says Dominic King, clinical lead at DeepMind Health. “The system learns how to identify potential issues and how to recommend the right course of action to a clinician. It’s too soon for us to comment on the results yet but the early signs are very encouraging.”
DeepMind Health 的临床主管多米尼克·金（Dominic King）说："我们的算法能够在扫描中解释视觉信息。这个系统学会如何识别潜在的问题，以及如何向临床医生推荐正确的行动。现在我们对结果发表评论还为时过早，但早期的迹象非常令人鼓舞。"
King says AI technology can help doctors identify cases faster by sifting through scan images and prioritising those that clinicians should look at most urgently.
IBM also recently announced its Watson AI can analyse images and assess patient notes to accurately identify tumours in up to 96% of cases. The system is being trialed by doctors at 55 hospitals around the world to help diagnose breast, lung, colorectal, cervical, ovarian, gastric and prostate cancers.
IBM 最近宣布，Watson AI 可以分析图像，并评估病人的诊断书，从而准确地识别出肿瘤病例，准确率高达96%。世界各地 55 家医院的医生正在对该系统进行测试，以帮助诊断乳腺癌、肺癌、结肠癌、宫颈癌、卵巢癌、胃癌以及前列腺癌。
Keeping the lights switched on
As fresh debate rages as to whether climate change may have caused two back-to-back catastrophic hurricanes of historic proportions in the US, how can AI maximise our use of clean, renewable energy to prevent further damage that has questionable effects on our climate patterns?
People around the world are increasingly reliant upon renewables to combat climate change and the pollution caused by fossil fuels, and the task of balancing power supplies with such intermittent sources is getting harder. The spread of smart meters – digital energy monitors that automatically record usage – is also providing more data than ever about how and when consumers use energy. The EU alone plans to have 500 million smart metres in homes by 2020.
世界各地的人们越来越依赖可再生能源来应对气候变化和化石燃料造成的污染，而平衡电力供应的任务变得越来越艰难。智能电表（如可自动记录使用情况的数字能源监视器）的普及，也提供了比以往任何时候都更多的数据，用来说明消费者使用能源的方式和时间。仅欧盟就计划到 2020 年在家庭中安装 5 亿个智能电表。
“Managing all these assets is impossible to do for a human controller, especially as response times required are often in the order of a few seconds,” says Valentin Robu, an assistant professor of smart systems at Heriot Watt University in Edinburgh. He has been working with UK-based start-up Upside Energy to develop new ways of managing electricity grids.
爱丁堡赫里瓦特大学（Heriot Watt University）智能系统助理教授瓦伦丁·罗布（Valentin Robu）表示："对人类操作者来说，管理所有这些事情是不可能的，尤其考虑到这些事情要求的反应时间通常只有几秒钟。"罗布一直在与英国的初创公司 Upside Energy 合作，开发管理电网的新方法。
They are building machine learning algorithms to monitor energy production and demand in real time. What does this mean? That energy can be stored during quiet times and then released at busy times, like first thing in the morning when everyone makes their morning coffee. As electric cars and battery units in people’s homes become more common, the technology can use these to store energy and smooth out the bumps in renewable electricity supply.
Robu also says AI could be used at an even more basic level by helping to reduce the demand that our devices put on the grid. For example, refrigerators could be controlled remotely by AI so they enter chill cycles only at times when demand on the grid is low.
罗布还表示，AI 可以在更基础的层面上使用，以帮助减少这些设备对电网的需求。例如，电冰箱可以通过 AI 远程控制，只有在电网需求较低的时候它们才会开启制冷功能。