人工智能改变商业的25种方式
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是时候靠谱地谈一谈人工智能技术的未来了。之所以说这话，是因为坊间对人工智能技术的探讨往往很不靠谱。很多人谈到人工智能，往往会走向两个相反的极端：要么精准地预测人工智能未来几年会催生或摧毁多少个就业机会，要么断言人工智能会将我们的世界变成天堂（或者地狱）。因此，在本文中，我们决定靠谱地讨论人工智能会怎样改变商业的面貌。在这个过程中，我们会尽量减少戏剧化的猜想。 “人工智能焦虑症”的一大症状，就是担心人工智能会造成大面积失业。然而实际上，未来会发生什么，没有人知道，也不可能有人知道。全球有千百万企业家和经理人在应用着各种快速革新的技术，他们会创造出怎样的奇迹，我们永远也猜想不到。举个例子，美国邮政部长亚瑟·萨默菲尔德1959年曾自信地预言称，过不了多久，邮件就会用导弹来运送了，因为导弹是当时人们能想象出来的最神奇的技术。随着二战后经济的发展，邮政部门要寄送的信件越来越多，在当时看来，邮政工人无疑是一份好工作。当时萨默菲尔德绝对想不到，有一天信件将不再被写在纸上——虽然当时电子邮件、短信和无线通信网络的雏形已经诞生，或至少已在研发阶段。在人工智能的问题上，我们可能也会犯同样的认知错误。 第二个重要事实，是人工智能的最终应用情况，很大程度上将由市场的力量决定。很多人以为人工智能将把世界变成一个乌托邦式的理想国，他们都忽视了市场这只“看不见的手”的作用。美国无线电公司老板大卫·萨诺夫曾经预言，在彩色电视普及后，大家就可以在家里欣赏艺术作品了。这听起来当然是极好的，但是没人想把它用在这么高雅的用途上。人工智能也是一样的，它会被广大企业和消费者用在数不清的实际用途上，其中大多数用途谈不上好也谈不上不好，但其累积效果是无法预见的。在我们思考人工智能的未来时，关键是不要把自己放在一个道德的高地上，而是要像现实世界的每一个趋利避害的人（包括好人和坏人）一样思考。 不过本文不会讨论坏人的例子。我们将在文中讨论25个人工智能的有益案例，其中有些案例非常具有启发性——而且它们都是真实的。 人工智能如何改变你的工作方式 让所有人都说同一种语言 从《神秘博士》（Doctor Who）和《星际迷航》（Star Trek）开始，科幻作品中就出现了能自动翻译语言的机器，有了它，人类和外星人就算不学习对方的语言也能无障碍地交流。现在，地球上的一些公司已经在制造这种设备了。谷歌近日发布的Pixel Buds智能耳机就是一例，有了它，美国公司的高管就可以给说葡萄牙语的同行打电话，谈谈跨国合作的事儿；跨国企业的员工可以更顺畅地与其他国家的同事沟通，哪怕他们并不会说同一种语言；销售人员可以给一个陌生地区的客户打推销电话，不必担心对方听不懂自己在说什么，说不定这一通电话就是公司咸鱼翻身的机会。虽然很多跨国公司都将英语定为公司的官方语言，不过基于人工智能的同声传译技术却使非英语母语者可以继续说自己的语言，保持自己的文化特点——在全球化的时代，这显然是一个优点。 读心术 语音控制是一项挺“酷”的技术。然而无论是亚马逊的Alexa、苹果的Siri还是微软的Cortana，你在公开场合跟它对话，都显得特别尴尬，也比较打扰别人。然而麻省理工学院的研究人员已经发明出了一种名叫AlterEgo的神奇装置，它是一种非侵入性的可穿戴设备，它可以在你开口说话前就知道你要说什么。AlterEgo可以在几秒钟内回答很多问题，也可以发送私人信息，或者内部记录信息流以留待稍后处理，这些都无需任何外部可观察到的操作。当然，AlterEgo并非真的有读心术，不过它可以解读人体下颌骨的电脉冲，这个位置正是人体的发声器官，从而使AlterEgo能做到“声未发而先知”。目前，麻省理工学院的研究人员仍在继续收集数据，并对该系统进行训练。以后，该系统可以作为高噪音环境下的一个沟通平台使用，也可以用于患有语言障碍的人士。不过AlterEgo虽然具有大大加速书写、计划和沟通的能力，但人类终究还是要花不少时间阅读这些文字。 34% 根据Pegasystems公司的一项调查，34%的人表示，他们曾经有过与人工智能互动的经历。（实际上，84%的人都曾与人工智能互动过。） 更聪明地招聘 千里马常有，而伯乐不常有。招聘是一个特别容易受个别人主观因素影响的过程。一个人很容易因为某个求职者的姓名、毕业院校甚至简历上的字体大小就对他产生好恶。所以现在有些公司已经开始在招聘中寻求人工智能技术的帮助了。 比如沃达丰、尼尔森和联合利华等公司在招聘时，会让求职者先玩一款由AI创业公司Pymetrics开发的手游，这款手游能够评估求职者的认知和情商水平，同时在设计中有避免了所有种族、性别等其他因素的影响。当软件筛选出表现最好的一批求职者后，联合利华会要求他们在HireVue网站上录一段视频，他们在视频中要回答一些问题，比如如何解决工作中遇到的各类挑战。该网站的人工智能算法不仅会分析求职者说了什么，还会观察他们的反应有多快，以及他们的面部表情透露了什么情绪线索。通过了这些初步测试的求职者便会得到真人面试的机会。 联合利华表示，启用该系统后，人才对该公司工作邀约的接受率提高了；从种族、民族和社会经济状况等多个指标上来看，公司人才的多元化也提高了。以录取新员工的毕业院校数量来看，该公司新员工教育背景的多元化程度达到了以往的三倍。 20% 据英国Computerlove公司的调查显示，有20%的人希望语音助手程序能使他们变得“更幽默或更有魅力”。 打造终极经理人 很多人根深蒂固地认为，只有人类才有资格评价人类的行为。不过那是过去了。现在，计算机算法已经在越来越多地评估我们的行为甚至意图，并得出结论。尤其是在职场中，为了了解潜在的人员流失风险、高绩效员工的特质，以及哪些因素有助于保持团队的活力，人力资源部门很多时候都会求助于人工智能程序。比如波士顿的Humanyze公司就在试验一种智能身份徽章，它可以持续追踪员工之间的沟通情况，使雇主能够找到其中的模式，分析公司的工作实际上是怎样完成的。 西雅图的创业公司Textio则使用人工智能技术帮助企业撰写合适的招聘广告（该公司的“增强写作平台”在通过堆砌语言以吸引多元化的求职者方面特别有效）。很多大公司在人力资源领域也引入了AI技术。比如英特尔公司正在研究使用人工智能技术开发一项新的内部工具，以将员工与公司内部的其他工作机会进行匹配，以更好地保留人才。 人工智能的这些新功能可以帮助企业吸引和留住他们所需要的人才，待这些流程实现自动化后，企业的招聘成本也将有所下降。那么它有没有什么缺点呢？一个潜在的风险，就是有可能造成企业与员工的疏远——毕竟员工都不喜欢雇主越来越多地侵入他们的生活。 人工智能将如何颠覆华尔街和银行业 你的按揭贷款经理是机器人 次贷危机爆发后冒出了一种新观点：机器可能比人类更知道如何正确地发放住房贷款。最近，房利美对抵押贷款机构进行的一项调查发现，美国有40%的抵押贷款银行已经采用了人工智能技术来处理手续繁琐的申请流程，检测客户可能的欺诈行为，以及预判借款者的违约风险。比如旧金山的Blend公司已经为包括富国银行在内的114家银行提供了在线抵押贷款申请程序，使贷款审批过程缩短了至少一星期。假设当年有这种人工智能技术，那么次贷危机还会出现吗？我认为即便不能完全避免，至少也能减轻次贷危机的烈度，因为机器会比人类更早发出预警信号。Blend公司联合创始人、CEO尼玛·甘沙里表示：“关于数据的错误决策可以在瞬间被发现和修正。”虽然银行尚未开始基于人工智能的评估结果来审批贷款的发放，但很多银行已经发现了人工智能程序的另一个好处——能让更多美国人获得住房贷款。Blend公司定义的“低收入群体”一向不愿申请抵押贷款。但现在，该群体通过Blend的移动应用申请房屋贷款的可能性是其他阶层的三倍。富国银行的消费银行业务主管玛丽·马克表示： “它消除了人们的恐惧。” 为专业投资者带来新优势 过去十年间，金融行业的数据量呈爆炸式增长，即使那些20来岁的分析师不眠不休地干，也不可能处理完所有数据。人力虽然做不到，机器却可以。因此，彭博、FactSet研究系统和汤森路透等金融研究机构都开发了一系列数据分析工具和技术，包括机器学习、深度学习和自然语言处理（NLP）技术等，以方便成千上万的专业金融人士迅速从海量信息中挖掘出有价值的见解。 彭博就是使用情感分析（亦是自然语言处理技术的一种）技术的先驱，彭博从10年前就开始研发这项技术了。简单说来，它所使用的机器学习技术会识别出某条新闻或某篇网文与一只股票有关，并赋予它一个情感分数。除了用于股市分析，人工智能技术也在向财富管理领域拓展。过去五年间，由于整个行业都在争相发掘包含在网站文章、语音分析、信用卡购买数据和卫星数据中的交易信号，各大投资集团里的所谓“另类数据”分析师的人数增加了四倍有余。包括贝莱德、富达、景顺、施罗德和普信集团在内的行业顶级研究机构都在使用人工智能技术。全球最大的资产管理公司贝莱德集团也是应用人工智能技术的先行者，它还建立了一个“贝莱德实验室”，专门用于开发人工智能技术。 72% 根据皮尤研究公司的调查，有72%的人担心机器人会抢走他们的工作。 业余投资者也能受益 由Betterment等创业公司和嘉信理财等传统经纪公司推出的“机器人理财顾问”服务，就是利用人工智能技术服务普通投资者的例子。这些理财工具的费用较低，它们基于你的风险偏好，用计算机算法来决定你的资产应该如何在股票、债券和其他资产上进行分配。这些公司的AI技术可以自动调整你的投资组合。当人工智能程序判定你需要合理避税或者需要遗产规划方面的帮助时，它还会让一名顾问（非机器人）打电话给你。 与此同时，一些金融机构也在研发能够帮助投资者做出明智的长期决策的投资工具。美银美林和摩根士丹利就是“量化基本面分析”这一新兴领域的两个大玩家。他们的目标是在基本的AI定量分析（也就是从海量数据中识别出模式）的基础上，结合由最顶尖的人类分析师的复杂分析训练的额外算法，用以进行基本面投资的评估，比如评估一个行业的潜长潜力，或是一家公司管理层的战略敏锐性等等。有了机器学习技术的加成，量化基本面分析系统将能够从失误中学习，进而不断完善。最终，普通投资者只需要花很少的钱，就拥了股神巴菲特般的长线投资智慧。到时，这套系统也可能有了一个比“量化基本面分析系统”更潮的名字。 人工智能如何改变我们制造事物的方法 更高效的设计 有人说，人工智能虽然对科技、医疗等行业冲击很大，但我们这些搞艺术创作的总归是安全的吧？并不完全是。美国有一家叫Autodesk的软件公司开发了一款叫Dreamcatcher的软件，它可以利用人工智能技术辅助人类设计师进行创作。这款软件已经被空中客车、安德玛和史丹利百得等多家知名企业采用。这款软件充分展示了机器也能创造出叹为观止的设计。人类设计师只需要输入需求、限制以及其他品质要求（甚至包括材料的总成本），软件就会自动生成几百甚至几千种设计方案。设计人员可以对这些方案进行筛选，在筛选的过程中，系统会自动判断设计者的偏好，并且给出更符合你偏好的迭代方案。空中客车利用该软件对A320客机的内饰隔板进行了重新设计，这种新设计的重量只有66磅，比之前的设计整整轻了45%。 人机融合 过去几十年间，机器人已经承担了各种各样的制造业工种。不过最近，有些机器人添加了一个新零件——人类。这种类型的机器人又叫“人机合作机器人”，形态不一而足，有的类似一个机器人助手，可以在人类工人劳动时将各种工具准确地递给他；有的则是像“钢铁侠”一样的外骨骼套装，人们穿了他，就会获得额外的力量以及AI软件的指导。比如宝马公司的斯帕坦堡工厂里就有一款昵称为“夏洛特小姐”的人机合作机器人，它主要用来安装车门。梅塞德斯奔驰公司也在开发人机合作机器人技术，以使该公司组装的部分奢华车型每一台都能更加个性化。比如在使用人机合作机器人取代了体积更大的自动化系统后，人类工人在机器人助手的帮助下，就能更快地在大量零件中找出定制版S级轿车所需的特殊零件。麻省理工学院的教授朱莉·肖正在开发一种特殊的软件算法，它能教会机器人解读人类发出的信号，并使它们知道何时以及如何与人类进行沟通。有些研究人员甚至正在研究如何将人机合作机器人与人的脑电波相连。到了这一步，究竟是机器在为人服务，还是人类已经成了机器的一部分呢？ 48% 根据Mindshare公司的调查，48%的人认为伪装成人类的机器人“令人毛骨悚然”。 提供清洁能源 如果风能的利用成本想要降到化石能源以下，那么风能转化为电能的过程就必须变得更加高效。西门子公司开发的一种机器学习技术恰好能起到这个效果。研究人员意识到，大型风力涡轮机可以利用天气和零部件振动等数据，不断对自身进行调整——比如调整风车叶片的角度等等。研究人员沃克玛·斯特金指出，靠分析计算，是实现不了这样的目标的。 然而这对于人工智能和机器学习技术却不是难事。斯特金指出，风力发电机的传感器其实早已生成了所需的参数，只不过“以前这些参数只用于远程和服务诊断，但现在它们也在帮助风力涡轮机发出更多电力。”这项技术甚至可以对风力轮机进行相应调整，使其适应因穿过前面其它风力轮机而变得不可预测的气流。 去年，西门子的风能部门与西班牙歌美飒公司的风能业务合并，成立了一家名叫西门子歌美飒可再生能源的独立公司。这项人工智能技术的广泛运用，也为该公司带来了新的机会。 守护人类的安全和健康 很多人并不了解自己的极限。很多人要么吃得太多，要么睡得太少，或者高估了自己在一段时间里能达到的目标。在一些小事上，这倒也无伤大雅。但在某些专业领域——比如长途开货车，或是操作重型设备，如果你不知道自己的极限，后果可能相当危险，甚至会付出严重代价。 有鉴于此，现在很多公司都使用人工智能程序作为“守护天使”，以保护高风险工种的安全。商业软件公司SAP的高级副总裁麦克·弗拉纳根介绍道，这些人工智能系统受过几百小时的员工传感器数据的训练，可以实时监测工作人员的心率、体温以及疲劳和紧张水平的指标数据，当员工需要休息时，系统就会发出警报信号，提醒员工休息。（SAP有一款安全产品就是这样工作的。） 那么对于我们这些普通群众呢？如无意外，很快我们也会在自己的汽车上看到这项技术了。目前，各大汽车厂商也在争相研发疲劳检测技术。目前市面上只有少辆车型搭载了防疲劳驾驶功能，功能本身也非常简单——车子的仪表盘上会亮起一个咖啡杯状的图标，提醒你应该休息了。Nuance Communications公司的汽车创新管理总监尼尔斯·兰克表示，过不了多久，疲劳检测以及语音和面部识别技术就将成为新车市场上的标配。该公司目前已与多数主流汽车厂商展开了合作。 人工智能保护个人安全的三种方式 制造能够自动选择其目标的武器 杀人机器人能够识别并消灭敌方势力，这个曾经只出现在灾难科幻小说里的场景如今已不再遥远，前提是各大公司和五角大楼下决心开展这一合作。国防官员到目前为止已经叫停了致命自主武器系统（政府的官方称法）的研发。在理论上，这一系统能够在没有任何人命令的情况下发动攻击，就像Facebook在没有征得人们同意的情况下标记照片中你的朋友那么简单。 然而，用于支持进行类似攻击的人工智能技术已经处于研发中。五角大楼最知名的人工智能计划Project Maven旨在使用机器学习算法，从无人机拍摄的视频中发现恐怖分子目标，协助军方的ISIS打击行动（据称涉及20家技术和国防承包商，但并未对外宣布名单）。虽然开发战争用物资对于国防行业来说并不是什么新鲜事，但五角大楼正越来越多地采用硅谷在人工智能和面部识别方面的专长。双方日趋紧密的关系最近引发了争议。在多名员工因抗议而辞职之后，谷歌已于今夏宣布退出Project Maven。未来，各大公司是否能揽获利润丰厚的新人工智能国防合约，唯一的障碍可能便是其自身的意愿。 2022 牛津和耶鲁大学研究人员称，到2022年，人工智能在叠衣方面的能力要超过人类。 规避威胁 一旦预防网络和现实生活中的攻击以失败告终，其代价是异常惨痛的。2017年，个人数据泄露的平均成本达到了近400万美元。但最近攻击的激增也并非都是坏事：它也意味着可供挖掘的数据增多了。机器学习技术数十年来一直被用于检测攻击模式，并过滤邮件，但诸如Barracuda Networks这样的供应商所提供的新系统居然能够使用人工智能来学习特定公司和其高管独特的沟通模式，从而确定可能的钓鱼诈骗和其他黑客攻击行为。在现实安全领域，连摄像头都采用了人工智能技术，以发现并尝试阻止威胁。来自于初创企业Athena Security的新摄像头可识别拔枪动作，甚至自动报警。总之：我们掌握的数据越多，我们便可以更多地利用人工智能来打击犯罪。 侵占公款的人，注意了！ 如何抓住金融罪犯？像汇丰、丹斯克这样的国际性银行并没有聘请负责合规业务的员工，并让他们通过查阅上万笔交易来寻找可疑的活动，而是更多地依靠人工智能来发现金融诈骗、洗钱和欺诈活动。（这一举措最近大有升温的趋势，因为多家银行因未能发现流经其账户的非法资金而遭到了巨额罚款。）汇丰携手人工智能初创企业Ayasdi实现其合规的自动化。在汇丰为期12周的试运行过程中，Ayasdi的人工智能技术让正误识（看起来可疑但却是合法的交易）减少了20%，同时其可疑活动报告的数量与人工查验的数量一致。 人工智能改变人们购物、餐饮和生活的7个方式 无需亲自驾驶的汽车 NBC电视剧《The Office》的主角迈克·斯考特在将一辆租来的福特金牛座（Taurus）推进宾夕法尼亚州斯克兰顿附近的一个湖中时叫到：“这是它自愿的！”从技术方面来看，我们很久之前便已经可以让无人驾驶汽车在理想的路况下安全行驶，但在现实世界中，汽车应多学点人类开车的方式。这是初创企业Comma.ai的主攻方向，该公司由臭名昭著的iPhone黑客乔治·霍茨创建。Comma.ai的Openpilot技术并没有教授计算机系统如何辨别树木或停止标识，而是分析了普通驾驶员的驾驶模式，并以此来培训自动驾驶模型。公司从名为Chffr的行车记录仪应用以及一个名为Panda的插件模块中调取了数百万英里的驾驶数据，然后对数据进行累积，以打造能够模拟人类司机的自动驾驶系统。公司的技术目前正用于本田、风投和现代的部分车型上，公司将自己称之为自动驾驶界的安卓，而将对手特斯拉的Autopilot视为iPhone。Autopilot是一个开源系统，声称自身的成功之处在于：用户将让其变得更好。但愿特色拉所说的用户并不包括迈克斯·考特吧。 16% 说自己无惧乘坐无人驾驶汽车的女性的比例，而男性的这一比例为38%，来自于路透社/IPSOS的调查。 你的新旅行伴侣 事实证明，埃亚菲亚德拉冰盖已经伴随我们很长一段时间了，它在火山灰褪去后便已经存在。这座2010年喷发的冰岛火山影响了数百万的飞行员，而且它的喷发也让旅行通信进入了新时代。在信息流功能受限的情况下，航空公司发现社交媒体可以作为一个有效、实时的乘客沟通方式。Accenture Interactive社交媒体和新兴渠道负责人罗伯·哈勒斯表示：“一旦发生这种情况，这类通讯模式成为了一种无法阻止的力量。”然而自那之后，旅行者的数量出现了激增，2016年的游客数达到了12.5亿人，增长30%。以人为基础的社交媒体互动要达到如此规模是“不可能的”，哈勒斯说道。 让我们来问问能够回答旅行者基础性问题的客服聊天机器人：我的航班有延误吗？我的酒店的退房日期是什么时候？例如，Booking.com便拥有这样一台机器，公司称它可以自动回答60%的客户问询。该技术的下一个目标是让机器人了解旅客旅行的性质，是商务还是休闲，然后再根据旅客的喜好围绕整个旅程进行推荐，从建议航班升舱到预留最好的素食餐厅的座位，例如匹兹堡的餐厅。因此，当前我们所说的这些聊天机器人可能很快会成为功能齐全的自动礼宾接待员。 升级呼叫中心 “需要什么帮助吗？”到2020年，IBM预计85%的客户服务互动在无需人工介入的情况下便可以完成。机器学习和自然语言处理让聊天机器人、改良后的电话支持和自助服务界面能够完成大多数人工代表可以提供的功能。 那270万从事客户服务代表工作的美国人怎么办？一些可能会被部署到那些机器人无法从事的工作岗位（例如应对怒气冲天的客户）。依靠这一技术的公司表示，这项技术能够帮助消除人为失误，大幅提升数据获取速度，并杜绝客户服务互动中的偏见。 不要以为这项技术的终点是机器人。瑞士投行瑞银集团最近携手新西兰人工智能专家Faceme，对经济学家丹尼尔·卡尔特进行克隆，以便让机器人能够以他本人的方式与客户进行互动。瑞银集团表示，这个化身使用了IBM Watson人工智能技术，并由卡尔特本人亲自培训，是该银行探索提供“人类数字融合服务”的一部分。 |
IT’S TIME TO GET REAL ABOUT A.I.’S FUTURE, a subject in desperate need of discipline. The technology’s mind-blowing possibilities have apparently inebriated various seers, who take two routes to fantasyland: propagating boldly precise forecasts of jobs to be spawned and destroyed years hence, or spinning tales of A.I. transforming our world into a heaven (or hell). Instead, we wanted to confront the realities of how A.I. is changing business—minus the melodrama. On the chief source of A.I.-induced anxiety—employment effects—the reality is that no one knows or can know what’s ahead, not even approximately. The reason is that we can never foresee human ingenuity, all the ways in which millions of motivated entrepreneurs and managers worldwide will apply rapidly improving technology. Postmaster General Arthur Summerfield predicted confidently in 1959 that mail would soon be delivered by packing letters into guided missiles, the wonder tech of the day. A growing economy meant more letters, and the future for postal workers seemed bright. It was, for a while. The possibility that mail would cease to be written on paper never occurred to Summerfield, though the necessary technologies for email, texting, and the cell network existed in rudimentary form or were being developed. We risk missing the boat in the same way with A.I. The second reality to remember is that A.I.’s eventual uses will be determined largely by market forces. Earnest discussions of how A.I. can be directed to make the world a utopia miss that point. They recall RCA chief David Sarnoff’s long-ago prediction that the coming of color TV would enable people to see fine art in their homes. That sounded wonderful, but nobody wanted it for such high-minded uses. A.I. will be used by companies and consumers for countless practical purposes, most of them modest, and the cumulative effect can’t be foreseen. As we try to guess A.I.’s future, the key will be to think like self-interested people (including both good and bad guys) in the real world. No bad guys here, though. These 25 examples of A.I. at work are beneficial, even inspiring—and they’re real. How AI Is Changing the Way You Work Getting Us All To Speak The Same Language EVER SINCE THE GOLDEN age of the original Doctor Who and Star Trek, science fiction has highlighted devices that can automatically translate languages so that humans can talk to aliens without needing to study far-out dialects. It turns out that companies here on Earth, like Google, are using artificial intelligence technologies to create devices that can translate conversations from one language to another. While Google’s recently released Pixel Buds is a promising start, consider the ways businesses could use the technology when it works seamlessly. American executives could call up their Portuguese-speaking counterparts and brainstorm global partnerships on the fly. Businesses with international offices could more effectively communicate with employees, who could work in tandem with colleagues in other countries who don’t speak the same language. Salespeople could scout for potential leads in new regions and make cold calls that could bring about their next game-changing deal. Although many companies have instituted an English only policy as a way to keep employees speaking the same lingo, on-the-fly translation technology lets non-U.S. employees speak their mother tongues and retain aspects of their cultures—a benefit in this era of globalization. Reading Your Mind MOVE OVER, ALEXA. Voice control is cool, but consulting Alexa, Siri, or Cortana can be awkward and disruptive in public. Enter AlterEgo—a noninvasive, wearable device created by MIT researchers that knows what you’re going to say before you even open your mouth. The device can answer many queries within seconds, send private messages, and internally record streams of information to access at a later time—all without any observable external actions. AlterEgo doesn’t really read minds, although it may sound that way. Instead, the device effortlessly facilitates private human machine communication by interpreting electrical impulses in the jaw that are triggered when words or phrases are internally vocalized. Although university-based researchers are still in the process of collecting data and training the system, AlterEgo might also eventually serve as a platform for communication between users in high-noise environments, such as the flight deck of an aircraft or a factory floor, as well as a mode of communication for those with speech impediments. And while AlterEgo could radically speed up the process of writing, planning, and communicating, for now, humans would still be the ones stuck actually reading all those emails. 34% Percentage of people who believed they had interacted with A.I., according to a study by Pegasystems. (Percentage who actually had: 84%.) Hiring Smarter THE HIRING PROCESS IS fraught with challenges. Humans may be subtly or unconsciously swayed by a last name, a college, even the font size of a résumé. Now some companies are seeing if A.I. can help. Applicants at Vodafone, Nielsen, and Unilever, for example, play a smartphone game designed by A.I. startup Pymetrics that measures cognitive and emotional traits with an algorithm designed to avoid racial, gender, or other bias. Unilever then asks top candidates selected by the software to record a video on HireVue, answering questions about how they would handle various situations encountered on the job. Another algorithm sifts the best candidates by reviewing not just what the individuals said but also how quickly they responded and what emotional cues they revealed in their facial expressions. Those candidates who pass the early tests are rewarded with regular job interviews with a live person. Unilever says that since it instituted the system it’s getting a higher rate of acceptances when it offers a job, and has increased applicant numbers across several diversity measures, including race, ethnicity, and socioeconomic status—and that it’s drawing from a more diverse pool at three times as many colleges and universities. 20% Percentage of people who want their voice assistants to help them be “funnier or more attractive,” according to a study by Computerlove in the U.K. Building The Ultimate Manager JUDGMENT OF HUMAN behavior was once reserved for, well, humans. But increasingly, algorithms are the ones evaluating and drawing conclusions on our actions and even intentions. That’s especially true in the workplace, where HR departments are turning to A.I. for more scalable (and hopefully, more reliable) insights into possible attrition risks, attributes of high performers, and what makes teams tick. Boston-based Humanyze is experimenting with smart ID badges that track how employees interact with each other throughout the day, enabling employers to look for patterns to figure out how work actually gets done. Textio, a Seattle startup, uses A.I. to help companies craft the right recruiting ads (the “augmented writing platform” is particularly effective at surfacing language that will attract more diverse candidates). Big companies are getting in on H-less HR too: Intel is looking at using artificial intelligence to power a new internal tool that would match employees to other opportunities within the company, all in the name of retention. These new capabilities could help companies attract and retain the talent they need (and cut down on on-boarding and recruiting costs by automating these processes). One possible downside? They also risk alienating the very people they claim to serve—employees might not like the increasingly intrusive workplace of tomorrow. How AI Is Shaking Up Banking and Wall Street Meet Your New Robot Mortgage Lender ONE THEORY HAS ARISEN in the decade since the subprime mortgage crisis: Machines may be better than humans at giving out home loans. A new Fannie Mae survey of mortgage lenders found that 40% of mortgage banks have deployed A.I.—using it to automate the document-heavy application process, detect fraud, and predict a borrower’s likelihood of default. San Francisco–based Blend, for one, provides its online mortgage-application software to 114 lenders, including lending giant Wells Fargo, shaving at least a week off the approval process. Could it have prevented the mortgage meltdown? Maybe not entirely, but it might have lessened the severity as machines flagged warning signs sooner. “Bad decisions around data can be found instantaneously and can be fixed,” says Blend CEO and cofounder Nima Ghamsari. While banks are not yet relying on A.I. for approval decisions, lending executives are already observing a secondary benefit of the robotic process: making home loans accessible to a broader swath of America. Consumers in what Blend defines as its lowest income bracket—a demographic that historically has shied away from applying in person—are three times as likely as other groups to fill out the company’s mobile application. Says Mary Mack, Wells Fargo’s consumer banking head: “It takes the fear out.” A New Edge For Pro Investors… IN THE WORLD OF FINANCE, there’s been such an explosion of data collected over the past decade that even those twenty-something analysts working around the clock don’t stand a chance of being able to process it all. But machines might. Bloomberg, FactSet Research Systems, and Thomson Reuters have all developed an array of data science tools and techniques—including machine learning, deep learning, and natural language processing (NLP)—to quickly unearth valuable insights for thousands of financial professionals. Bloomberg was a pioneer of sentiment analysis (an example of NLP), which it began developing around a decade ago, in which machine-learning techniques are used to flag a news story or tweet as being relevant to a stock and assign a sentiment score. A.I. is also spreading to wealth management. Investment groups have more than quadrupled their number of “alternative data” analysts over the past five years, as asset managers scramble to unlock the potential of trading signals contained in website scrapes, language analysis, credit card purchases, and satellite data. Firms reported to be using A.I. for investment research include BlackRock, Fidelity, Invesco, Schroders, and T. Rowe Price. BlackRock, the world’s largest asset manager, has been a forerunner in adopting A.I. and is setting up a BlackRock Lab for Artificial Intelligence. 72% Percentage of people who are afraid of robots taking over their tasks, according to Pew Research. …And For The Amateurs Too “ROBO-ADVISER” SERVICES, offered by startups like Betterment and traditional discount brokerages like Charles Schwab, are already using A.I. to serve the investing masses. Their low-fee investment tools rely on algorithms to determine how your assets should be split among stocks, bonds, and other assets, based on your needs and your stomach for risk. Their A.I. can automatically rebalance your portfolio; it can also nudge a (nonrobotic) adviser to call you when the algorithms predict you need help with tax strategy or estate planning. The next frontier: A.I. smart enough to help savers make good decisions about long-term, buy-and-hold investments. Bank of America Merrill Lynch and Morgan Stanley are among the bigger players in an emerging discipline known (awkwardly) as quantamental analysis. They aim to combine the quantitative processing for which basic A.I. is best suited (basically, the capacity to spot patterns in gargantuan loads of data) with additional algorithms trained in the sophisticated analysis associated with super smart humans—assessing, say, the growth potential of an industry or the strategic acumen of a company’s management. Machine learning could eventually enable a quantamental system to learn from its mistakes. The ultimate goal: Warren Buffett–like stock-picking wisdom at low prices—and perhaps a name catchier than “quantamental.” How AI Is Changing How We Build Things Designing More Efficiently SURE, COMPUTER ALGORITHMS ARE TAKING over tech and science and medicine … but the creatives are still safe, right? Not exactly. A new program from software developer Autodesk called Dreamcatcher (rendering above) can use A.I. techniques to assist human designers as they go about their creative tasks. Already in use by companies including Airbus, Under Armour, and Stanley Black & Decker, the software is an example of the burgeoning field of generative design. A designer inputs requirements, limitations, and other qualities into the program—even the total cost of materials. The software then produces hundreds or even thousands of options. As the human designer winnows the choices, the software susses out preferences and helps iterate even better options. Airplane manufacturer Airbus used the software to redesign an interior partition in the A320 and came up with a design that was 66 pounds, or 45%, lighter than the previous setup. Melding Humans And Robots ROBOTS HAVE BEEN ON THE ASSEMBLY LINE doing all kinds of manufacturing for decades. Lately, a new feature is being added to the automated work machines: humans. Dubbed “cobots,” short for collaborative robots, the new setups range from robotic helpers that can hand the correct part to a human worker to an almost Ironman like robotic exoskeleton suit that a person wears to gain added strength and A.I. software guidance. BMW has a cobot nicknamed Miss Charlotte that is helping assemble doors at its Spartanburg, S.C., plant. Mercedes-Benz is turning to cobot technology to help personalize each car that the luxury-automaker assembles in some of its most expensive categories. Replacing larger automated systems, humans with more nimble cobot helpers can be quicker at choosing from among the huge variety of parts needed to customize S-Class sedans, for example. MIT professor Julie Shaw is working on software algorithms developed with machine learning that will teach cobots how and when to communicate by reading signals from the humans around them. Some researchers have even looked at connecting cobots to human brainwave readouts. Mind-reading assistive robots? Now that’s collaboration. 48% Percentage of people who found chatbots pretending to be human “creepy,” according to Mindshare. Powering Clean Energy IF WIND ENERGY IS TO BE decisively cheaper than fossil-fuel power, the process of transforming wind into electricity must get more efficient. Machine-learning technology developed at Siemens is helping. Researchers realized that huge wind turbines could use data on weather and component vibration to fine-tune themselves continually, for example, by adjusting the angles of rotor blades. But “you cannot analytically calculate this,” says researcher Volkmar Sterzing. That’s the right kind of problem for A.I. and machine learning. Sensors were already generating the needed parameters, but “previously, these were used only for remote maintenance and service diagnostics,” says Sterzing. “Now they are also helping wind turbines generate more electricity.” The technology can even adjust turbines to the unpredictable airflows coming through the turbines in front of them. Deploying this A.I. broadly is now an opportunity for Siemens Gamesa Renewable Energy, an independent company formed last year by combining Siemens’s wind operations with the wind power business of Spain’s Gamesa. Keeping An Eye On The Mortals HUMANS ARE NOT GREAT at knowing their own limits—they eat too much, sleep too little, and overestimate what can be achieved in a period of time. That may seem a matter of little consequence when it comes to, say, Thanksgiving dinner, but in certain professions—like long-haul trucking and heavy-equipment operation—such fallibility can be dangerous and catastrophically costly. That’s why companies are increasingly using A.I., guardian angel–like, to safeguard employees in high-risk jobs. Systems, trained on hundreds of hours of employee sensor data, monitor conditions—like an operator’s heart rate, body temperature, and indicators of fatigue level or nervousness—in real time and signal when that individual needs to rest or take a break, explains Mike Flannagan, an SVP at business software firm SAP. (SAP has a Connected Worker Safety product that does this.) As for the rest of us? We can expect to see this type of technology soon in our own garages, where automakers are dreaming up ways for our cars to keep an eye on us. While the tech is currently limited to a coffee cup icon that flashes on the dash in a few models, Nils Lenke, head of innovation management for automotive at Nuance Communications, an A.I. firm that works with most of the major carmakers, says fatigue-detecting voice and facial recognition technology will soon be standard in new vehicles. 3 Ways AI Is Making You Safer Making Weapons That Pick Their Targets ONCE THE STUFF OF APOCALYPTIC SCI-FI tales, killer robots capable of choosing and taking out our nation’s enemies are now within reach—if companies and the Pentagon decide to go that far. Defense officials have so far stopped short of developing Lethal Autonomous Weapons Systems (the government’s official term), which could theoretically strike without a human order as easily as Facebook can tag friends in your photos without your say-so. But the A.I.-driven technology that could form the basis for such attacks is well underway. Project Maven, the Pentagon’s most high-profile A.I. initiative, aims to use machine-learning algorithms to identify terrorist targets from drone footage, assisting military efforts to combat ISIS (more than 20 tech and defense contractors are reportedly involved, though they have not all been publicly named). Although supporting war efforts is nothing new for the defense industry, the Pentagon has increasingly looked to Silicon Valley for expertise in A.I. and facial recognition. That growing relationship has recently sparked controversy, with Google announcing this summer that it would withdraw from Project Maven after several employees quit in protest. Going forward, companies’ only barrier to winning lucrative new A.I. defense contracts may be their own unwillingness. 2022 Year in which A.I. will be better than humans at folding laundry, according to researchers at Oxford and Yale. Averting Threats THE FAILURE TO prevent attacks in cyberspace and IRL (in real life) is an expensive line item—the average cost of an individual data breach was nearly $4 million in 2017. But the surge in attacks of late has an upside: It means there’s also more data to mine. Machine-learning techniques have been used to detect patterns and filter emails for decades, but newer systems from vendors like Barracuda Networks can use A.I. to actually learn the unique communication patterns of particular companies and their execs in an effort to pinpoint potential phishing scams and other hacking attempts. In the world of physical security, A.I. is even being used in security cameras to “see” and try to stop threats. New cameras from startup Athena Security can identify when a gun is pulled and even automatically alert the police. In short: The more data we have, the more we can use A.I. to fight crime. Embezzler Beware! HOW DO YOU catch a financial criminal? Instead of bulking up compliance staff to sift through thousands of transactions in search of suspicious activity, banks across the globe like HSBC and Danske Bank are increasingly turning to A.I. to flag financial scams, money laundering, and fraud. (This push has gained even more momentum recently as several banks were hit with huge fines for failing to detect illegal funds flowing through their accounts.) HSBC partnered with A.I. startup Ayasdi to automate some of its compliance. In its 12-week pilot with HSBC, Ayasdi’s A.I. technology achieved a 20% reduction in false positives (transactions that looked suspicious but were legit), while retaining the same number of suspicious-activity reports as human review. 7 Ways AI Is Changing How You Shop, Eat, and Live Driving So You Don’t Have To “THE MACHINE KNOWS WHERE IT’S GOING!” CRIED Michael Scott, protagonist of NBC’s The Office, before launching a Ford Taurus rental car into a lake near Scranton, Pa. Getting an autonomous vehicle to drive safely under idealized road conditions has technically been possible for a while now, but for the real world, the cars are going to have to learn to drive a little bit more like us. That’s where Comma.ai, a startup founded by notorious iPhone hacker George Hotz, comes in. Rather than teaching its computer systems what a tree or a stop sign looks like, Comma.ai’s Openpilot technology analyzes the patterns of everyday drivers to train its self-driving models. The company is pulling in millions of miles of driving data from a dashcam app called Chffr and a plug-in module called Panda, then aggregating that data to create an autonomous system that mimics human drivers. The company—whose technology currently works with select Honda, Toyota, and Hyundai vehicles—is styling itself as the Android to Tesla’s Autopilot iPhone, an open-source system that is pegging its success to the notion that users will make it better. Let’s just hope Michael Scott isn’t one of them. 16% Percentage of women who said they would feel comfortable riding in a driverless car, vs. 38% for men, according to a Reuters/IPSOS poll. Your New Travel Companion EYJAFJALLAJÖKULL, it turns out, has stayed with us long after the ash faded away. The Icelandic volcano that erupted in 2010 affected millions of fliers and, in doing so, ushered in a new era in travel communications. With information flow capabilities strained, airlines discovered social media as an effective, real-time way to reach passengers. “Once that happened,” says Rob Harles, Accenture Interactive’s head of social media and emerging channels, that mode of communication “was an unstoppable force.” Since then, however, the number of travelers has ballooned, with 1.25 billion arrivals in 2016, an increase of 30%. Human-powered social media interaction on that scale is “impossible,” Harles says. Enter the customer service chatbot that’s able to answer travelers’ basic questions: Is my flight delayed? When is my hotel checkout? Booking.com, for instance, has a bot that the company claims can solve 60% of customer queries automatically. The next stage of this technology is for bots to understand the nature of your trip—business or pleasure?—and to make recommendations based on your preferences throughout your journey, ranging from suggesting a flight upgrade to reserving a table at the best vegan café in, say, Pittsburgh. So what’s currently known as a chatbot may soon resemble a full-blown automated concierge. Upgrading The Call Center “CAN I HELP YOU?” By 2020, IBM estimates that 85% of customer service interactions will be handled without a human agent. Machine learning and natural language processing make it possible for chatbots, enhanced phone support, and self-service interfaces to perform most of the functions of human representatives. As for the 2.7 million Americans who are employed as customer service representatives? Some may be redeployed to tasks that bots can’t do (like dealing with truly irate customers). Companies relying on this technology say it can help eliminate human error, drastically increase speed in data retrieval, and remove bias from customer service interactions. And don’t think this ends with bots. Swiss investment bank UBS recently teamed up with New Zealand A.I. expert FaceMe to digitally clone chief economist Daniel Kalt to interact with clients just as he would in the flesh. The bank said the avatar, built using IBM Watson A.I. technology and trained by the real Kalt, is part of its exploration to provide a “mix of human and digital touch.” |
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点球成金2.0 2017年，美国国家冰球联盟的选秀团队看到19岁的防守队员肖恩·德兹（上图），并没怎么留意。仅仅一年之后，德兹在第二轮便被选入多伦多枫叶队。之所以差别如此巨大，是因为总部位于蒙特利尔的创业公司Sportlogiq开发的人工智能软件，数据显示出德兹强大的组织能力。这款软件叫点球成金2.0。Sportlogiq只是利用人工智能技术帮球队寻找新星的公司之一。澳大利亚数据分析公司Brooklyn Dynamics的联合创始人卡姆·波特表示：“关键在于在人才未成型时及时鉴别，寻找可塑之才。”该公司曾与几家美国职业棒球大联盟球队合作，还为2017年环法自行车赛开发了机器学习人工智能系统，收集实时数据并预测比赛结果。 Brooklyn Dynamics正开发一款应用程序，繁忙的选秀团队和教练可使用机器学习技术，分析潜在球员和当前的球员，创建全球各地大学和专业团队均可访问的集中式数据库。“这是一个独特的工具，可成为招募人员的绝招。”波特说。“该组织的其他成员可以查看[统计数据]并加入前期讨论，判断哪些球员能为俱乐部带来价值。” 改变购物方式 现在实体商店有了新吸引力，店面都是绝佳的人工智能数据收集实验室。家居建材零售商家得宝就在分析数百万笔交易数据，弄清楚顾客还需要什么东西，例如厨房整体翻新，提供详细的家庭装修指南以及超级精准的交叉销售。丝芙兰利用ModiFace（最近被欧莱雅收购）的人工智能支持面部识别，帮助购物者选择最合适的眼影。该软件分析了数百万历史用户，更好地预测适合当前顾客的商品。 MIT-spinoff Celect利用机器学习预测购物者的行为方式，判断商店哪些地方更适合哪些促销活动，研究售卖哪些产品业绩最佳。 想高效检查完10条过道上价签？沃尔玛就已在50家商店测试机器人，机器人负责扫描货架上的缺货商品，将客户放错位置的产品放回原位，检查错误价签等。对于人类来说都是耗时繁琐的工作。创投调研机构CB Insights表示，虽然沃尔玛对应用技术守口如瓶，但Navii和Simbe之类制造人工智能机器人的公司非常引人注目，投资人也在密切观察。 广告能逗你笑吗？ 现在营销人员想达成目标越发困难，超模肯达尔·詹娜出演的百事可乐广告就明显效果不佳。但越来越多营销人士依靠人工智能降低失误的几率。情感人工智能公司Affectiva公司表示，《财富》美国500强企业里有四分之一在创意开发流程中使用其技术，主要在人工智能技术支持的调查研究测试用户对备选广告的反应。 Affectiva的系统已接受87个国家700万张面孔（以及38亿个面部框架）的图像训练，解码了个人的面部表情，技术可识别人们看到广告一刻的20种面部表情以及8种情绪，包括“厌恶”。 2011年以来，媒体研究巨头Kantar Millward Brown已应用Affectiva的产品（鉴别了3万个广告），发现耐克广受赞美的四分卫科林·卡珀尼克广告评分达到了微笑。“由此能确定，卡珀尼克关于牺牲和梦想的信息引发了积极的反应。”该公司董事总经理格雷厄姆·佩奇表示。他们还发现，观众对世界杯广告中的女运动员反应回应，比较出乎意料。 佩奇指出，除了帮客户提升广告效果，Kantar也从中累积了对所有客户有意的经验。该公司表示，观众描述为“进步”的广告，即主角更为现代（而非传统）时，效果提升了25%。 自己种出食物 表面上看农业很简单：在土里播种、浇水、收获，然后重复。但实际上种植粮食基于一系列复杂的因素。“我们在农业中处理的大量数据非常复杂。”室内垂直农业企业Plenty的联合创始人兼首席科学官奈特·斯托瑞说。环境因素（举几个例子：气流、二氧化碳、光照和湿度），植物遗传以及施肥和浇水之类人类行为都是相互作用的变量。现在Plenty和许多创业公司都在用人工智能技术协助管理农业中各种复杂决策。例如，Plenty及其竞争对手Bowery和Gotham Greens都在搭建系统收集和分析图像数据，通过机器学习确认植物是否缺氮、缺铁或遭遇虫害问题等，然后及早应对。“软件可以发现问题所在，而且能实现大规模自动检测，人力很难做到。”斯托瑞说。 人工智能改变医疗的三种方式 让医疗重归人性 当前美国医疗行业前景相当不明朗：每年超过1200万个严重诊断错误，医疗领域3.6万亿美元有三分之一浪费，预期寿命将连续三年减少（此前从未出现），医生倦怠、抑郁和自杀的水平均达到顶峰。与此同时，每个人产生的医疗数据超过以往任何时候，举几个例子，可穿戴传感器生理学、扫描解剖学、DNA测序，肠道微生物组生物学均是来源。进入深度学习人工智能领域，神经网络会影响各类临床医生，实现准确辨识扫描片、载片、皮肤病变和眼底等，还能在卫生系统应用，促进远程监控推广，最终不需要实体医院；在消费者层面，可提供虚拟医疗顾问更好地管理甚至防治疾病。这仍是人工智能整合人医疗实践的早期阶段，宣传热闹却实证寥寥。但这是我们应对各种严峻挑战的好机会，可以利用丰富的数据减少错误和浪费，并节省时间，显著改善临床医生与患者的关系。 |
Moneyball 2.0 WHEN NHL SCOUTS looked at Sean Durzi (above) in 2017, they decided to pass on the 19-year-old defenseman. Just one year later, Durzi went second round to the Toronto Maple Leafs. The difference was powerful new A.I. software by Montreal-based startup Sportlogiq that parsed terabytes of data to uncover his powerful playmaking ability—call it Moneyball 2.0. Sportlogiq is just one of several companies using A.I. to help teams spot the next star. “It’s all about identifying talent in hidden pockets and finding that diamond in the rough,” says Cam Potter, cofounder of Brooklyn Dynamics, an Australian data-analytics company that has worked with several Major League Baseball teams and even developed a machine-learning A.I. system for the 2017 Tour de France that collected real-time data points and spit out race predictions. Brooklyn Dynamics is developing an app that will soon allow time-crunched scouts and coaches to run machine-learning analytics on both prospects and current players, creating a centralized database that can be accessed by college and pro teams around the world. “It’s a unique tool to add to the recruiter’s repertoire,” Potter says. “Other members of the organization can then look at [the stats] and join in on the draft discussion to eventually decide who’s going to bring value to the club.” Change How You Shop BRICK-AND-MORTAR stores have a new calling: They’re perfect A.I. data collection labs. Home Depot is using data from millions of transactions to figure out what else you—the DIY-er grappling with, say, a big kitchen renovation—might need and provide detailed home project guides as well as hyper-targeted cross-selling. Sephora has used A.I.-powered facial recognition by ModiFace (recently bought by L’Oréal) to help shoppers select that exact right makeup shade: The software analyzes millions of other past users to better predict what will look good on you. And MIT-spinoff Celect uses machine learning to forecast how shoppers behave, determine what kinds of promotions work better in what part of the store, and figure out where products should be placed for optimal results. As for that price check in aisle 10? Walmart, for one, has tested robots at 50 stores that scan shelves for out-of-stock items, products placed back in the wrong spot by customers, and incorrect prices —all time-consuming and cumbersome jobs for humans. Though retailers are tight-lipped about in-store tech, companies like Navii and Simbe that make A.I.-powered robots are attracting attention and investors, according to CB Insights. Will This Ad Make You Smile? MARKETERS DON’T ALWAYS hit the mark—we’re looking at you and that Kendall Jenner spot, Pepsi—but increasingly, they’re leaning on artificial intelligence to make those misfires less likely. Emotion A.I. firm Affectiva says a quarter of Fortune 500 companies use its tech in their creative development processes, testing the reaction to potential ads in A.I.-enhanced survey research. Affectiva’s system, which has been trained on images of 7 million faces (and 3.8 billion facial frames) from 87 countries, decodes the facial expressions of individuals—the tech identifies 20 specific ones as well as eight emotions, including “disgust”—moment by moment as they watch ads. Media research giant Kantar Millward Brown, which has deployed Affectiva’s product since 2011 (30,000 ads’ worth) found Nike’s lauded Colin Kaepernick ad scored smiles at key points. “We were really able to pinpoint the fact that it was Kaepernick’s message about sacrifice and dreams that triggered the positive response,” says Graham Page, the firm’s managing director. They also found that viewers responded positively, and with surprise, to women players featured in World Cup ads. Page noted that beyond helping clients sharpen their campaigns, Kantar has gained broad insights that benefit all clients. Ads that viewers describe as “progressive,” with protagonists featured in modern roles (rather than traditional ones) are 25% more effective, according to the firm. Growing Your Next Meal ON THE SURFACE, FARMING SEEMS LIKE a simple endeavor: Pop seeds in the ground, water, harvest, repeat. But in reality, how food is grown is built on a series of intricate equations. “A lot of the data we deal with in agriculture is very complex,” says Nate Storey, the cofounder and chief science officer of Plenty, an indoor vertical-farming enterprise. Environmental factors (airflow, carbon dioxide, light, and humidity, to name a few), the genetics of the plant, and the things we do to it, like fertilizing and watering, are all interacting variables. Now Plenty and a number of other startups are using A.I. to help manage the complex decisions that go into farming. For example, Plenty and its indoor-ag rivals Bowery and Gotham Greens are all building systems that collect and analyze data sets of images that can help identify whether a plant has an issue, like nitrogen or iron deficiency or a pest problem, through machine learning and then preemptively treat it. “The software can learn what the problems are and do it in an automated fashion at a large scale that we couldn’t individually do,” Storey says. 3 Ways AI Is Changing Healthcare Making Health Care Human Again THE CURRENT U.S. HEALTH CARE PICTURE is pretty bleak: more than 12 million serious diagnostic errors each year, a third of the $3.6 trillion spent attributed to waste, reduction in life expectancy for what will be three years in a row (which is unpre¬cedented), and peak levels of physician burnout, depression, and suicide. That’s all happening at a time when there is more medical data per individual than ever, imagined with wearable sensor physiology, scan anatomy DNA sequencing, gut microbiome biology, just to name a few layers. Enter deep-learning A.I., with neural networks that will impact every type of clinician, from helping to accurately read scans, slides, skin lesions, eyegrounds, and more, to health systems, promoting the use of remote monitoring that ultimately obviates the need for regular hospital rooms, and at the consumer level, by providing a virtual medical coach to better manage or even prevent diseases. It’s still early in the integration of A.I. into medical practice, with far more hype than validation. But it’s our best shot to deal with all of the formidable challenges: to use the wealth of data to reduce errors and waste, and the gift of time to markedly improve the clinician-patient relationship. |
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超越医生 就在过去几年，一系列技术越发可信，当然仍在不断进步，即通过人工智能技术读取放射扫描（如Imagen），识别肿瘤并跟踪癌症的扩散（Arterys），用视网膜成像检测眼睛状况（谷歌的DeepMind），通过“不流血的血液测试”（梅奥合资企业和AliveCor）标记血钾水平危险的异常，并以其他方式协助诊断甚至预测疾病等棘手问题。从历史上看，诊断错误率在5%到20%之间，某些病症的错误率更高，与此同时医疗系统因医生短缺和倦怠承受压力，人工智能或许能帮忙缓解。 |
Outsmarting Your Doctor IN JUST THE PAST few years, there have emerged credible if still-in-the-works A.I.-powered technologies that can read radiology scans (like Imagen), identify tumors and track the spread of cancer (Arterys), detect eye conditions using retinal imaging (Google’s DeepMind), flag dangerously abnormal potassium levels via a “bloodless blood test” (Mayo Clinic Ventures and AliveCor), and otherwise assist with the tricky business of diagnosing, or even predicting, disease. Historically, diagnostic error rates have been put at 5% to 20%, though the rate is higher for some conditions, while the health care system is strained by doctor shortage and burnout—some things A.I. may be able to treat. |
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重塑药物研发 医药行业从不乏命运起伏的案例。某种药可能在早期研究中看起来安全，却在大规模临床试验中出现问题，代价极其高昂。德勤数据显示，2017年美国大型生物制药公司的投资回报率降至令人沮丧的3.2%。这也是BERG和Roivant Sciences等美国公司，以及英国的Exscientia等都希望借助人工智能更好地调配资源。BERG与阿斯利康和赛诺菲巴斯德等大制药公司合作，利用算法提供的临床数据，为药物和分子找出可能奏效的生物靶点，从而治疗帕金森病等疾病。赛诺菲在分析大量数据，希望了解为什么流感疫苗对某些人有效而对其他人无效（考虑到去年严重的流感疫情，这是很重要的公共卫生问题）。利用人工智能协助制药工具仍处于早期阶段。但前景很明确，将制药研发工作集中在有希望的目标上，避免浪费大量的时间和金钱，希望有一天能令药物开发过程更加简化，不管是药企还是患者都可从中受益。 7% 非营利组织Ideall的一项研究显示，认为“机器人可以代替自己工作”的人力资源行业员工占总人数7%。 逆转疾病 美国的医疗系统一直被批评只注重分诊，却不主动寻找更便宜也更积极的治疗方法， 企业也为生产力损失和医疗成本暴涨付出巨大代价。Virta Health的首席执行官萨米·因肯能另辟蹊径，他想用人工智能防止有糖尿病风险的患者发病，甚至在早期试验中通过纯数字平台治疗2型糖尿病。Virta为顾客安排健康顾问，努力改变顾客的生活方式，顾问会提供饮食和其他因素方面的个性化建议。数字平台还提供数字连接工具测量血糖、酮、血压和体重等指标。临床医生了解患者预期的血糖和体重改善情况之后，工作流程中可按患者情况安排诊疗次序。Virta也有竞争对手，IBM的沃森健康部门和医疗技术巨头美敦力正合作开发一款名为Sugar.IQ的应用程序，提供类似的工具。（财富中文网） 译者：Min, Feb |
Reinventing Drug R&D THE MEDICINE BUSINESS IS FILLED WITH TWISTS OF FATE. A drug may appear safe for humans in early studies with small groups of patients only to crash and burn in spectacularly expensive fashion in a large-scale clinical trial. In fact, return on investment for the largest biopharmaceutical companies in the U.S. fell to a dismal 3.2% in 2017, according to Deloitte. Which is why American companies like BERG and Roivant Sciences and U.K.-based Exscientia want to harness the power of A.I. to better deploy resources. BERG has partnered with major drugmakers like AstraZeneca and Sanofi Pasteur to use clinical data fed through an algorithm to identify promising biological targets for drugs and molecules that may be able to treat diseases like Parkinson’s. Sanofi is also analyzing huge amounts of data to gain a deeper understanding of why certain flu vaccines are effective for some people but not for others (a critical public health question considering last year’s devastating flu season). A.I. as a central medicine-making tool is still in its early stages. But the promise is clear: Being able to funnel pharma R&D efforts to the most promising targets can avoid a huge waste of time and money and, hopefully one day, lead to a more streamlined drug development process that benefits companies and patients alike. 7% Percentage of HR employees who think “a robot could do their job,” according to a study by Ideall. Reversing Disease AMERICA’S HEALTH care system has been criticized for favoring triage over cheaper, proactive approaches—and businesses pay the price in lost productivity and skyrocketing health care costs. Virta Health CEO Sami Inkinen is taking a different tack, using A.I. to prevent patients at risk for diabetes from developing the full-blown disease and, in early trials, even reversing Type 2 diabetes through its purely digital platform. Virta aims to shift customers’ lifestyles by connecting them with coaches who give them personalized recommendations on diet and other factors. It also provides digitally connected tools to measure blood sugar, ketones, blood pressure, and weight. Using a patient’s anticipated blood sugar and weight improvement, clinicians can prioritize patients in their hourly workflow. Virta is not alone: IBM’s Watson Health unit and medtech giant Medtronic are collaborating on an app called Sugar.IQ that offers similar tools. |
