本文标题为“你的生活是怎样的" appeared in German last week in the "Digitaliserung" column ofwirtschaftwoche..

当一项技术有了突破性进展时,往往只能事后决定。在人工智能(AI)和机器学习(ML)的情况下,这是不同的。ML是人工智能的一部分,它描述规则并从大量数据中识别模式,以便预测未来的数据。这两个概念几乎无所不在,在大多数流行语排行榜上名列前茅。

就个人而言,我认为 - 这与AI和ML的崛起明显挂钩 - 即在今天开发智能应用并使用它们的时间从未如此美好的时光。必威体育精装版app官网为什么?因为三件事在一起。首先:全球用户正在数字捕获数据,这是通过传感器或GPS的物理世界,或通过单击流数据在线。结果,存在临界数据。其次,无论其大小如何,都有足够的公司和组织,在公司和组织中有足够的计算能力。第三,已经发生了“算法革命”,这意味着现在可以同时训练万亿季度,使整机学习过程更快。这允许更多的研究,这导致知识所需的“临界质量”,这是为了开发新算法和架构的发展指数增长。必威体育精装版app官网

我们可能会与AI相对较长的路,但进步悄然来。毕竟,在过去的50年中,AI和ML是只能访问的研究人员和科学家的独家圈子。现在正在发生变化,因为AI和ML服务的软件包,框架和工具今天可供各种公司和组织,包括在此领域没有专用研究组的公司。麦肯锡的管理顾问预计,全球基于AI的服务,软件和硬件将每年增长15-25%,2025年达到约130亿美元的数量。许多初创企业正在使用AI算法想象力的所有事情 - 在医学图像中寻找肿瘤,帮助人们学习外语,或在保险公司中自动化索赔。与此同时,正在创建完全新的应用程序,其中人与机器之间的自然对话正在进行中级。

Progress through machine learning

Is the hype surrounding AI and ML even justified? Definitely, because they offer business and society fascinating possibilities. With the help of digitization and high-performance computers, we are able to replicate human intelligence in some areas, such as computer vision, and even surpass the intelligence of humans. We are creating very diverse algorithms for a wide range of application areas and turning these individual pieces into services so that ML is available for everyone. Packaged into applications and business models, ML can make our life more pleasant or safer. Take autonomous driving: 90% of car accidents in the US can be traced to "human failure". The assumption is that the number of accidents will decline over the long term if vehicles drive autonomously. In aviation, this has already been reality for a long time.

麻省理工学院的先驱埃里克·布林约夫森和安德鲁·麦卡菲预测,所谓“第二机器时代”的宏观经济效应,将与蒸汽机取代人类肌肉力量时所释放出的效果相当(“第一机器时代”)。许多人对人工智能与人类智能并存的想法感到不安。这是可以理解的。因此,我们必须在技术发展的同时,讨论人类和人工智能在未来如何共存;出现的道德和伦理方面;如何确保我们对人工智能有良好的控制;以及我们需要哪些法律参数来管理所有这些。回答这些问题将与解决技术挑战的努力同等重要,教条和意识形态都无济于事。相反,我们需要的是一场客观的、基础广泛的辩论,考虑到整个社会的福祉。必威体育精装版app官网

靠在亚马逊的机器

在过去的20年里,亚马逊成千上万的软件工程师一直在研究ML,我们敢说我们是将AI和ML作为商业技术应用时间最长的公司。我们知道,每当市场参与者面临进入壁垒时,创新技术总会起飞。

That is the case right now with AI and ML. In the past, anyone who wanted to use AI for himself had to start from scratch: develop algorithms and feed them with enormous amounts of data – even if he later needed an application for a strictly confined context. This is referred to as so-called "weak" AI. Many of the consumer interfaces that everyone is familiar with today, such as recommendations, similarities or autofill functions for search prediction – they are all ML driven. In the meantime, they can predict inventory levels or vendor lead times, detect customer problems and automatically deduct how to solve them; and discover counterfeit goods and sort out abusive reviews, thereby protecting our customers from fraud. But that is only the tip of the iceberg. At Amazon, we are sitting on billions of historical order information data, which allows us to create other AI/ML-based models based on AI for many different kinds of functionalities. For example programming interfaces that developers can use to analyze images, change text into true-to-life language or create chatbots. But ultimately, there is something to be found for everyone who wants to define models, train them, and then scale. Pre-configured, attuned libraries and deep learning frameworks are widely available, which allow anyone to get started very fast.

Companies like Netflix, Nvidia, or Pinterest use our capabilities in ML and deep learning. More and more layers are being created in a kind of ecosystem on which companies and organizations can 'dock' their business – depending on how deep they want to, and are able to, immerse themselves in the subject matter. Decisive is the openness of the layers and the reliable availability of the infrastructure. In the past, AI technologies were so expensive that it was hardly worth it to use them. Today, AI and ML technologies are available off the shelf, and they can be called up according to one's individual requirements. They form the basis for new business models. Even users who are not AI specialists can very easily and affordably incorporate the building blocks into their own services. In particular small and medium-sized companies with innovative strength can benefit. They do not have to learn any complex ML algorithms and technologies, and they can experiment without incurring high costs.

Artificial intelligence helps to satisfy the customer

其中一个最先进的应用领域是电子商务。AI支持的预选机制有助于公司释放客户从复杂性的决策。最终目标是客户满意度。如果只有三种类型的牙膏,顾客可以轻松挑选一个并对此感觉良好。当提供超过50种,选择变得复杂。你必须决定,但你不确定决定是正确的。越多的可能性,对客户变得越难。我们最着名的算法来自该领域:根据具有类似属性的产品的购买历史或对类似物质感兴趣的其他客户的行为过滤产品建议。

当然,一致的质量也有助于客户满意。智能支持使提供商和客户更轻松。例如,对于亚马逊新鲜,例如,我们已经开发了算法,了解新鲜杂货的外观,这种国家持必威体育精装版app官网续多长时间,以及当食物不再销售时。航空公司或铁路运输公司也可以通过运行基于货运的图像数据的算法来使用这一点,以便通过运行算法;该算法将识别损坏的商品并自动对其进行排序。

如果您可以预测需求,您可以更有效地计划

在B2B和B2C业务中,快速提供商品是至关重要的。正是由于这个原因,我们亚马逊公司开发出了能够预测日常商品需求的算法。这对于时尚商品来说尤其复杂,因为它们总是有许多不同的尺寸和变化,而且再订购的可能性非常有限。有关过去需求的信息,以及季节性商必威体育精装版app官网品可能出现的波动、特价的影响以及客户对价格变化的敏感性等,都会输入到我们的系统中。今天,我们可以精确地预测在一个特定的日子里,有多少衬衫在特定的尺寸和颜色将被出售。我们已经解决了这个问题,并将该技术作为web服务提供给其他公司。例如,MyTaxi就得益于我们基于ML的服务,可以计划客户何时何地需要车辆。

新的分工

但是人工智能不仅仅是预测。在与众多行业相关的实现领域,我们正在思考人工智能如何能够最大限度地帮助我们走出泰勒主义工作模式的另一步。应用在机器人上,人工智能可以让人们从日常活动中解脱出来,而这些活动通常是身体上的困难和压力。机器非常擅长,有时甚至胜过人类要完成的复杂任务,例如在仓库中为一定数量的订单找到最佳路线,并将重货运输到发送给客户的地点。相比之下,对于本应简单的任务,机器人会不知所措;例如,识别落在错误架子上的盒子。那么如何将两位选手中的佼佼者结合起来呢?通过让智能机器人向人类学习如何识别正确的货物,接受各种订单,并在最有效的路线上自主地在仓库中导航。这就是我们如何去掉任务中最乏味的部分,并将资源转移到与客户的更多交互上。

我们的客户SCDM使用的核心思想是为“人类”的优势释放资源,但在一个完全不同的背景下。SCDM是一家为银行和保险公司提供数字化支持的服务提供商。使用人工智能,SCDM使客户能够对格式非常不同的文档(PDF、Excel或照片)进行分类,例如包含数百页的投资产品绩效报告。通过同时扫描数十万个文档,SCDM的算法可以识别与特定请求相关的文档,找出特定类型准备的相关数据所在位置,然后从文档中提取数据。因此,在计算数字时,偏见和错误更少,与投资者、分析师和其他客户等重要利益相关者进行人际交往的时间也更多。

Machine learning in education, medicine and development aid

除了他们潜在的效率和生产力,ML和AI也可以用于教育。Duolingo提供免费语言课程应用程序,使用文本语言算法评估和纠正学习者的发音。在医学中,AI支持分析X射线CTS或MRT图像的医生。世界银行还利用AI以实现基础设施计划,发展援助和未来更具目标的措施。必威体育精装版app官网

更大的乐观空间

尽管所有这些发展,来自学术界,商业和必威体育精装版app官网政府的许多人都对ML和AI的批判性观点。有警告说,新的超级情报正在危及我们的文明 - 这些警告都有有效吸引宣传。

然而,歇斯底里和兴奋都应该是llowed to get the upper hand in the public debate. What we need instead is a pragmatic-optimistic view of the emerging possibilities. AI enables us to get rid of tasks in our work which damage our health or where machines are better than we are. Not with the goal of making ourselves redundant. Rather, in order to gain more personal and economic freedom – for interpersonal relationships, for our creativity and for everything that we humans can do better than machines. That is what we should strive for. If we don't, we will ultimately forego the economic and societal opportunities that we could have grasped.

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