Gartner的2019战略性技术趋势：量子计算、区块链、AI

Gartner列出了企业和组织在2019年需要探究的十大战略性技术趋势：智能设备、增强分析、AI驱动的开发、数字孪生、边缘计算、沉浸式体验、区块链、智能空间、数字道德和隐私、量子计算。

这十大科技趋势被认为在未来5年将产生破坏性创新，并带来商业机遇。无处不在的智能设备提供各种基于大数据的贴心服务，将是科技的未来。Gartner称之为Intelligent Digital Mesh，其具体的定义如下：

Intelligent：AI将深入所有已有的垂直行业，并创造出新的行业。

Digital：物理世界和数字世界将被折叠，新的「沉浸」世界将会产生。

Mesh：人、生意、设备、内容、服务将连结成一个不断扩张的大网。

分析师认为，上述三点覆盖下的所有趋势都将带来持续的创新增量。以下是Gartner的2019年十大战略性技术趋势图谱以及对技术趋势的详细介绍分析，供技术创业者们参考。

智能设备[Autonomous things]

Whether it’s cars, robots or agriculture, autonomous things use AI to perform tasks traditionally done by humans. The sophistication of the intelligence varies, but all autonomous things use AI to interact more naturally with their environments.

Autonomous things指利用人工智能技术代替人类完成任务的工具，无论自动驾驶车辆、机器人、无人机、智能化应用或自动化代理都属于这一范畴。这五类设备覆盖了四个维度：陆地、海洋、大气及数字世界。五类应用、四个维度交织出多种可能，例如，在田间，无人机和农业机器人能够互相配合完成耕种任务。Gartner认为，未来每个应用程序、服务、或者IoT设备都将包含某种程度的「智能」。尽管这类设备能否被称为「智能」如今尚未达成共识，但不可否认的是，AI技术的确赋予了它们更优的与环境交互的能力、协调能力以及分析能力。

人们应该在实际业务中以及所使用的工具中不断探索融入AI技术的可能性。但值得注意的是，这类技术目前只能胜任某些较窄的任务，并不像人脑一样具备通用的决策能力，更遑论智力。

增强分析[ Augmented analytics]

Data scientists now have increasing amounts of data to prepare, analyze and group — and from which to draw conclusions. Given the amount of data, exploring all possibilities becomes impossible. This means businesses can miss key insights from hypotheses the data scientists don’t have the capacity to explore.

增强分析侧重于增强智能的特定领域，利用机器学习来彻底改变开发、使用和共享分析内容的方式。增强分析功能会迅速发展而得到主流采用，成为数据准备、数据管理、业务流程管理、流程挖掘和数据科学平台的一项关键功能。增强分析自动获得的洞察力也将嵌入到企业应用软件中，比如人力资源、财务、销售、营销、客户服务、采购和资产管理等部门的应用软件，从而优化所有员工的决策和行动，而不仅仅是分析员和数据科学家的决策和行动。增强分析可使数据准备、洞察力获取和洞察力可视化这个过程实现自动化，在许多情况下无需专业的数据科学家。

这将导致平民数据科学，这一套新兴的功能和实践使主要职责不是从事统计和分析工作的用户也能够从数据中获取预测性和规范性的洞察力。到2020年，平民数据科学家数量的增长速度会比专家级数据科学家数量快五倍。企业组织可以利用平民数据科学家来填补数据科学家奇缺和高成本导致的数据科学和机器学习人才缺口。

AI驱动的开发[ AI-driven development]

AI-driven development looks at tools, technologies and best practices for embedding AI into applications and using AI to create AI-powered tools for the development process. This trend is evolving along three dimensions.

市场正迅速转变，原来盛行这种方法：专业的数据科学家必须与应用软件开发人员合作，共同开发大多数由AI增强的解决方案；现在流行这种模式：专业的开发人员可以单枪匹马，使用作为一项服务而提供的预定义模型。这为开发人员提供了由AI算法和模型组成的生态系统，并提供了将AI功能和模型集成到解决方案中的定制开发工具。随着AI运用于开发流程本身，使各种数据科学、应用软件开发和测试功能实现自动化，专业应用软件开发面临另一批机会。到2022年，至少40%的新应用软件开发项目会在团队中有AI开发人员协同工作。

最终，高度先进的基于AI的开发环境使应用软件的功能和非功能方面实现自动化，这将带来‘平民应用软件开发人员’新时代。在这个新时代，非专业人员将能够使用AI驱动的工具自动生成新的解决方案。让非专业人员无需编写代码就能生成应用软件的工具普及，但我们预计AI驱动的系统会让灵活性达到一个新的水平。

数字孪生[ Digital twins]

数字孪生是指现实世界中的实体或系统的数字化表示。Gartner估计，到2020年将有超过200亿个联网的传感器和端点；可能会有数十亿个物件存在数字孪生。企业组织会一开始实施数字孪生，它们会不断改进数字孪生，提升收集和可视化合适数据的能力，运用合适的分析工具和规则，并高效地应对业务目标。

数字孪生是物联网之后的阶段，一个方面体现为企业实施本组织的数字双生（DTO）。DTO是一种动态软件模型，它依赖操作数据或其他数据来了解组织如何实施业务模型，连接其当前状态，部署资源，应对变化以提供预期的客户价值。DTO有助于提高业务流程的效率，并且创建更灵活、更动态、更迅疾的流程，有望自动应对不断变化的形势。

边缘计算[ Empowered edge]

Edge computing is a topology where information processing and content collection and delivery are placed closer to the sources of the information, with the idea that keeping traffic local will reduce latency. Currently, much of the focus of this technology is a result of the need for IoT systems to deliver disconnected or distributed capabilities into the embedded IoT world. This type of topology will address challenges ranging from high WAN costs and unacceptable levels of latency. Further, it will enable the specifics of digital business and IT solutions.

边缘是指人们使用的端点设备或嵌入在我们周围的端点设备。边缘计算描述了这样一种计算拓扑结构：信息处理和内容收集及传递更靠近这些端点。它试图保持流量和处理本地化，目标是减少流量、缩短延迟。

在短期内，推动边缘的是物联网和这种需求：使处理接近端点，而不是在集中式云服务器上处理。然而目的不是打造一种新的架构，云计算和边缘计算将作为互补模式而共同发展，云服务作为一种集中式服务加以管理，不仅在集中式服务器上执行，还在本地的分布式服务器和边缘设备本身上面执行。

在今后五年，专用AI芯片以及更强大的处理能力、存储和其他先进功能将被添加到种类更广泛的边缘设备上。这个嵌入式物联网世界极具多样性，加上工业系统等资产具有很长的生命周期，这将带来管理方面的重大挑战。从长远来看，随着5G日渐成熟，不断扩展的边缘计算环境会有更可靠的通信技术连回到集中式服务。 5G提供更低的延迟、更高的带宽，并且每平方公里的节点（边缘端点）数量急剧增加，最后一点对边缘来说非常重要。

沉浸式体验[ Immersive technologies]

Through 2028, conversational platforms, which change how users interact with the world, and technologies such as augmented reality (AR), mixed reality (MR) and virtual reality (VR), which change how users perceive the world, will lead to a new immersive experience. AR, MR and VR show potential for increased productivity, with the next generation of VR able to sense shapes and track a user’s position and MR enabling people to view and interact with their world.

对话式平台正在改变人们与数字世界互动的方式。虚拟现实、增强现实和混合现实正在改变人们感知数字世界的方式。感知模式和交互模式方面这种共同的转变将造就未来的沉浸式用户体验。

随着时间的推移，我们将从考虑单个设备和分散的用户界面技术转变为注重多渠道多模式体验。多模式体验将把人们与数字世界连接起来，周围有成百上千的边缘设备，包括传统计算设备、可穿戴设备、汽车、环境传感器和消费类电器。多渠道体验不光使用这些多模式设备当中先进的计算机感官（比如热量、湿度和雷达），还使用人类的所有感官。这种多体验环境将营造一种环境体验，其中我们周围的空间将构成「计算机」，而不是单个设备构成「计算机」。确切的说，整个环境就是计算机。

区块链[Blockchain]

区块链是一种分布式账本，有望重塑各行各业，因为它能够实现信任，提供透明度，减少业务生态系统之间的摩擦，因而可能降低成本，缩短交易结算时间，并改善现金流。今天，人们对银行、票据交换所、政府及充当中央权威的许多其他机构寄予信任，「单一版本的真相」在它们的数据库中安全地保管。集中式信任模式给交易增添了延迟和摩擦成本（佣金、手续费和货币的时间价值）。区块链提供了另一种信任模式，无需负责仲裁交易的中央机构。

目前的区块链技术和概念还不成熟，人们对它缺乏了解，而且在任务关键型规模化业务运营中未经证实。面对支持较复杂场景的复杂元素，尤其如此。尽管面临挑战，但区块链具有强大的颠覆性潜力，这意味着CIO和IT领导者应该开始评估区块链，即使他们在今后几年并不积极采用这些技术。

如今许多区块链项目并没有实现区块链的所有属性，比如高度分布式的数据库。这些受区块链启发的解决方案只是通过自动化业务流程或通过数字化记录来实现运营效率的一种手段。它们有望加强已知实体之间的信息共享，并改善跟踪并追踪物理和数字资产的机会。然而，这些方法并没有发挥区块链真正颠覆的价值，可能加大厂商锁定的风险。选择这个方法的企业应了解限制因素，准备好逐步完成区块链解决方案，还要明白这点：可以使用更高效、更优化地使用现有的非区块链技术获得相同的效果。

智能空间[ Smart spaces]

A smart space is a physical or digital environment in which humans and technology-enabled systems interact in increasingly open, connected, coordinated and intelligent ecosystems. As technology becomes a more integrated part of daily life, smart spaces will enter a period of accelerated delivery. Further, other trends such as AI-driven technology, edge computing, blockchain and digital twins are driving toward this trend as individual solutions become smart spaces.

智能空间是一种物理或数字环境，人员和技术支持的系统在日益开放、互联、协调和智能的生态系统中彼此交互。多个要素（包括人员、流程、服务和物件）汇集在智能空间中，为目标人群和行业场景打造更沉浸式、更交互式、更自动化的体验。

这个趋势融合已有一段时间，围绕智能城市、数字化工作场所、智能家居和联网工厂等要素。我们认为，市场正在进入加快提供强大智能空间的时期，技术成为我们日常生活中不可或缺的一部分，无论这个我们是员工、客户、消费者、社区成员还是公民。

数字道德和隐私[ Digital ethics and privacy]

Consumers have an growing awareness of the value of their personal information, and they are increasingly concerned with how it’s being used by public and private entities. Enterprises that don’t pay attention are at risk of consumer backlash.

数字道德和隐私是个人、组织和政府日益关注的一个问题。人们越来越关注公共和私营部门的组织如何使用他们的个人信息，没有积极主动地打消这些顾虑的组织只会遇到越来越强烈的反对。

有关隐私的任何讨论都必须立足于数字道德以及客户、用户和员工的信任这个更广泛的话题上。虽然隐私和安全是建立信任的基本要素，但信任实际上不仅仅牵涉这些要素。信任是指在没有证据或调查的情况下认为陈述是真实的。最终，一家组织在隐私方面的立场取决于其在道德和信任方面更广泛的立场。由隐私转向道德使谈话的重心不仅仅围绕「我们是否合规」，而是转向「我们是否在做正确的事」。

量子计算[Quantum computing]

Quantum computing is a type of nonclassical computing that is based on the quantum state of subatomic particles that represent information as elements denoted as quantum bits or “qubits.”

量子计算是一种非经典计算，对亚原子粒子（比如电子和离子）的量子状态进行操作，这些粒子代表的信息就是由量子比特（qubit）表示的元素。量子计算机的并行执行和指数级可扩展性意味着它们擅长处理对于传统方法而言过于复杂的问题，或者传统算法需要很长时间才能找到解决方案的问题。汽车、金融、保险、制药和军事等行业以及研究机构有望从量子计算领域的进展获得最大的好处。比如在制药行业，量子计算可用于为原子层面的分子相互作用建模，从而缩短新型抗癌药的上市时间；量子计算可以加快分析并更准确地预测蛋白质的相互作用，因而开发出新的制药方法。

CIO和IT领导者应该开始为量子计算作规划，加深了解以及如何利用量子计算来解决实际的业务问题。在这项技术仍处于新兴状态时就要学习。找出量子计算大有潜力的实际问题，并考虑可能对安全带来的影响。但别相信量子计算在未来几年会彻底改变事物这种说法。大多数企业应该在2022年之前了解和关注量子计算，可能从2023年或2025年开始使用这项技术.

原文：

Blockchain, quantum computing, augmented analytics and artificial intelligence will drive disruption and new business models.

Although science fiction may depict AI robots as the bad guys, some tech giants now employ them for security. Companies like Microsoft and Uber use Knightscope K5 robots to patrol parking lots and large outdoor areas to predict and prevent crime. The robots can read license plates, report suspicious activity and collect data to report to their owners.

These AI-driven robots are just one example of “autonomous things,” one of the Gartner Top 10 strategic technologies for 2019 with the potential to drive significant disruption and deliver opportunity over the next five years.

“The future will be characterized by smart devices delivering increasingly insightful digital services everywhere,” said David Cearley, Gartner Distinguished Vice President Analyst, at Gartner 2018 Symposium/ITxpo in Orlando, Florida. “We call this the intelligent digital mesh.”

- - Intelligent: How AI is in virtually every existing technology, and creating entirely new categories.
  - Digital: Blending the digital and physical worlds to create an immersive world.
  - Mesh: Exploiting connections between expanding sets of people, businesses, devices, content and services.

“Trends under each of these three themes are a key ingredient in driving a continuous innovation process as part of the continuous next strategy,” Cearley said.

The Gartner Top 10 Strategic Technology trends highlight changing or not yet widely recognized trends that will impact and transform industries through 2023.

Trend No. 1: Autonomous things

Autonomous things exist across five types:

- - Robotics
  - Vehicles
  - Drones
  - Appliances
  - Agents

Those five types occupy four environments: Sea, land, air and digital. They all operate with varying degrees of capability, coordination and intelligence. For example, they can span a drone operated in the air with human-assistance to a farming robot operating completely autonomously in a field. This paints a broad picture of potential applications, and virtually every application, service and IoT object will incorporate some form of AI to automate or augment processes or human actions. Collaborative autonomous things such as drone swarms will increasingly drive the future of AI systems

Explore the possibilities of AI-driven autonomous capabilities in any physical object in your organization or customer environment, but keep in mind these devices are best used for narrowly defined purposes. They do not have the same capability as a human brain for decision making, intelligence or general-purpose learning.

Trend No. 2: Augmented analytics

Augmented analytics represents a third major wave for data and analytics capabilities as data scientists use automated algorithms to explore more hypotheses. Data science and machine learning platforms have transformed how businesses generate analytics insight.

“By 2020, more than 40% of data science tasks will be automated”

Augmented analytics identify hidden patterns while removing the personal bias. Although businesses run the risk of unintentionally inserting bias into the algorithms, augmented analytics and automated insights will eventually be embedded into enterprise applications.

Through 2020, the number of citizen data scientists will grow five times faster than professional data scientists. Citizen data scientists use AI powered augmented analytics tools that automate the data science function automatically identifying data sets, developing hypothesis and identifying patterns in the data. Businesses will look to citizen data scientists as a way to enable and scale data science capabilities. Gartner predicts by 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader use by citizen data scientists. Between citizen data scientists and augmented analytics, data insights will be more broadly available across the business, including analysts, decision makers and operational workers.

Trend No. 3: AI-driven development

- 1. The tools used to build AI-powered solutions are expanding from tools targeting data scientists (AI infrastructure, AI frameworks and AI platforms) to tools targeting the professional developer community (AI platforms, AI services). With these tools the professional developer can infuse AI powered capabilities and models into an application without involvement of a professional data scientist.
  2. The tools used to build AI-powered solutions are being empowered with AI-driven capabilities that assist professional developers and automate tasks related to the development of AI-enhanced solutions. Augmented analytics, automated testing, automated code generation and automated solution development will speed the development process and empower a wider range of users to develop applications.
  3. AI-enabled tools are evolving from assisting and automating functions related to application development (AD) to being enhanced with business domain expertise and automating activities higher on the AD process stack (from general development to business solution design).

The market will shift from a focus on data scientists partnered with developers to developers operating independently using predefined models delivered as a service. This enables more developers to utilize the services, and increases efficiency. These trends are also leading to more mainstream usage of virtual software developers and nonprofessional “citizen application developers.”

Trend No. 4: Digital twins

A digital twin is a digital representation that mirrors a real-life object, process or system. Digital twins can also be linked to create twins of larger systems, such as a power plant or city. The idea of a digital twin is not new. It goes back to computer-aided design representations of things or online profiles of customers, but today’s digital twins are different in four ways:

- 1. The robustness of the models, with a focus on how they support specific business outcomes
  2. The link to the real world, potentially in real time for monitoring and control
  3. The application of advanced big data analytics and AI to drive new business opportunities
  4. The ability to interact with them and evaluate “what if” scenarios

The focus today is on digital twins in the IoT, which could improve enterprise decision making by providing information on maintenance and reliability, insight into how a product could perform more effectively, data about new products and increased efficiency. Digital twins of an organization are emerging to create models of organizational process to enable real time monitoring and drive improved process efficiencies.

Trend No. 5: Empowered edge

“Technology and thinking will shift to a point where the experience will connect people with hundreds of edge devices”

Through 2028, Gartner expects a steady increase in the embedding of sensor, storage, compute and advanced AI capabilities in edge devices. In general, intelligence will move toward the edge in a variety of endpoint devices, from industrial devices to screens to smartphones to automobile power generators.

Build the AI Business Case

A CIO's guide to implementing AI in the enterprise

DOWNLOAD FREE E-BOOK

Trend No. 6: Immersive technologies

By 2022, 70% of enterprises will be experimenting with immersive technologies for consumer and enterprise use, and 25% will have deployed to production. The future of conversational platforms, which range from virtual personal assistants to chatbots, will incorporate expanded sensory channels that will allow the platform to detect emotions based on facial expressions, and they will become more conversational in interactions.

Eventually, the technology and thinking will shift to a point where the experience will connect people with hundreds of edge devices ranging from computers to cars.

Trend No. 7: Blockchain

Blockchain is a type of distributed ledger, an expanding chronologically ordered list of cryptographically signed, irrevocable transactional records shared by all participants in a network. Blockchain allows companies to trace a transaction and work with untrusted parties without the need for a centralized party (i.e., a bank). This greatly reduces business friction and has applications that began in finance, but have expanded to government, healthcare, manufacturing, supply chain and others. Blockchain could potentially lower costs, reduce transaction settlement times and improve cash flow. The technology has also given way to a host of blockchain-inspired solutions that utilize some of the benefits and parts of blockchain.

Pure blockchain models are immature and can bedifficult to scale. . However, businesses should begin evaluating the technology, as blockchain will create $3.1T in business value by 2030. Blockchain inspired approaches that do not implement all the tenets of blockchain deliver near term value but do not provide the promised highly distributed decentralized consensus models of a pure blockchain.

Read more: The CIO’s Guide to Blockchain

Trend No. 8: Smart spaces

Smart spaces are evolving alone five key dimensions: Openness, connectedness, coordination, intelligence and scope. Essentially, smart spaces are developing as individual technologies emerge from silos to work together to create a collaborative and interaction environment. The most extensive example of smart spaces is smart cities, where areas that combine business, residential and industrial communities are being designed using intelligent urban ecosystem frameworks, with all sectors linking to social and community collaboration.

Trend No. 9: Digital ethics and privacy

Conversations regarding privacy must be grounded in ethics and trust. The conversation should move from “Are we compliant?” toward “Are we doing the right thing?”

Governments are increasingly planning or passing regulations with which companies must be compliant, and consumers are carefully guarding or removing information about themselves. Companies must gain and maintain trust with the customer to succeed, and they must also follow internal values to ensure customers view them as trustworthy.

Trend No. 10: Quantum computing

Quantum computing is a type of nonclassical computing that is based on the quantum state of subatomic particles that represent information as elements denoted as quantum bits or “qubits.”

Quantum computers are an exponentially scalable and highly parallel computing model. A way to imagine the difference between traditional and quantum computers is to imagine a giant library of books.

While a classic computer would read every book in a library in a linear fashion, a quantum computer would read all the books simultaneously. Quantum computers are able to theoretically work on millions of computations at once. Quantum computing in the form of a commercially available, affordable and reliable service would transform some industries.

Real-world applications range from personalized medicine to optimization of pattern recognition. This technology is still in an emerging state, which means it is a good time for businesses to increase their understanding of potential applications and consider any security implications. Aside from a select group of businesses where specific quantum algorithms would provide a major advantage, most enterprises could remain in exploration phase through 2022 and begin exploiting the technology later.