Gartner的2019戰略性技術趨勢：量子計算、區塊鏈、AI

Gartner列出了企業和組織在2019年需要探究的十大戰略性技術趨勢：智能設備、增強分析、AI驅動的開發、數字孿生、邊緣計算、沉浸式體驗、區塊鏈、智能空間、數字道德和隱私、量子計算。

這十大科技趨勢被認為在未來5年將產生破壞性創新，并帶來商業機遇。無處不在的智能設備提供各種基于大數據的貼心服務，將是科技的未來。Gartner稱之為Intelligent Digital Mesh，其具體的定義如下：

Intelligent：AI將深入所有已有的垂直行業，并創造出新的行業。

Digital：物理世界和數字世界將被折疊，新的「沉浸」世界將會產生。

Mesh：人、生意、設備、內容、服務將連結成一個不斷擴張的大網。

分析師認為，上述三點覆蓋下的所有趨勢都將帶來持續的創新增量。以下是Gartner的2019年十大戰略性技術趨勢圖譜以及對技術趨勢的詳細介紹分析，供技術創業者們參考。

智能設備[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指利用人工智能技術代替人類完成任務的工具，無論自動駕駛車輛、機器人、無人機、智能化應用或自動化代理都屬于這一范疇。這五類設備覆蓋了四個維度：陸地、海洋、大氣及數字世界。五類應用、四個維度交織出多種可能，例如，在田間，無人機和農業機器人能夠互相配合完成耕種任務。Gartner認為，未來每個應用程序、服務、或者IoT設備都將包含某種程度的「智能」。盡管這類設備能否被稱為「智能」如今尚未達成共識，但不可否認的是，AI技術的確賦予了它們更優的與環境交互的能力、協調能力以及分析能力。

人們應該在實際業務中以及所使用的工具中不斷探索融入AI技術的可能性。但值得注意的是，這類技術目前只能勝任某些較窄的任務，并不像人腦一樣具備通用的決策能力，更遑論智力。

增強分析[?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.

增強分析側重于增強智能的特定領域，利用機器學習來徹底改變開發、使用和共享分析內容的方式。增強分析功能會迅速發展而得到主流采用，成為數據準備、數據管理、業務流程管理、流程挖掘和數據科學平臺的一項關鍵功能。增強分析自動獲得的洞察力也將嵌入到企業應用軟件中，比如人力資源、財務、銷售、營銷、客戶服務、采購和資產管理等部門的應用軟件，從而優化所有員工的決策和行動，而不僅僅是分析員和數據科學家的決策和行動。增強分析可使數據準備、洞察力獲取和洞察力可視化這個過程實現自動化，在許多情況下無需專業的數據科學家。

這將導致平民數據科學，這一套新興的功能和實踐使主要職責不是從事統計和分析工作的用戶也能夠從數據中獲取預測性和規范性的洞察力。到2020年，平民數據科學家數量的增長速度會比專家級數據科學家數量快五倍。企業組織可以利用平民數據科學家來填補數據科學家奇缺和高成本導致的數據科學和機器學習人才缺口。

AI驅動的開發[?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.

市場正迅速轉變，原來盛行這種方法：專業的數據科學家必須與應用軟件開發人員合作，共同開發大多數由AI增強的解決方案；現在流行這種模式：專業的開發人員可以單槍匹馬，使用作為一項服務而提供的預定義模型。這為開發人員提供了由AI算法和模型組成的生態系統，并提供了將AI功能和模型集成到解決方案中的定制開發工具。隨著AI運用于開發流程本身，使各種數據科學、應用軟件開發和測試功能實現自動化，專業應用軟件開發面臨另一批機會。到2022年，至少40%的新應用軟件開發項目會在團隊中有AI開發人員協同工作。

最終，高度先進的基于AI的開發環境使應用軟件的功能和非功能方面實現自動化，這將帶來‘平民應用軟件開發人員’新時代。在這個新時代，非專業人員將能夠使用AI驅動的工具自動生成新的解決方案。讓非專業人員無需編寫代碼就能生成應用軟件的工具普及，但我們預計AI驅動的系統會讓靈活性達到一個新的水平。

數字孿生[?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.?

數字孿生是指現實世界中的實體或系統的數字化表示。Gartner估計，到2020年將有超過200億個聯網的傳感器和端點；可能會有數十億個物件存在數字孿生。企業組織會一開始實施數字孿生，它們會不斷改進數字孿生，提升收集和可視化合適數據的能力，運用合適的分析工具和規則，并高效地應對業務目標。

數字孿生是物聯網之后的階段，一個方面體現為企業實施本組織的數字雙生（DTO）。DTO是一種動態軟件模型，它依賴操作數據或其他數據來了解組織如何實施業務模型，連接其當前狀態，部署資源，應對變化以提供預期的客戶價值。DTO有助于提高業務流程的效率，并且創建更靈活、更動態、更迅疾的流程，有望自動應對不斷變化的形勢。

邊緣計算[?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.

邊緣是指人們使用的端點設備或嵌入在我們周圍的端點設備。邊緣計算描述了這樣一種計算拓撲結構：信息處理和內容收集及傳遞更靠近這些端點。它試圖保持流量和處理本地化，目標是減少流量、縮短延遲。

在短期內，推動邊緣的是物聯網和這種需求：使處理接近端點，而不是在集中式云服務器上處理。然而目的不是打造一種新的架構，云計算和邊緣計算將作為互補模式而共同發展，云服務作為一種集中式服務加以管理，不僅在集中式服務器上執行，還在本地的分布式服務器和邊緣設備本身上面執行。

在今后五年，專用AI芯片以及更強大的處理能力、存儲和其他先進功能將被添加到種類更廣泛的邊緣設備上。這個嵌入式物聯網世界極具多樣性，加上工業系統等資產具有很長的生命周期，這將帶來管理方面的重大挑戰。從長遠來看，隨著5G日漸成熟，不斷擴展的邊緣計算環境會有更可靠的通信技術連回到集中式服務。 5G提供更低的延遲、更高的帶寬，并且每平方公里的節點（邊緣端點）數量急劇增加，最后一點對邊緣來說非常重要。

沉浸式體驗[?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.?

對話式平臺正在改變人們與數字世界互動的方式。虛擬現實、增強現實和混合現實正在改變人們感知數字世界的方式。感知模式和交互模式方面這種共同的轉變將造就未來的沉浸式用戶體驗。

隨著時間的推移，我們將從考慮單個設備和分散的用戶界面技術轉變為注重多渠道多模式體驗。多模式體驗將把人們與數字世界連接起來，周圍有成百上千的邊緣設備，包括傳統計算設備、可穿戴設備、汽車、環境傳感器和消費類電器。多渠道體驗不光使用這些多模式設備當中先進的計算機感官（比如熱量、濕度和雷達），還使用人類的所有感官。這種多體驗環境將營造一種環境體驗，其中我們周圍的空間將構成「計算機」，而不是單個設備構成「計算機」。確切的說，整個環境就是計算機。

區塊鏈[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).?

區塊鏈是一種分布式賬本，有望重塑各行各業，因為它能夠實現信任，提供透明度，減少業務生態系統之間的摩擦，因而可能降低成本，縮短交易結算時間，并改善現金流。今天，人們對銀行、票據交換所、政府及充當中央權威的許多其他機構寄予信任，「單一版本的真相」在它們的數據庫中安全地保管。集中式信任模式給交易增添了延遲和摩擦成本（傭金、手續費和貨幣的時間價值）。區塊鏈提供了另一種信任模式，無需負責仲裁交易的中央機構。

目前的區塊鏈技術和概念還不成熟，人們對它缺乏了解，而且在任務關鍵型規模化業務運營中未經證實。面對支持較復雜場景的復雜元素，尤其如此。盡管面臨挑戰，但區塊鏈具有強大的顛覆性潛力，這意味著CIO和IT領導者應該開始評估區塊鏈，即使他們在今后幾年并不積極采用這些技術。

如今許多區塊鏈項目并沒有實現區塊鏈的所有屬性，比如高度分布式的數據庫。這些受區塊鏈啟發的解決方案只是通過自動化業務流程或通過數字化記錄來實現運營效率的一種手段。它們有望加強已知實體之間的信息共享，并改善跟蹤并追蹤物理和數字資產的機會。然而，這些方法并沒有發揮區塊鏈真正顛覆的價值，可能加大廠商鎖定的風險。選擇這個方法的企業應了解限制因素，準備好逐步完成區塊鏈解決方案，還要明白這點：可以使用更高效、更優化地使用現有的非區塊鏈技術獲得相同的效果。

智能空間[?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.

智能空間是一種物理或數字環境，人員和技術支持的系統在日益開放、互聯、協調和智能的生態系統中彼此交互。多個要素（包括人員、流程、服務和物件）匯集在智能空間中，為目標人群和行業場景打造更沉浸式、更交互式、更自動化的體驗。

這個趨勢融合已有一段時間，圍繞智能城市、數字化工作場所、智能家居和聯網工廠等要素。我們認為，市場正在進入加快提供強大智能空間的時期，技術成為我們日常生活中不可或缺的一部分，無論這個我們是員工、客戶、消費者、社區成員還是公民。

數字道德和隱私[?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.

數字道德和隱私是個人、組織和政府日益關注的一個問題。人們越來越關注公共和私營部門的組織如何使用他們的個人信息，沒有積極主動地打消這些顧慮的組織只會遇到越來越強烈的反對。

有關隱私的任何討論都必須立足于數字道德以及客戶、用戶和員工的信任這個更廣泛的話題上。雖然隱私和安全是建立信任的基本要素，但信任實際上不僅僅牽涉這些要素。信任是指在沒有證據或調查的情況下認為陳述是真實的。最終，一家組織在隱私方面的立場取決于其在道德和信任方面更廣泛的立場。由隱私轉向道德使談話的重心不僅僅圍繞「我們是否合規」，而是轉向「我們是否在做正確的事」。

量子計算[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.”

量子計算是一種非經典計算，對亞原子粒子（比如電子和離子）的量子狀態進行操作，這些粒子代表的信息就是由量子比特（qubit）表示的元素。量子計算機的并行執行和指數級可擴展性意味著它們擅長處理對于傳統方法而言過于復雜的問題，或者傳統算法需要很長時間才能找到解決方案的問題。汽車、金融、保險、制藥和軍事等行業以及研究機構有望從量子計算領域的進展獲得最大的好處。比如在制藥行業，量子計算可用于為原子層面的分子相互作用建模，從而縮短新型抗癌藥的上市時間；量子計算可以加快分析并更準確地預測蛋白質的相互作用，因而開發出新的制藥方法。

CIO和IT領導者應該開始為量子計算作規劃，加深了解以及如何利用量子計算來解決實際的業務問題。在這項技術仍處于新興狀態時就要學習。找出量子計算大有潛力的實際問題，并考慮可能對安全帶來的影響。但別相信量子計算在未來幾年會徹底改變事物這種說法。大多數企業應該在2022年之前了解和關注量子計算，可能從2023年或2025年開始使用這項技術.

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

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 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.

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Trend No. 2: 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.

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

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:

• • 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.
  • 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.
  • 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.”

Read more:?How to Build a Business Case for Artificial Intelligence

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:

• • The robustness of the models, with a focus on how they support specific business outcomes
  • The link to the real world, potentially in real time for monitoring and control
  • The application of advanced big data analytics and AI to drive new business opportunities
  • 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

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.

“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.

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Trend No. 6: 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.?

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

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.

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

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.

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.?

Read more:?The CIO’s Guide to Quantum Computing

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.

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