Gartner又在吹牛逼？2017十大技術(shù)預(yù)測不見得都是真理 |雙語

原文作者：Steve Andriole

編譯：楊麗

Gartner上月剛剛對2017年十大戰(zhàn)略性技術(shù)趨勢進(jìn)行了預(yù)測，分別是：AI與高級機(jī)器學(xué)習(xí)；智能應(yīng)用；智能對象；VR與AR；數(shù)字孿生（Digital Twin）；區(qū)塊鏈和分布式總賬；會話系統(tǒng)；網(wǎng)格應(yīng)用與服務(wù)體系架構(gòu)；數(shù)字技術(shù)平臺以及自適應(yīng)安全架構(gòu)（Adaptive Security Architecture）。

與此同時(shí)，其他機(jī)構(gòu)也紛紛根據(jù)自己收集的數(shù)據(jù)對未來一年的行業(yè)趨勢進(jìn)行預(yù)測，其中，Villanova University的教授Steve Andriole就預(yù)測各行各業(yè)的公司機(jī)構(gòu)將在未來使用以下技術(shù)：

云計(jì)算（公有云&私有云）

機(jī)構(gòu)化與非結(jié)構(gòu)化大數(shù)據(jù)分析

數(shù)字化學(xué)習(xí)

BYOD自帶設(shè)備

移動應(yīng)用程序

可穿戴技術(shù)

定位技術(shù)

IOT物聯(lián)網(wǎng)

電子支付系統(tǒng)

數(shù)字安全技術(shù)（尤其是多層認(rèn)證）

值得注意的是：AI技術(shù)，自動化推理，以及深度學(xué)習(xí)技術(shù)并未出現(xiàn)在Steve Andriole的預(yù)測中。

下文具體解釋了Gartner的十大預(yù)測，而Steve Andriole對這些預(yù)測一一作出了點(diǎn)評。

趨勢一：AI與高級機(jī)器學(xué)習(xí)

人工智能（AI）和高級機(jī)器學(xué)習(xí)（ML）由許多科技和技術(shù)（如：深度學(xué)習(xí)、神經(jīng)網(wǎng)絡(luò)、自然語言處理（NLP）組成。更先進(jìn)的技術(shù)將超越基于規(guī)則的傳統(tǒng)算法，創(chuàng)造能夠理解、學(xué)習(xí)、預(yù)測、適應(yīng)，甚至可以自主操作的系統(tǒng)。Gartner認(rèn)為，到2020年前，AI將成為服務(wù)供應(yīng)商的主戰(zhàn)場。

評價(jià)：Gartner的預(yù)測顯然有些道理，但更大的問題是資金何時(shí)到位。如果是一家中大型企業(yè)，那么可將廣義上的AI技術(shù)定位為預(yù)測之一，并先行將預(yù)測中提及的應(yīng)用程序投入運(yùn)營。不過，關(guān)鍵是這種嵌入式的AI技術(shù)應(yīng)該如何產(chǎn)生。因?yàn)檫@個(gè)重點(diǎn)不能放在如何為大多數(shù)企業(yè)創(chuàng)造一個(gè)新型的智能應(yīng)用程序，除非你已經(jīng)是一家AI技術(shù)公司，否則，你的重點(diǎn)應(yīng)該在追蹤嵌入在應(yīng)用中的“智能”技術(shù)。這類技術(shù)能夠支持業(yè)務(wù)流程，模型，以及從一開始就被定義為企業(yè)中“智能”模塊的全新應(yīng)用。

趨勢二：智能應(yīng)用

Gartner分析師Cearley指出：“未來10年，幾乎每個(gè)應(yīng)用和服務(wù)都將包含一定的人工智能。這將成為一種長期發(fā)展趨勢，不斷發(fā)展和壯大人工智能和機(jī)器學(xué)習(xí)應(yīng)用和服務(wù)。

評價(jià)：贊同。目前，在應(yīng)用、基礎(chǔ)設(shè)施和設(shè)備上的智能分量越來越重，當(dāng)然，除非你是自動化推理或個(gè)人助理技術(shù)的創(chuàng)造者。Gartner預(yù)計(jì)，到2018年，全球最大的200家企業(yè)大多數(shù)都會使用智能應(yīng)用，并利用大數(shù)據(jù)的完整工具包和分析工具來完善自身的產(chǎn)品，改善用戶體驗(yàn)。大多數(shù)這種“使用”形式，將通過新的“智能”特性嵌入到某一行業(yè)中的現(xiàn)有應(yīng)用程序中。

趨勢三：智能物件

智能對象超越執(zhí)行剛性編程模型的物理物件，利用人工智能和機(jī)器學(xué)習(xí)實(shí)現(xiàn)高級行為，與周圍環(huán)境和人類更加自然地互動。隨著無人機(jī)、無人駕駛汽車和智能家電等智能物件不斷普及，Gartner預(yù)計(jì)單獨(dú)的智能物件將發(fā)展為協(xié)作的智能物件模型。

評價(jià)：這種預(yù)測實(shí)際上已經(jīng)滯后了。

趨勢四：VR和AR

虛擬與現(xiàn)實(shí)的融合是數(shù)字業(yè)務(wù)價(jià)值的主要驅(qū)動力。Cearley認(rèn)為：“到2021年，沉浸式消費(fèi)和商業(yè)內(nèi)容、應(yīng)用程序的市場格局將發(fā)生巨大變化。VR和AR功能將與數(shù)字格網(wǎng)合并，形成一個(gè)更加無縫的設(shè)備系統(tǒng)，提供超級個(gè)性化和互相關(guān)聯(lián)的應(yīng)用和服務(wù)，精心編排用戶收到的信息流?！?/p>

評價(jià)：實(shí)際上，虛擬現(xiàn)實(shí)和增強(qiáng)現(xiàn)實(shí)技術(shù)已經(jīng)出現(xiàn)，并投入使用了。

趨勢五：數(shù)字孿生

數(shù)字孿生指的是物理事物或系統(tǒng)的動態(tài)軟件模型，它依賴傳感器數(shù)據(jù)理解其狀態(tài)，對變化做出響應(yīng)，并改進(jìn)操作、增加價(jià)值。數(shù)字孿生包括一個(gè)由元數(shù)據(jù)、條件或狀態(tài)、事件數(shù)據(jù)和分析形成的組合。

評價(jià)：Gartner預(yù)測道：“三到五年內(nèi)，數(shù)以億計(jì)的物件將由數(shù)字孿生呈現(xiàn)，這是一套物理事物或系統(tǒng)的動態(tài)軟件模式?！钡珜?shí)際上，這并沒有太多新意。而需要更多關(guān)注的是：智能生態(tài)系統(tǒng)的內(nèi)容、廣度和控制將繼續(xù)從不同維度上進(jìn)行擴(kuò)展。例如，IOT和可穿戴設(shè)備均通過分析進(jìn)行連接、推理并變得“智能”。

趨勢六：區(qū)塊鏈和分布式總賬

區(qū)塊鏈?zhǔn)且环N分布式分類賬，由價(jià)值交換交易（以比特幣或其他代幣計(jì)算）按順序分組成塊。每個(gè)塊鏈接到前一個(gè)塊，使用加密的信任和保證機(jī)制，在對等網(wǎng)絡(luò)進(jìn)行記錄。

評價(jià)：實(shí)際上，人們并不十分了解區(qū)塊鏈技術(shù)。盡管區(qū)塊鏈有巨大的潛力，被廣泛認(rèn)為是一種重要技術(shù)，但是如何能更為廣泛的采用則十分具有挑戰(zhàn)性。不過，事實(shí)上，區(qū)塊鏈初創(chuàng)公司手頭握有大量資金，需要比預(yù)期得更久才能獲得技術(shù)的成熟和部署。而值得懷疑的是，需不需要等到2022年。像集中式和分布式集成，以及傳統(tǒng)架構(gòu)的兼容性等問題將持續(xù)存在。

因此，區(qū)塊鏈的實(shí)施方需要進(jìn)行深入合作，這深受加密貨幣交易過程的重視。區(qū)塊鏈企業(yè)遇到的下一個(gè)業(yè)務(wù)挑戰(zhàn)則是“市場份額”，也就是說，區(qū)塊鏈這個(gè)行業(yè)將演變成為各方割據(jù)或一家獨(dú)大的局面。會出現(xiàn)下一個(gè)“紅帽”公司，還是很多“Hadoop”技術(shù)公司?總有一家公司會嘗到區(qū)塊鏈技術(shù)的甜頭。

趨勢七：會話系統(tǒng)

目前，會話界面的重點(diǎn)是聊天機(jī)器人和支持麥克風(fēng)的設(shè)備。然而，數(shù)字格網(wǎng)擁有一系列不斷擴(kuò)展的端點(diǎn)，人們通過這些端點(diǎn)訪問應(yīng)用程序和信息，或與他人、社交群體、政府和企業(yè)進(jìn)行互動。設(shè)備網(wǎng)已不再局限于傳統(tǒng)的臺式計(jì)算機(jī)和移動設(shè)備，它涵蓋人類可能與之交互的各類端點(diǎn)。

評價(jià)：目前，語音對系統(tǒng)界面非常重要。越來越智能、越來越交互的語音接口，將是“用戶”與“機(jī)器”交互的首選方式。未來的交互模式是一種主動性的對話，這進(jìn)一步增強(qiáng)語音交互。主動式語音協(xié)助，由用戶位置等數(shù)據(jù)進(jìn)行支持，將會在沒有用戶/機(jī)器問答的情況下也能指導(dǎo)和管理。會話語音的價(jià)值也會隨著語義分析的加重而增加，該語義分析能夠理解用戶服務(wù)記錄和流媒體社交媒體的各類數(shù)據(jù)，并進(jìn)行推理。

趨勢八：網(wǎng)格應(yīng)用和服務(wù)架構(gòu)

在格網(wǎng)應(yīng)用程序和服務(wù)架構(gòu)（MASA）中，移動應(yīng)用程序、網(wǎng)絡(luò)應(yīng)用程序、桌面應(yīng)用程序和物聯(lián)網(wǎng)應(yīng)用程序?qū)㈡溄拥綇V泛的后端服務(wù)網(wǎng)，創(chuàng)建被用戶視為“應(yīng)用程序”的內(nèi)容。

評價(jià)：目前，在非理想的市場競爭中，專有基礎(chǔ)架構(gòu)數(shù)量上不占優(yōu)勢，那么所有的軟硬件基礎(chǔ)架構(gòu)進(jìn)行無縫集成和協(xié)同，難度不小。

趨勢九：數(shù)字技術(shù)平臺

Gartner確定了實(shí)現(xiàn)數(shù)字業(yè)務(wù)新功能和商業(yè)模式的五個(gè)要點(diǎn)——信息系統(tǒng)、客戶體驗(yàn)、分析和智能、物聯(lián)網(wǎng)和業(yè)務(wù)生態(tài)系統(tǒng)。每個(gè)企業(yè)都將建立一些由這五個(gè)數(shù)字技術(shù)平臺組成的平臺。這些平臺為數(shù)字業(yè)務(wù)提供基本的構(gòu)建塊，將成為數(shù)字業(yè)務(wù)的關(guān)鍵推動力。

評價(jià)：數(shù)字技術(shù)平臺不斷涌現(xiàn)出新事物，如分析，物聯(lián)網(wǎng)，可穿戴設(shè)備，定位系統(tǒng)和云計(jì)算。但同時(shí)要謹(jǐn)記，所有這些事物并非割裂，而是相互交織。隨著越來越多的基礎(chǔ)架構(gòu)和應(yīng)用程序開始融合，企業(yè)必須了解這種融合如何影響他們的競爭力和應(yīng)變能力。

趨勢十：自適應(yīng)安全架構(gòu)

Cearley認(rèn)為：“成熟的安全技術(shù)應(yīng)作為確保物聯(lián)網(wǎng)平臺的基準(zhǔn)。監(jiān)控用戶和實(shí)體行為是物聯(lián)網(wǎng)中尤其必要的一項(xiàng)重要補(bǔ)充，然而，物聯(lián)網(wǎng)邊界是許多IT安全專業(yè)人員產(chǎn)生薄弱領(lǐng)域的新前沿，因而經(jīng)常需要新的補(bǔ)救工具和流程，而建立物聯(lián)網(wǎng)平臺必須將這些因素考慮在內(nèi)。”

評價(jià)：Villanova大學(xué)的數(shù)據(jù)同樣強(qiáng)調(diào)了數(shù)字安全的重要性。業(yè)務(wù)安全層面包括：審計(jì)、合規(guī)、政策和程序，還有安全技術(shù)本身。不過，業(yè)務(wù)端滯后于技術(shù)端，而技術(shù)端則滯后于設(shè)備和應(yīng)用端。所有的安全架構(gòu)必須不斷適應(yīng)，同時(shí)要認(rèn)識到安全行業(yè)本質(zhì)上是一個(gè)響應(yīng)機(jī)構(gòu)，旨在解決問題，并不在意這個(gè)問題存在了多久。

綜上所述，Gartner給出的十大技術(shù)性趨勢預(yù)測還有待商榷。一是，這些技術(shù)預(yù)測的呈現(xiàn)方式很抽象。換句話說，抽象的呈現(xiàn)方式很難引起爭議和討論。二是，這些預(yù)測和很多其他的預(yù)測有雷同，比如說，誰不把AI作為潛在的重要技術(shù)呢？三是，這些預(yù)測很大程度上不具有可操作性，也就是說，并沒有說明下一步如何進(jìn)行。

除此之外，如果追蹤、評估或測試這些技術(shù)的話，那么最為可能影響企業(yè)中的規(guī)則、流程和整個(gè)業(yè)務(wù)模式。而Villanova大學(xué)做出的技術(shù)預(yù)測則是根據(jù)公司在多個(gè)行業(yè)中試驗(yàn)所得，并非只是對技術(shù)的追蹤和總結(jié)。公司應(yīng)該追蹤、評估、測試且衡量關(guān)于：新興或潛在的破壞性技術(shù)可能對新產(chǎn)品開發(fā)、競爭定位、收入甚至盈利能力帶來的影響。因此，所謂梳理總結(jié)出的預(yù)測，一定要有目標(biāo)定位，而非僅限于推測。

英文原文：

Gartner’s Top 10 Strategic Technology Trends for 2017 were recently published. They are insightful, but predictable. They’re also somewhat out of sync with data we – and others – have recently collected.

Let’s look at the top 10 strategic technology trends for 2017 by first noting some findings we discovered from data collected in 2016 at Villanova University. We discovered that companies across many industries plan to pilot the following (rank-ordered) technologies:

Cloud Computing (Public & Private)

Structured & Unstructured Big Data Analytics

eLearning

BYOD (Bring Your Own Device)

Mobile Applications

Wearable Technologies

Location-Based Technologies

Internet-of-Things (IOT)

ePayment Systems

Digital Security Technologies (Especially Multilayer Authentication)

These are the technologies and technology clusters that companies actually plan to pilot in 2016 and 2017. Noticeably absent from the list is AI/automated reasoning/deep learning – the first technology on the Gartner list.

With this as backdrop, let’s look at what Gartner is telling us.

Trend #1: AI & Advanced Machine Learning

Gartner is clearly on to something here, but the larger question is about investment timing. If I were running a large or mid-sized company I’d place AI (broadly defined) on every watch list I could find and pilot the most promising applications that appeared on the list. The key point, however, is just how embedded AI has become. The emphasis should not be on creating new, smart applications for most companies (unless you’re an AI company), but to track the growing intelligence embedded in the applications?available to enable their business processes and models, as well as whole new applications that were developed from the outset to be “smart” in your industry.

#2: Intelligent Apps

Same thing here: it’s about the growing intelligence within applications, infrastructures and devices (unless, of course, you’re one of the creators of automated reasoning or personal assistant technologies). But I just have to ask, does anyone doubt this observation?: “By 2018, Gartner expects most of the world’s largest 200 companies to exploit intelligent apps and utilize the full toolkit of big data and analytics tools to refine their offers and improve customer experience.” ?Most of this “exploitation” will occur via new “intelligent” features embedded in existing applications in specific industries.

#3: Intelligent Things

The prediction here feels more like a trailing than a tracking indicator.

#4: Virtual & Augmented Reality

Yes, virtual and augmented realties are already here – and working.

#5: Digital Twins

While I love the “digital twins” metaphor, there’s not much new here: “within three to five years, billions of things will be represented by digital twins, a dynamic software model of a physical thing or system.” The larger issue is the content, breadth and control of the intelligent ecosystem – which will continue to expand in multiple directions. IOT and wearables, for example, are about connectivity, inference and intelligence via analytics.

#6: Blockchain

Blockchain is here to stay. We just don’t yet know where, when or for how long. As I said last week, “while blockchain has enormous potential, there are challenges to widespread adoption. While it’s true that there’s a lot of funding for blockchain start-ups, it’s also true that we’ve seen technologies perceived as broadly important as blockchain take longer to mature and deploy than anticipated, though I doubt we’ll have to wait until 2022 for clarity. Issues like centralized versus distributed integration and legacy architecture compatibility will persist; there will be significant required cooperation among implementation parties for blockchain to become core to many flavors of transaction processing (beyond cryptocurrency). The business challenge for blockchain companies will be “market share”: will it be open and diffuse – “neutral” – or will it be aggressively proprietary or “closed.” Will a Redhat emerge or will there be countless Hadoops? Someone will win the blockchain sweepstakes.”

#7: Conversational Systems

Voice is the killer interface of the early 21st century. Others are in the pipeline but voice interfaces – especially if they’re intelligent and conversational (which they increasingly are) – will be the preferred way “users” interact with their “machines.”?One of interaction models that will emerge – and will further empower voice interaction – is proactive (versus reactive) conversations. Proactive voice assistance – enabled by location and other data – will direct and manage without user/machine Q&A. The value of conversational voice will also increase by semantic analyses capable of understanding and inferring from all kinds of data including customer service records and streaming social media.

#8: Mesh App and Service Architecture

Seamless integration and interoperability across all hardware and software architectures (in a non-standard, competitive marketplace) remains the goal – which is challenging given the number or proprietary architectures out there.

#9: Digital Technology Platforms

Digital technology platforms are emerging around emerging around new centers of activity, like analytics, IOT, wearables, location-based systems and, of course, cloud computing. But remember that all of these (and other) activity clusters intersect more than they standalone. More and more infrastructures and applications are converging and companies must understand how that convergence affects their ability to pivot and compete.

#10: Adaptive Security Architecture

Our data at Villanova also underscores the importance of digital security. There’s the business of security – audits, compliance, policies and procedures – and the security technology itself. The business side lags the technology side (which lags the device and applications sides). All security architectures must continuously adapt while recognizing that the security industry is essentially a reactive one destined to chase solutions to problems it had no idea existed an hour, a day or a week ago.

There are several problems with lists like these. First, they are presented at a very high level of abstraction. Put another way, they are high-level and therefore more difficult to dispute or even discuss. Second, they are similar to many, many other lists: who doesn’t have “AI” on their list of potentially important technologies? Finally, these kinds of lists are seldom “actionable”: very little guidance is provided to readers about exactly what to do next? A reasonable question would be, “if these technologies are so important what should I be doing right now?”

The answer to the last question is track/assess/pilot the technologies most likely to impact business rules, processes and whole business models in your industry. The ten technologies we discovered at Villanova are technologies that companies plan to pilot, not just track, across multiple industries. While tracking is interesting and sometimes fun, companies should track, assess, pilot and measure the impact that emerging and potentially disruptive technologies might have on their new product development, competitive positioning, revenue and profitability, among other objectives. Lists should be purposeful, not just speculative.

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