觀世界 | 全球知名企業(yè)高管預測2019人工智能趨勢

來源：數(shù)據(jù)觀 時間：2018-12-20 15:19:04 作者：Gil Press

20 More AI Predictions For 2019

數(shù)據(jù)觀|（編譯）

Artificial intelligence (AI) is everywhere, driven by large investments, lots of startups, all established technology vendors, and enterprises big and small experimenting with what it can do for their bottom line.?

人工智能(AI)無處不在，在大型投資、大量初創(chuàng)公司、所有成熟的技術供應商以及大大小小的企業(yè)的推動下，他們都在試驗AI能為他們的利潤做些什么。

?

“Some AI Applications will not live up to the hype, and that's OK.?People have been planning to have self-driving cars for a while. Some still fear an AI take over might be just 20 years away but the truth is we're still a long way away from truly autonomous cars. And as for an AI takeover, that will only exist in SciFi movies for the foreseeable future. My prediction is that our expectations for AI and the reality of its capability will meet somewhere in the middle. The next 5 years will look a lot like they do now, but our day-to-day will become more and more efficient in subtle, yet significant, ways. AI bots will get better at answering questions and vetting customer service cases, smart assistants will be more equipped to complete tasks and self-driving car features will continue to improve, but they will not take over the road”—Richard Socher, Chief Scientist,?Salesforce

“一些人工智能應用會辜負炒作，但這沒關系。人們計劃擁有自動駕駛汽車已經(jīng)有一段時間了。一些人仍然擔心人工智能可能在20年后將會接管，但事實是，我們距離真正的自動駕駛汽車還有很長的路要走。至于人工智能的收購，在可預見的未來，這只會出現(xiàn)在科幻電影中。我的預測是，我們對人工智能的期望和它的能力的現(xiàn)實將在中間的某個地方達到。未來5年看起來和現(xiàn)在很像，但我們的日常生活將變得越來越有效率，以微妙但重要的方式。人工智能機器人將在回答問題和審查客戶服務案例方面變得更好，智能助手將擁有更多完成任務的設備，自動駕駛汽車的功能將繼續(xù)改進，但它們不會取代道路”

——Salesforce首席科學家Richard Socher。

“The adoption of artificially intelligent offerings will continue to scale into different verticals from manufacturing to education, retail and more in 2019. In the healthcare sector, for example, AI-enhanced applications have the capability to reduce emergency waiting room times and even free up doctors’ time through the use of AI in detecting and diagnosing tumors. As new advances and applications make their way into various verticals, expect to see accelerated adoption as technology costs come down and organizational and business outcomes improve. At Lenovo, we’re already using AI in our supply chain and parts planning process so that we can better develop best in class experience for customers also keen to transform their business with artificial intelligence”—Gianfranco Lanci, Corporate President and Chief Operating Officer,?Lenovo

“2019年，人工智能產(chǎn)品的應用將從制造業(yè)繼續(xù)擴展到教育、零售等各個垂直領域。例如，在醫(yī)療行業(yè)，人工智能增強的應用程序能夠通過使用人工智能來檢測和診斷腫瘤，減少急診候診室的時間，甚至解放醫(yī)生的時間。隨著新的科學進展和應用程序進入各個垂直領域，技術成本的降低與組織和業(yè)務結果的改善，我們預計這將加速人工智能的采用度。在聯(lián)想，我們已經(jīng)在供應鏈和零部件規(guī)劃過程中使用了人工智能，以便更好地為那些同樣渴望使用人工智能改造業(yè)務的客戶提供一流的體驗”

——聯(lián)想公司總裁兼首席運營官Gianfranco Lanci。

“Patients will find themselves talking via a variety of omni-channel UIs in addition to the pre-existing chat bots that are currently available on mobile apps and other health care IT platforms. Consumer frameworks for conversational experiences like Alexa and Google Home may add HIPAA privacy support that opens the gates for bots to keep the dialog going during the big gaps in time between patient visits. In care settings, nurse call buttons beside hospital beds, forms to collect health histories, and klunky scheduling apps will evolve into customer-focused robot medical assistants”—Dan Housman, chief technology officer for ConvergeHEALTH,?Deloitte

“患者會發(fā)現(xiàn)，除了現(xiàn)有的移動端APP和其他互聯(lián)網(wǎng)醫(yī)療平臺的聊天機器人之外，他們還可以通過各種全通道用戶界面進行醫(yī)患交談。像Alexa和Google Home這樣的會話體驗，用戶框架或許可以添加HIPAA（《健康保險流通與責任法案》）隱私支持，這將為機器人打開一扇大門，以便在患者在前后訪問的間隔時間內(nèi)保持適時溝通。在護理機構，醫(yī)院病床旁的護士呼叫按鈕、病歷表格以及殘舊的排程應用程序，都將發(fā)展成為以客戶為中心的機器人醫(yī)療助手?！?/p>

——?德勤(Deloitte)總經(jīng)理兼德勤醫(yī)療系統(tǒng)項目ConvergeHEALTH首席技術官Dan Housman。

“2019 will see the pendulum shift to a focus on performing analytics at the edge. Organizations will save time and money by processing and analyzing data at the edge versus moving it back to a core, storing it and applying traditional analytics. Use cases include anomaly detection (fraud), pattern recognition (predicting failures/maintenance) and persistent streams. Autonomous vehicles, Oil and gas platforms, medical devices are all early examples of this trend that we will see expand in 2019. Cost drivers for this trend are bandwidth (semi-connected environments as well as expensive cellular) considerations and storage (reduce the amount of data sent to the cloud)”—Jack Norris, Senior Vice President, Data and Applications,?MapR

“2019年我們將看到鐘擺式的轉折——聚焦邊緣分析的執(zhí)行。與其傳統(tǒng)地將數(shù)據(jù)移回核心存儲、應用，企業(yè)更青睞于邊緣處理、分析數(shù)據(jù)以節(jié)省時間和成本。其用例包括異常檢測(欺詐)、模式識別(故障預測/維護)和持久流。自動駕駛汽車、油氣平臺、醫(yī)療設備都是這一趨勢的早期例子，我們將在2019年看到這一趨勢的擴展。這一趨勢的成本驅(qū)動因素是帶寬(半連接環(huán)境以及昂貴的蜂窩網(wǎng)絡)和存儲(減少發(fā)送到云端的數(shù)據(jù)量)”

——開源大數(shù)據(jù)技術公司MapR數(shù)據(jù)與應用程序高級副總裁Jack Norris。

“Public demands for responsible AI will increase.?2018 was the year of awakening. 2019 will be the year of action. It won't be just data ethicists and human rights advocates demanding fairness, accountability, and transparency.?Consumers are already changing how they use Facebook?or deleting their accounts altogether and this trend is likely to spread to other social media and other services that leverage personal data. Greater numbers of pledges and declarations about the responsible creation and use of AI will be written and companies will be pressured to adopt them. The public will fight back over government use of biased AI in decisions impacting human rights. More employees will demand influence over what they create and refuse to contribute to harmful automation. Companies will have to lead with their conscious-- whether they are buying AI solutions or building them-- and seek assurances that the systems are fair in order to avoid being the next headline on AI gone awry”—Kathy Baxter, Architect of Ethical AI Practice,?Salesforce

“公眾對可靠人工智能的需求將會增加。如果說2018年是AI覺醒之年，那2019年就將成為行動之年。不僅是數(shù)據(jù)倫理學家和人權倡導者要求公平、問責和透明，消費者已經(jīng)在改變他們使用Facebook的方式，或者干脆刪除他們的賬戶，這種趨勢可能會蔓延到其他通過注冊個人數(shù)據(jù)信息來登錄的社交媒體和其他服務。更多關于有責創(chuàng)造和使用人工智能的承諾書及聲明將形成書面文件，企業(yè)將被迫采用遵循，公眾也將對政府在影響人權的決策中使用有偏見的人工智能進行反擊。更多員工將要求對他們所創(chuàng)造的東西施加影響，并拒絕為有害的自動化做出貢獻。企業(yè)將不得不以自己的意識為先導——無論是購買人工智能解決方案還是建立人工智能，都要確保這些系統(tǒng)是合法的，以避免成為下一個‘問題AI’頭條?！?/p>

——Salesforce倫理AI實踐架構師Kathy Baxter。

“Advanced?analytics and?artificial intelligence?will continue becoming more highly focused and purpose-built for specific needs, and these capabilities will increasingly be embedded in management tools. This much-anticipated capability will simplify IT operations, improve infrastructure and application robustness, and lower overall costs. Along with this trend, AI and analytics will become embedded in high availability and disaster recovery solutions, as well as cloud service provider offerings to improve service levels. With the ability to quickly, automatically and accurately understand issues and diagnose problems across complex configurations, the reliability, and thus the availability, of critical services delivered from the cloud will vastly improve”—Jerry Melnick, President and CEO,?SIOS Technology

“高級分析和人工智能將繼續(xù)變得更加集中，并為特定的需求而專門構建，這些功能將越來越多地嵌入管理工具中。這個備受期待的功能將簡化IT操作，改進基礎設施和應用程序穩(wěn)健性，并降低總體成本。隨著這一趨勢的發(fā)展，人工智能和分析技術將被嵌入到高可用性和故障恢復解決方案中，以及云服務提供商提供的提高服務水平的產(chǎn)品中。由于能夠快速、自動和精確地理解問題，跨越復雜配置診斷問題，云端關鍵服務的可靠性和可用性將極大提高?！?/p>

——SIOS Technology總裁兼首席執(zhí)行官Jerry Melnick。

“As chatbots and AI continue to evolve, the depth and breadth of functions they can perform will increase. What does this mean for the workforce, positively and negatively? On one hand, machine learning will help people sift through massive amounts of data and do their jobs more effectively. On the other, customer service and support roles will be phased out as people grow more comfortable with bot interactions. This will begin to occur on a wider scale in 2019, as more enterprises adopt AI and chatbots to either boost productivity among their existing workforce, or phase out positions that can be taken with the assistance of these technologies”—David Cohn, Co-founder and Chief Strategy Officer,?Pigeon

“隨著聊天機器人和人工智能的不斷發(fā)展，它們能夠執(zhí)行的功能深度和廣度將會增加。這對勞動力意味著什么，積極還是消極?一方面，機器學習將幫助人們篩查大量數(shù)據(jù)，并更有效地完成工作。另一方面，隨著人們對機器人交互越來越熟悉，客戶服務和售后角色將逐步消失。到2019年，隨著越來越多的企業(yè)將采用人工智能和聊天機器人來提高現(xiàn)有員工的生產(chǎn)率，或者逐步淘汰這些技術可以替代的職位，這種情況將大范圍出現(xiàn)。”

—— Pigeon聯(lián)合創(chuàng)始人兼首席戰(zhàn)略官David Cohn。

“A dirty little secret about industrial-strength AI is that many of these systems are trained and evaluated on datasets created and labeled by thousands (or more) human raters. As we tackle more complex AI problems, the need for massive amounts of high-quality human judgments will increase, but there will be breakthroughs in leveraging machine learning techniques to make collecting those judgments more time- and cost-efficient. At the same time, methods which use minimal or even no labeled data (aka unsupervised techniques) will reduce our reliance on large swaths of labeled data, enabling deep learning models to be more robust on new and different types of problems”

—Joel Tetreault, Head of Research,?Grammarly

“關于工業(yè)級人工智能的一個不可告人之處是，這些系統(tǒng)多數(shù)是由成千上萬甚至更多人類評分者創(chuàng)建和標記的數(shù)據(jù)集進行培訓和評估的。隨著我們解決更復雜的人工智能問題，對大量高質(zhì)人類判斷的需求將會增加，但在利用機器學習省時省錢收集這些判斷方面將會出現(xiàn)突破。與此同時，使用最少甚至不使用標記數(shù)據(jù)的方法(又稱無監(jiān)督技術)將減少我們對大量標記數(shù)據(jù)的依賴，從而使深度學習模型能夠更穩(wěn)健地處理新的、不同類型的問題?！?/p>

——Grammarly研究主管Joel Tetreault。

“Knowledge Graphs are the new black! The technologies needed – NLP, Graph DB, Content Analytics – are now aligned to enable knowledge graphs to easily codify domain knowledge. From usable chatbot, guided processes to automated advisors, we’ll see increased use in many industries and domains, including healthcare, financial services, and supply chain”—Jean-Luc Chatelain, Managing Director & Chief Technology Officer,?Accenture Applied Intelligence

“知識圖是新的黑馬!所需的技術——NLP、圖形數(shù)據(jù)庫、內(nèi)容分析——現(xiàn)在已看齊以使知識圖能夠輕松地編碼領域知識。從可用的聊天機器人、引導流程到自動化顧問，我們將看到他們在眾多行業(yè)和領域里的使用率增加，包括醫(yī)療保健、金融服務和供應鏈?！?/p>

——Accenture Applied Intelligence總經(jīng)理兼首席技術官Jean-Luc Chatelain。

“AI has?moved into the mainstream?with innovations in?self-driving cars, smart speakers,?and?facial recognition.?Less visible, yet equally impactful, are AI applications around logistics, manufacturing, healthcare, and cybersecurity.? And what makes cybersecurity unique is that it’s an essential component of all the other?technologies.?Whether we choose to live in an ‘intelligent’ or an ‘a(chǎn)rtificially intelligent’ world, one thing is certain:?If AI and deep learning isn’t enhancing your cybersecurity strategy, you’re far more likely to get hacked.?AI makes it considerably more difficult for cybercriminals to earn their disreputable income.? With?an AI-powered defense, attackers are left to seek out softer targets (those who don’t think they need AI) or they’re forced to develop even more sophisticated and costly methods of attack – and so the cyber arms race continues”—Joe Levy, CTO,?Sophos

“隨著自動駕駛汽車、智能揚聲器和面部識別技術的創(chuàng)新，人工智能已進入主流。雖然人工智能在物流、制造、醫(yī)療保健和網(wǎng)絡安全方面的應用不那么引人注目，但同樣具有影響力。網(wǎng)絡安全的獨特之處在于它是所有其他技術的重要組成部分。無論我們選擇生活在一個“智能”還是“人工智能”的世界，有一件事是肯定的:如果人工智能和深度學習不能增強你的網(wǎng)絡安全戰(zhàn)略，你就更有可能被黑客攻擊。人工智能讓網(wǎng)絡犯罪分子更難獲得聲名狼藉的收入。有了人工智能的防御，攻擊者只能尋找更容易的目標(那些認為自己不需要人工智能的目標)，或者被迫開發(fā)出更復雜、成本更高的攻擊方法——因此，網(wǎng)絡軍備競賽仍在繼續(xù)?！?/p>

——Sophos首席技術官Joe Levy。

“AI is entering the Age of Commodity.?You do not need to know how the technology of a microwave works in order to use it, it is simply a tool. With the huge influx of no-code, point-and-click tools we are entering into the same phase with AI where it will become a widely used utility by everyone, regardless of technical background. As a result, most of the AI applications in the coming years will be built by people with little or no AI training”—Vitaly Gordon, VP Data Science,?Salesforce

“人工智能正在進入商品時代。你不需要知道微波技術如何工作才能使用它，它只是一個工具。隨著大量無代碼、點擊式工具的涌入，我們正進入與人工智能相同的階段，在這個階段，無論技術背景如何，它都將成為一種廣泛使用的實用工具。因此，未來幾年的大部分人工智能應用程序?qū)⒂珊苌倩驔]有人工智能培訓的人開發(fā)?！?/p>

——Salesforce數(shù)據(jù)科學副總裁Vitaly Gordon。

“Robotic Process Automation (RPA) has been one of the hottest areas of tech in the last two years – because of its simple, easy-to-understand value prop – process automation, efficiency; freeing resources up to focus on higher value activities, etc.?But It has fundamental limits – it’s only effective with rote, repetitive processes and it cannot impact workflows involving unstructured content which makes up over 80% of data in most enterprises.?At the same time, AI and machine learning are seen as too esoteric; requiring too much data science expertise, too much hand-holding, too much uncertainty and risk about ROI. Companies will look to bridge the gap in 2019 – between the horsepower of RPA and the intellect of AI/machine learning through what many experts are calling ‘intelligent process automation”—Tom Wilde, CEO and Founder,?Indico

“機器人流程自動化(RPA)在過去兩年中一直是最熱門的技術領域之一——因為其簡單、易于理解的價值主張——流程自動化、效率高，釋放資源以專注于更有價值的活動等等。但它有基本的局限性——它只對死記硬背、重復的流程有效，不會影響涉及非結構化內(nèi)容的工作流，在大多數(shù)企業(yè)中，非結構化內(nèi)容占數(shù)據(jù)的80%以上。與此同時，人工智能和機器學習被視為過于深奧，需要太多的數(shù)據(jù)科學專業(yè)知識、太多扶持、太多關于利潤率的不確定性和風險。2019年，各公司有望通過專家們提出的“智能流程自動化”，在RPA馬力和人工智能/機器學習之間架起一座橋梁?！?/p>

——?Indico首席執(zhí)行官和創(chuàng)始人Tom Wilde。

“Artificial Intelligence (AI) and?machine?learning (ML) are?overhyped for many real-life applications, including the contact center industry.?For example, instead of trying to identify specific patterns in images or data (an AI/ML sweet spot), it will be much more useful to increase the volume of satisfying self-service support sessions through intelligently applied automation to resolve common questions and provide guided user flows through defined business processes. By leveraging human intelligence primarily for those support scenarios that can’t be effectively automated, call center operations will be further optimized”—Anand Janefalkar, Founder and CEO,?UJET

“人工智能(AI)和機器學習(ML)在許多實際應用中被夸大了，包括呼叫中心行業(yè)。例如，與其試圖識別圖像或數(shù)據(jù)中的特定模式(AI/ML的最佳點)，不如通過智能應用自動化來解決常見問題并通過定義的業(yè)務流程提供指導用戶流程，從而增加滿足自助服務支持會話的數(shù)量。通過主要利用人類智能來支持那些不能有效自動化的場景，呼叫中心的操作將得到進一步優(yōu)化。”

——?UJET創(chuàng)始人和首席執(zhí)行官Anand Janefalkar。

“In 2019, there will be a shift from AI toolkits to AI solving specific enterprise challenges, such as IT and human resources employee experiences. To date, the model has been that enterprises can apply hard-to-come-by skills to leverage AI toolkits to build a custom application. This is shifting to using AI to solve common enterprise problems”—Pat Calhoun, Founder and CEO,?Espressive

“2019年，將會有一個從人工智能工具包到人工智能解決具體企業(yè)挑戰(zhàn)的轉變。如IT和人力資源員工的經(jīng)驗，迄今為止，模型成了企業(yè)利用AI工具包構建自定義應用程的難獲技術。這正轉向使用AI來解決常見的企業(yè)問題?！?

——Espressive創(chuàng)始人和首席執(zhí)行官Pat Calhoun。

“In 2019, AI’s early adopters in the enterprise will look to gain more value from their AI investments as they expect more abundant and richer, built-in AI solutions within cloud applications in terms of functionality, user experience, and accessibility (multi-device, chatbots, digital assistants, etc.).? We’ll see companies investing in third party data sources and smart data (dynamic signals and flexible classifications that are regularly refreshed) to optimize outputs. Trust, transparency, and explainable AI will become bigger issues as organizations wrestle with machine learning bias. Customers will realize that machine learning requires human supervision and features like supervisory controls, coupled with data insights, to help early adopters tweak machine learning outputs and generate more value from AI investments”—Melissa Boxer, VP of Adaptive Intelligent Applications,?Oracle

“2019年，人工智能在企業(yè)中的早期采用者希望從他們的人工智能投資中獲得更多價值，因為他們希望云應用程序中內(nèi)置的人工智能解決方案在功能、用戶體驗和可訪問性(多設備、聊天機器人、數(shù)字助理等)方面更豐富。我們將看到公司投資于第三方數(shù)據(jù)源和智能數(shù)據(jù)(動態(tài)信號和定期刷新的靈活分類)以優(yōu)化輸出。隨著企業(yè)與機器學習偏差作斗爭，信任、透明度和可解釋的人工智能將成為更大的問題?？蛻魧庾R到機器學習需要人類的監(jiān)督和監(jiān)督控制等功能，再加上數(shù)據(jù)洞察，以幫助早期采用者調(diào)整機器學習輸出，并從人工智能投資中產(chǎn)生更多價值”

—— Oracle自適應智能應用副總裁Melissa Boxer。

“Marketers have long talked about taking the ideal next best action when it comes to marketing programs based on where people are in the buying cycle. However, this has been impossible to achieve without massive amounts of data being synthesized by AI in real time. The emergence of AI to take over manual tasks involving huge data sets means that automated next best action triggered by specific activity in the buying cycle will become a reality in 2019”—Peter Isaacson, CMO,?Demandbase

“長期以來，營銷人員一直在談論，當涉及到基于人們在購買周期中所處位置的營銷計劃時，應該采取僅次于最佳的理想行動。然而，如果沒有人工智能實時合成的海量數(shù)據(jù)，這是不可能實現(xiàn)的。人工智能的出現(xiàn)接管了涉及龐大數(shù)據(jù)集的手工任務，這意味著在2019年，由購買周期中的特定活動觸發(fā)的自動次優(yōu)行動將成為現(xiàn)實?！?/p>

——Demandbase首席營銷官Peter Isaacson。

“In 2019, incorporating AI will be an essential part of the marketing strategy. Trained models around predictive analytics, sentiment analysis, programmatic advertising, to name a few, will revolutionize how marketers automate more aspects of the marketing pipeline and develop highly targeted Account Based Marketing (ABM) strategies. This will require investment in new technologies but will also lower custom acquisition costs by making marketing dollars more effective”—Daniel Raskin, CMO,?Kinetica

“2019年，整合人工智能將成為營銷戰(zhàn)略的重要組成部分。圍繞預測分析、情緒分析、程序化廣告等方面，訓練有素的模型，將徹底變革營銷人員在營銷渠道上多方面的自動化，并開發(fā)出具有高度針對性，基于客戶的營銷(ABM)策略。這需要對新技術進行投資，但也將通過提高營銷資金的有效性來降低定制采購的成本?！?/p>

——Kinetica首席營銷官Daniel Raskin。

“Artificial intelligence and machine learning will become a requirement for new solutions for simplified operations:?The IT skills gap will require progressive enterprises to implement new, innovative solutions that automate complex operations. Machine learning and artificial intelligence will become key requirements for new IT solutions to help businesses close the skills gap through smarter operations and modern IT solutions. Enterprise software firms will force their strategic vendors to integrate AI and ML into their existing offerings to provide a more efficient operating model and a higher level of success for meeting their desired outcomes”—Don Foster, Senior Director,?Commvault

“人工智能和機器學習將成為簡化操作新解決方案的需求:IT技能的差距會導致先進企業(yè)實現(xiàn)自動化復雜操作的創(chuàng)新解決方案。機器學習和人工智能將成為新的IT解決方案的關鍵需求，以幫助企業(yè)通過更智能的操作和現(xiàn)代IT解決方案縮小技術差距。企業(yè)軟件公司將迫使他們的戰(zhàn)略供應商將人工智能和機器學習集成到他們現(xiàn)有的產(chǎn)品中，以提供更有效的操作模型和更高層次的成功來滿足他們期望的結果?！?/p>

——Commvault資深總監(jiān)Don Foster。

?“Nearly every IT department will adopt AI to automate enterprise monitoring, reduce manual work of IT staff, and enable a vision of applications that can repair themselves”—Dave Anderson, Digital Performance Expert,?Dynatrace

“幾乎每個IT部門都將采用人工智能來實現(xiàn)自動化企業(yè)監(jiān)控，減少IT人員的手上工作，并實現(xiàn)使應用程序能夠自我修復的愿景?！?/p>

——?Dynatrace數(shù)字顯示專家Dave Anderson。

“The robotics industry will see many startups trying to find a niche and trying to capture as much market share possible. However, in order to succeed, robotics startups must consider regulatory regulations from the start of designs so that they meet applicable safety regulations or else they will fail when they go to market”. Ryan Braman, Test Engineering Manager,?TUV Rheinland

“我們在機器人行業(yè)將會看到許多初創(chuàng)公司試圖找到一個利基市場，并盡可能多地搶占市場份額。然而，為了取得成功，機器人初創(chuàng)企業(yè)必須從設計的一開始就考慮監(jiān)管規(guī)定，以滿足適用的安全法規(guī)，否則在進入市場時就會失敗?！?和璟祎)

——TUV Rheinland測試工程經(jīng)理Ryan Braman。

注：《全球知名企業(yè)高管預測2019人工智能趨勢》來源于Forbes(點擊查看原文)。數(shù)據(jù)觀編譯/和璟祎，轉載請注明譯者和來源。

責任編輯：李蘭松